Data Processing & Analytics

A Cloud-Based Analytics Platform for Regulatory Compliance and Business Insights


Previously I wrote an article about what I called the “Intelligent Data Plane” where I described how analytic functions and data processing could exist together to allow businesses to make use of their data securely and ubiquitously across departments of an organization. The article described how cloud data platforms have emerged to help enterprises gain insights into the massive amounts of data available to them. I described two new companies, one adding value in the document analysis workflow area (making manual processes more efficient) and one in the manufacturing area (making manufacturing data available to management through analytics and reports) to show the promise of analytic processes in various markets. 

In this article I focus on the special requirements within the financial services industry for trade and communications oversight and regulatory reporting. I also highlight how a new company (SteelEye Limited) has built a unique cloud-based platform to serve the needs of this industry and also provide business insight into trade and communications data generally.

Background and Motivation

My experience running the engineering organization that handled communications surveillance and other analytics-based products within Bloomberg LP for five years taught me how critical the analysis of communications and trade data is for financial-services firms. Financial firms (some of the world’s largest banks, hedge funds and private equity entities), rely on 24 hour/day access to the data they generate in their trading and general business operations, the efficient search of these data, and the analysis of their data in relation to financial regulations of all types.

My Intelligent Data Plane article illustrated how cloud-based platforms such as Snowflake Inc. allow companies to harness all of their data into a single view where analytics can be performed. These platforms are great resources for corporations as they allow one “place” where many relevant data sets can be viewed by different departments of the company and then analyzed with Business Intelligence Tools, Data Analytics packages, etc. 

Such platforms are fine for general business analysis of data but lack the functionality required for the analysis of financial transactions and the related data in company communications that accompany them. When it comes to the regulatory requirements of financial services firms, specialized platforms are needed.

Financial Service Firms – Special Requirements

The financial services sector is a demanding one. The data that is exchanged to represent transactions and communicate between firms has to be retained (record-keeping) and the data has to be processed and stored in a certain way. Preparing and indexing data for financial services regulatory applications requires special knowledge of the regulations and how regulators, in-house counsel interfacing with regulators and compliance officers need to search and access data. These requirements are more rigorous than those used for general enterprise data (for plain search purposes).

Search criteria around transactional data is important as it is common that the legal and regulatory teams from financial institutions need verification of what is inside their trades, electronic communications and other data, and how these data relate to one another. Data in various formats must be “normalized” so that key data attributes of trades, email and instant message, and even chat room data can be identified with search queries. The time-based nature and custody of communications must be preserved when data is processed for indexing.

These large data sets representing transactions (and communications related to transactions) are often housed within disparate systems that each have a separate search and retrieval interface. It is generally a nightmare to try and coalesce a single view of all relevant data pertinent to a regulatory matter across these enterprise systems. 

For this monumental task, a special kind of platform is required; one that is tuned for regulatory reporting but also flexible enough to support general views into massive data sets. Such a platform was designed, built and brought to market by a group of seasoned financial services executives. They formed a company, SteelEye, and built a groundbreaking platform. They delivered this platform in a very compressed timeframe by having the correct expertise within the company and starting with a fresh perspective. The SteelEye cloud-based service, leveraging the near infinite processing and storage capability of the cloud, transcends the power and capability of other solutions by orders of magnitude.

Prior to the SteelEye platform, compliance teams would be required to log in to disparate systems, pull results and correlate them by manual means. This is of course a costly alternative to receiving a single view of results from the highly scalable SteelEye platform. SteelEye provides one view into a firm’s data negating the manual processes required by multiple enterprise databases or search indices associated with single-use “silo-based repositories”.

SteelEye – A Unifying Cloud-Based Platform for Analytics and Regulatory Compliance

SteelEye is a compliance technology and data analytics firm that simplifies trade and communications oversight and regulatory reporting through a unique, fully integrated, and cloud-based platform. By connecting large volumes of data from multiple sources, SteelEye enables firms to meet regulatory obligations quickly, efficiently, and accurately. With SteelEye, firms gain full visibility and control of their trading and compliance operations, with cutting-edge analytics that provide timely insights on risks and opportunities. 

Founded in 2017, the company is led by Matt Smith (former Chief Information Officer at Noble Group and Senior Product Manager at Bloomberg) and serves clients in the buy and sell-side space across the UK, Europe, and the US. The company has several key case studies and client success stories documented here. A recent and significant funding event has been completed to accelerate growth into the North American Market in 2021.

This quote from the CEO (Matt Smith) encapsulates the vision and mission of the company:

“What makes the SteelEye platform an exciting and cutting-edge solution for financial firms is its ability to simplify key compliance processes by connecting and making sense of large quantities of data that naturally doesn’t fit together. This enables firms to meet multiple regulatory needs using just one platform and truly use their data to uncover new insights.” 

Significance of Team

The team at SteelEye have extensive experience in the regulatory and compliance market. As a consequence of this, they understand all regulatory requirements for building world-class compliance products. Beyond that, they fully understand the sheer scale of processing and analytics required within an industry that has to retain and manage 100’s of millions to billions of transactions per day. This uniquely positions the team to understand how to build not only compliance applications but also enterprise scale applications for the general business analysis market. The blend of team members with both compliance/regulatory and technology experience is unique in my experience and sets SteelEye apart from its competitors. The team has built a suite of applications on top of a massively scalable data platform that can provide insight on both risks and opportunities.

Significance of Product

The team at SteelEye recognized that their market requires a true “Intelligent Data Platform”. The team have extensive experience in the various regulations to which their clients must adhere and have decades of combined experience that shines through in the platform’s applications and interfaces, which help with both financial compliance and business operations management. The applications built on top of the SteelEye data platform provide the support that compliance officers require and that are not available in general data analysis platforms. SteelEye applications leverage the same underlying data set representations for a variety of purposes.

SteelEye have produced reporting applications and an API that allow their clients to get the answers they need for regulators in record-breaking amounts of time.

Matt and I both worked together at Bloomberg LP, so I was not surprised when he showed me the platform and it was designed brilliantly. The user interface leads the seasoned professional or the novice compliance analyst through the process of finding relevant data for reporting or analysis purposes. The value of having all of a firm’s trade and communications data in one cloud platform where it can be analyzed against the requirements of a number of different regulations was evident in a quick demonstration.

Platform Capabilities

  • Data Platform
    • Record keeping
    • Trade Reconstruction
    • eDiscovery
  • Trade Oversight
    • Trade Surveillance
    • Holistic Surveillance
    • Best Execution and TCA
  • Communications Oversight
    • Communications Surveillance
  • Regulatory Reporting
    • MIFID II
    • EMIR
  • “Insights” – next generation reporting tools
    • API
    • SDK

Regulatory Support

The SteelEye platform can support a wide range of regulations and rules, including those imposed by Dodd-Frank, EMIR, MAR, MiFID II, SEC, FINRA and more.   

Key Attributes of Platform

  • Multiple compliance obligations satisfied through applications on one platform
    • Various applications for different regulations are supported on one view of a client’s data
      • Multiple data sources collected in the platform are viewed as a whole data set
      • All applications work on the data set in its entirety
    • Each application is tuned by subject matter regulatory experts 
  • Solutions are built to evolve
    • As regulations change, the platform is powerful and can adapt to these shifts
    • Applications are not constrained by capabilities of the platform
  • Automation enabled through Machine Learning (ML) and Advanced Analytics
    • to simplify processes, improve investigations and make better predictions

Flexible Platform Adds Value to and Beyond Compliance

Despite having vertical applications that support specific requirements of specific regulations, the platform also supports “ad hoc” analysis of data. Therefore, as regulators or business analysts need answers to questions that are not in a pure regulatory context, the platform can supply the answers.

The engineering team at SteelEye selected the Elastic Search technology stack (sometimes referred to as and “ELK” stack) as a base for their platform’s analytic capabilities. This was an excellent choice as Elastic Search has petabyte scale, is very extensible and presents API’s to allow for easy integration.

Having led the team at Bloomberg LP that rebuilt its entire data processing pipeline for compliance/surveillance, I respect a team that selects the right technology for not just solving the problems of the present, but also looking to the future and anticipating the need to extend the platform as the needs of customers evolve

The Elastic Search stack provides:

  • Petabyte scale
  • Data sharing – REST APIs are built into Elastic Search
  • Flexibility – adding documents to the stack enhances the data model naturally as documents (data representations) are stored as vector representations that are the basis for ML models

As mentioned above, the SteelEye platform has tuned applications for specific regulatory adherence but is also extensible enough to handle more “ad hoc” uses that transcend compliance alone. Also (as pointed out above) the platform can evolve to meet new regulatory requirements. SteelEye engineers leverage ML and advanced analytics within the platform for a variety of purposes.

The technology team anticipated the future needs of the platform by utilizing both ML and Classification capabilities into the product. This article explains how the Elastic Search stack can be used for classification of data. Data classification is also used (particularly within Trade and Communications Surveillance) to reduce the occurrence of false positive results and make surveillance alerts more relevant.  Examples of how SteelEye has implemented practical items that make surveillance and search results more relevant include:

  • Email classification to eliminate non-relevant emails (such as those containing commercial advertising and solicitation emails not related to business operations). Customers can exclude such email messages from being monitored in market abuse watches, and the system “learns” what these “look like” over time – getting better at identifying and excluding them.
  • Customers can select the areas of certain communications they wish to monitor, for example the email subject and body, which contain the most relevant content, and exclude sections like the email signature or “the rest of the thread”. 
  • The surveillance system of the product includes both rules-based (lexicon based) criteria (as is customary and required in many financial services contexts). The lexicon constructed by SteelEye is reported to be many times larger than competitive rule sets. This multi-layered approach (of employing ML plus required rule sets) is very intelligent and again is designed to reduce the number of false positive results reported by the system.

Examples of how the technology team have implemented practical items that make surveillance and search results more relevant include:

  • Email classification to eliminate non-relevant emails (such as those containing commercial advertising and solicitation emails not related to business operations). Customers can exclude such email messages from being monitored in market abuse watches, and the system “learns” what these “look like” over time – getting better at identifying and excluding them.
  • Customers can select the areas of certain communications they wish to monitor, for example the email subject and body, which contain the most relevant content, and exclude sections like the email signature or “the rest of the thread”. 
  • The surveillance system of the product includes both rules-based (lexicon based) criteria (as is customary and required in many financial services contexts). The lexicon constructed by SteelEye is reported to be many times larger than competitive rule sets. This multi-layered approach (of employing ML plus required rule sets) is very intelligent and again is designed to reduce the number of false positive results reported by the system.


SteelEye has built an outstanding and award-winning data analysis platform that supports compliance and regulatory reporting superbly. The product is vastly scalable and extensible to support evolving regulations. The seasoned team at SteelEye envisioned the product from a true cloud-based perspective. In so doing, the platform is also extremely useful for general business analytic purposes that can aid commercial efforts of all kinds within an enterprise.

Economy and Business Politics and Public Policy

Two Market-Oriented Approaches to Attack Income Inequality

The issue of income inequality divides the nation and is a source of social unrest. The government, led by the Democratic Party and President Biden propose a traditional approach of taxing “the wealthy” and providing broad social welfare programs to address income inequality. This is a classic redistribution of wealth where taxes are raised on one class of people and given to another to achieve “fairness” of some sort. Providing these kinds of social programs have been tried since the 1960’s and have not proven to end poverty or to deliver long-term prosperity to the country’s citizens. If they did, we would not still be talking about raising taxes to fund social programs that raise living standards.

Traditional social programs may help the political party proposing them to gain voter support, but they don’t permanently address issues like income inequality. Eventually they become a burden so great that taxpayers vote in a new set of government leaders who undo or “reform” the social program legislation that transfers wealth from one class to another. The less fortunate never seem to have their circumstances changed fundamentally as a result.

The best ways to attack income inequality include educating the people, or giving them an incentive to better educate themselves, and through helping citizens have a “stake” in the capitalist economy. Both of these goals can be achieved with long term approaches where the power of compounding returns in the public stock markets are used to improve the lives of U.S. citizens. This is a radical new approach that is intended to make people more reliant on the success of capitalism than the largess of government.

Root of The Problem

Most arguments I see around income inequality end up with the statement: “the top earners have access to financial assets, and the lower earners struggle paycheck to paycheck because they do not (have such access to financial assets)”.

These debates almost universally start with arguments about how “the wealthy” don’t pay enough in taxes and end in agreement (almost as an after thought) that the middle and upper classes have access to stock portfolios through ownership of 401K or IRA accounts, or outright stock ownership. leading to income disparity between the two groups. There is a lot of disagreement over what “the fair share” is that should be paid by the wealthy. There seems to be no disagreement that the issue is that lower income individuals don’t have financial assets, and that financial assets are the best way to grow wealth over the long term.

New Thinking” for a Solution

So why don’t we give everyone interests in the capitalist economy by making sure that they have tax-advantaged access to stocks? Stocks have historically grown between 8% and 10% per year since the year 1900. This source calculates that the return on stocks has been 9.85% over this period of time. The same source shows that the inflation-adjusted return on equity assets over that period of time is 6.76%. Given that this includes a couple of highly inflationary periods (after World War II and again during the commodity shock driven inflation of the 1970’s), and that central banks may have a much better handle on monetary policy to fight runaway inflation than they once did, one could assume that inflation-adjusted returns in the stock market could (over long periods of time) average around 8% annually.

This assumption around 8% growth in the stock market is a bit on the optimistic side, and Stan Druckenmiller (famous investor) on CNBC recently, cast some serious doubt on the central bank’s ability to control inflation with all the recent stimulus spending proposed by the Biden administration, but for sake of argument, let’s go with the 8% assumption.

At 8% compounded annual returns, the government could fund two programs that take advantage of the growth that is possible with equities in the stock market. One that would help citizens gain an education and one that gives them a way to own stocks and financial assets that at least fund their retirement and give them some security in their retirement and older years.

These would be an “Educational Assistance Fund” and a “Birthright Fund” (the latter fund was proposed by Bill Ackman, famous hedge fund manager and private investor). These proposals would both start off with the Government providing an investment at the outset, but the money provided would be invested in zero-cost index funds where it would grow tax-free and benefit the owner (individual citizens) at some point in the future. The “Big Idea” behind both of these programs would be that the stock market and its compound growth over time would provide the funding for the benefits, not ongoing taxpayer dollars.

The Proposals

Educational Assistance Fund

The concept of an Educational Assistance Fund is simple. Have the government “seed fund” an initial investment of $100 billion and invest it in zero-cost index funds that would yield about 8% per year. Pay benefits out of this fund to graduates of training programs (technical trades, IT training, or traditional college degree programs) as the fund produces returns. Students would put “skin in the game” by borrowing the money to train themselves, but would qualify for payments from the Educational Assistance Fund to enable them to pay the tuition back to qualified institutions.

The program would have “stage one” where the initial government investment provides the available benefits for the first ten years of the fund. The second stage of the program (“stage two”) would be funded in the first ten years by a corporate tax on companies making $100 million or more in profits. This money would compound in value for ten years while the stage one investment return dollars are paid out as benefits. Stage one would provide help to graduates immediately, stage two would provide enormous benefits to graduates, contributing life-changing aid to those who are employed and who sought an education.

The stage one of the programs pays out benefits of around $8 billion per year (8% of the $100 billion invested by the government). During the first ten years (as stated above) a 1/2 of one percent corporate income tax on corporations that have more than $100 million in profits would be collected and invested in the zero-cost index funds that also contain the first $100 billion. After ten years that second stage fund would begin paying benefits too, but at that point the fund would have grown substantially larger and the benefits available to students would be much greater.

To qualify for access to the funds, and to give graduates an incentive to major in fields where employment is most likely, the educational assistance fund would only pay out to employed graduates. If a college graduate were employed, they could qualify for amounts as large as $20,000 (or more, depending on the performance of the index funds, and the number of participants requesting benefits) in benefits per person per year sometime after the ten year “second stage” phase of the proposal.

This would be a huge boost to graduates who would otherwise experience large debt burdens from earning their degrees. It would also encourage those who earned a degree in one discipline where employment is unlikely to get further education and obtain a degree where employment is more likely.

A student in any type of program (Four year baccalaureate program, graduate school, trade school, IT training, etc.) would qualify. They could borrow the money to attend training classes and gain access to the educational fund distributions to help them pay it back after they graduate and become employed. The payments would be made to the educational institution by the employer and the employer would be reimbursed by the educational assistance fund. Students could remain eligible for payments from the fund until their debts are paid off through distributions from the fund.

Seed funded Stage (first Ten Years)

The educational assistance fund would be “seed-funded” by the government initially. The first $100 billion would be a one time investment that would be invested in zero-cost index funds. With an 8% annual return this would provide $8 billion per year that could be spent to offset student debt for graduating students of specific trade or technical programs, or graduating seniors and graduate students from traditional college programs..

The government could earmark $100 billion as an “endowment” to fund the program and an additional $8 billion if benefits were to be paid out immediately (without waiting for the investments to compound), making the initial investment $108 billion in the Educational Assistance Fund.

There are around 2 million undergraduate (bachelor degrees) awarded per year, so this $8 billion could provide $2,000 – $4000 per person/year in immediate aid to offset borrowing costs. This is an estimate as vocational training and IT training programs are also included. As an estimate, somewhere in this range is a feasible assumption and would be a great benefit to those seeking job training.

In “stage two” of the fund (explained next) the fund will have grown in magnitude and the benefits paid out of the fund will be much larger than the benefits paid out of the fund in stage one. In stage two of the fund, the payments to graduates would increase approximately ten times from what they were in stage one.

Self-sustaining Stage (After Ten Years of Compounded Returns)

The second part of the educational assistance fund would be paid in to the fund by corporations with profits greater than $100 million per year. During the first ten years of the fund the corporate contributions will be reinvested in the stock market and allowed to grow. No benefits would be paid out from the collection of the funds from the corporations until after year ten of the fund.

The Fortune 500 last year posted profits of $14.2 trillion. A half-percent tax on the profits of Fortune 500 corporations alone would provide $71 billion a year. $71 billion invested in the fund every year for ten years (with 8% annual return compounding) would yield $1.1 trillion (at the end of ten years). The actual amount in the fund after ten years would be larger than the $71 billion because there are more than 500 companies making profits greater than $100 million. I just used the magnitude of the profits of the Fortune 500 to show the feasibility of the concept.

So the fund would be at least $1.1 trillion after ten years and probably much larger than $1.1 trillion with all companies making more than $100 million in profits (not just the Fortune 500) paying their contributions into the fund. This assumes that the assumption of 8% compounded annual returns holds true over the first ten years of the fund. Given the uncertainty of the returns but that there will be more than $71 billion per year paid into the fund (due to more than the Fortune 500 contributing) it is clear that this proposal is feasible.

If the fund were $1.1 trillion after ten years the amount invested in the fund would provide $88 billion in investment income per year. This assumes that no further corporate contributions were made into the fund after the ten year “waiting period”. With further contributions (which there would be) the fund would grow larger, albeit at a smaller rate of increase since the returns on the index funds would be getting paid to graduates. Also with a $1.1 trillion base and the original $100 billion “seed” amount from stage one of the proposal, the fund would be at least $1.2 trillion.

With $1.2 trillion available and an 8% return assumption, this could yield approximately $96 billion in benefits per year. This spread over 2 million – 4 million participating students graduating from some kind of program each year, would yield between $24,000 and $48,000 per student (depending on returns and participation rates).

If the average debt burden on a graduate were $40,000 per year and the returns of the fund were spread over existing and new graduates each year, participants in the program could retire their debt in a few years. After year ten (phase two of the plan) overall debts of newly graduating recipients could be paid off within one or five years (depending on the number of graduates and the performance of the invested platform assets). This is without the student making payments from their own funds.

This would be possible because during the second stage of the plan, those getting access to fund distributions could receive between $10,000/person/year and $40,000/person/year. This would relieve their debt burden more quickly than was possible in the first ten years of the plan. Because congress would be prohibited from taking any of the assets paid into the fund for any other purpose, and payments into the plan by corporations would continue every year, the fund would continue to grow and benefits could grow over time.

The important part of this plan is that it would not require ongoing taxation to fund the “educational trust fund” and it would provide incentives for graduates to remain employed to qualify for the automatic payoffs of their loans in a few years. Income redistribution (through taxation) would not be necessary and the trust fund would pay employers to relay the payments to qualifying graduates. The government would only be minimally involved.

The trust fund would be managed by index fund managers, not the government. Benefits would be tax-free to recipients and free them from the payments on their debt sooner. This would allow employed graduates to enjoy more disposable income and to build their lives more comfortably sooner than they could without such a plan.

The BirthRight Fund (Bill Ackman’s Idea)

This idea is brilliant and simple: give each newborn baby a stipend and invest it for them in a zero-cost index fund. The investment will grow tax-free throughout a person’s lifetime and at age 65 they are eligible to withdraw funds from it.

The idea of giving every new born baby a “stake” in the economy belongs to Bill Ackman, the famous hedge fund manager and CEO of Pershing Square Capital Management. In an article he authored for The NY Times Deal Book series, Mr. Ackman explains how $6,750 invested at the birth of a child will grow to over $1,000,000 by age 65 and $2 million after 74 years, assuming an 8% annual return. His “Birthright fund” (as he calls it in his article) would be invested in zero-cost equity index funds. Individuals would not be allowed to withdraw funds until at least age 65.

Mr. Ackman’s idea is brilliant as it harnesses the magical principal of compounding annual returns (just like the educational assistance fund). When returns (dividends and share growth) are allowed to be reinvested and to compound within the Birthright fund, the holder has an ever-growing asset tied to the success of the economy and the capitalist system. Growing tax-free within the account, the holder of the Birthright fund holds shares in a broad swath of the capitalist economy. Given historical growth rates of the stock market as a whole of 8% to 9% over the last 70 years, it is a reasonable assumption that the Birthright fund would be a great place to grow wealth for all Americans.

In the following chart, generated from this source, the concept is illustrated. A baby born into the BirthRight Fund will see the initial $6750 investment grow to be worth over $1 million in 65 years.

The initial stipend of $6750 would be invested at birth and held in the BirthRight Fund (as Mr. Ackman named it) until a given person with rights to the fund turned age 65. The funds would compound tax-free and there would be no tax penalty at the end of the investment period (a minimum of 65 years, the recipient could let it grow in the fund longer). At age 65, the money could be drawn to pay for living and retirement expenses.

Things I would add include that the fund (and the educational assistance fund) could never be diverted by congress to any government purpose, the fund amounts can never be borrowed against (like with an IRA) and the funds can be inherited by one’s family as part of their estate. In all respects the funds are theirs, the future recipient is the sole owner of the funds and they are not subject to inheritance or “wealth taxes” in any way.

This plan depends on the historic rates of return holding up over the 65 years of a person’s life, so that is a risk worthy of noting. The risk of inflation eroding the returns is larger with this plan than the educational assistance fund as the BirthRight Fund will pay for future expenses denominated in dollars.

The educational assistance fund will pay off dollar amounts set in the past so inflation is not as much a worry. The educational assistance funds will be paid toward a fixed amount of dollars that was the cost of a past education, the BirthRight fund will be paying for expenses in the future that might fluctuate higher than anticipated in dollar terms.

Over such a long period of time (65 years or more), the funds would grow significantly and a recipient of the BirthRight fund would still be imminently better off than they would without it. Within such a timeframe, the growth rates of equity investments are likely to remain near historical averages. The power of compounded annual returns is so strong that betting on the phenomenon is a worthwhile gamble. Inflation erosion aside, the BirthRight Fund is a brilliant idea and Mr. Ackman should be lauded for proposing it.

Benefits of this approach

This is very good for women. Women who decide to take care of children or another family member during their lifetime continue to accrue the benefits of the capitalist system. They are not tied to their husband’s earning power should they feel that they have to prioritize child or elder care over career earning power. Many women who get divorced are dealt a major long-term setback economically. This would cushion that blow significantly.

This allows the very disadvantaged and those from economically challenged backgrounds to achieve and build household wealth. African-American families, Latino families as well as poor white families would all benefit equally from the BirthRight Fund. Non-white families have much lower household net worth than white families in the United States. This would level the playing field in that respect significantly.

This could solve the Social Security problem entirely. With means testing of benefits, a family with the BirthRight benefits would not need Social Security. The Social Security system could eventually be phased out as it would no longer be needed. The current Social Security tax paid to the government could be eliminated or diverted to a health-care fund that could offset medical expenses in one’s later years.

The truly disadvantaged would be able to take risks they otherwise not have been able to take. Starting a business and knowing that one’s retirement is still available to them could allow a “burst” of entrepreneurial activity to occur. This would be a huge benefit of such a plan.

The biggest benefit would be that this ties (as Mr. Ackman points out) each citizen to the success of the American capitalist system. Instead of relying on the government to provide assistance to families in their retirement years, each person can count on the success of the places they work and the country as a whole to provide for them.

Overall Benefit

The tendency to think of corporations and “the wealthy” as the enemy would be reduced with both of these plans. The success of the economy would be paying individuals who educate themselves and take risks to build their careers. It also binds individuals to the “American Dream” of hard work and self fulfillment. Both of these plans would be worthy of implementation and would go a long way toward providing equal opportunity for all. Social cohesion, not social division would result as more Americans feel tied to the economy.

Data Processing & Analytics Economy and Business

Datanomix – “Production Intelligence”

In one of my recent posts, I defined what I called “the Intelligent Data Plane” and the SW components that exist within a data intelligent processing “stack” to make data produced within an enterprise “useful”. A company that has built a platform for collecting data from manufacturing facilities and presenting it to users in an intelligent way is Datanomix, Inc., a company located in Nashua, NH.

This platform is interesting because it fits into my thesis as a “Stage Two” platform where data is not only collected and presented to users intelligently, but where data is provided in a fashion that can aid other applications of interest to its users. In addition to performing its own analytic functions, this platform acquires, secures and generates data that can enable other analytic processes that may be of interest to manufacturers.

Please recall the diagram (below) for an overview of the intelligent data plane architecture.

Intelligent Data Plane (Manufacturing Data Analysis)

In this reference architecture for intelligent data analysis, Datanomix would be the Intelligent Data Plane component interfacing between the manufacturing factory floor and cloud services where vital production data is sent for analysis.

Datanomix Platform
Datanomix’ platform is an intelligent analytics platform that can access and collect production data from manufacturing machines (Computer Numerically Controlled “CNC” machines, “smart” lathes, 3 -D printers, etc.) over standard API interfaces. Datanomix deploys easily configured local SW components that interact with the factory floor machines to collect data from production equipment. The Datanomix SW intelligently acquires and filters the data into what is relevant to a particular process within the manufacturing workflow. Subsequently the data is transformed and transported to cloud SW components where it is saved, indexed for retrieval and formatted into reports that are meaningful.

The Datanomix SW transforms the data it retrieves into standard JSON objects that can be displayed in reports or indexed in the cloud. The objects are transported over an encrypted channel to the cloud data service (Azure for example) where it is indexed and analyzed. All data at rest is encrypted in the cloud for security purposes.

Production Monitoring Dashboard

The Datanomix SW also interfaces to standard ERP systems and can incorporate the company “GEMBA” board ( a Japanese term meaning “real place”) representing the status of the manufacturing process. It has standard connectors to display the GEMBA board on a Smart TV television monitor in a factory setting.


The Datanomix SW also has connectors for monitoring machine health and operating parameters. The SW can apply anomaly detection algorithms to establish automatic thresholds for normal operation and send alerts when a machine is operating outside of normal parameters.

By sending the local data to a hosted cloud service where it can be stored, indexed and analyzed, Datanomix can add features to the platform easily. Through their hosted cloud service they provide a wholistic view of an entire manufacturing process. Manufacturing managers get the data they need rapidly and conveniently to help them understand what parts of their operations may need refinement.

As data is stored securely and in a standard format understood by other applications, it can be shared easily. Datanomix’ customers may need to share process control data with their customers for quality assurance purposes. Due to these capabilities, Datanomix can provide true “Stage Two” capabilities by enabling data analysis, security and sharing without requiring a large commitment from their customer’s IT staff.

The platform is not limited to use by industrial manufacturing entities. The platform has relevant use cases with pharmaceutical manufacturers who collect large amounts of process control data and need to store and retrieve it conveniently. Use cases in the semi-conductor manufacturing space also exist.

Datanomix “checks the boxes” for intelligent data acquisition, filtering, transformation and secure transport of data to cloud locations (as shown in the Intelligent Data Plane architecture). Data at rest is protected within the cloud service with strong encryption as well. They have excellent User Interface capabilities to highlight the reports they can generate for customers. Analytic capabilities exist for monitoring machine performance. The platform is easily extensible in the future as more intelligent analysis features are requested by customers. In these respects, the Datanomix platform is a strong performer within the “Intelligent Data Plane” architecture.

Data Processing & Analytics Economy and Business

“Intelligent Data Plane”

Executive Overview

The data analytics platform revolution is in Stage One of its development. Investors, entrepreneurs and enlightened customers have enabled the development of great platforms that perform data analysis, yield insights on data sets at great scale and deliver enormous value. The term “Artificial Intelligence” (AI) is used to describe the functions that many of these platforms deliver. The right way to think about this may be that these platforms will first deliver enhanced, human-supervised Machine Learning (ML) rather than true native intelligence, and that argument is made here, but this article is more about describing an “Intelligent Data Plane” architecture.

This architecture will enable these platforms to not only ingest data and perform a “best in breed” function based on AI/ML, but to allow customers to hook new platforms together with existing ones in a “data pipeline” fashion where data can move between platforms and add value across all of them. Stage Two of this revolution is building service platforms in a way that allows these “best of breed” technologies to securely share and analyze data among one another and provide greater advantage than they could by working alone.

Background and Perspective

In April of 2012, famous investor Ann Winblad pronounced that “Data is the new oil” on a segment of CNBC. This was a long time ago, but it has proven to be true. In a more recent article (from March 23, 2020), Peter Wagner, founder of, a Silicon Valley venture firm, outlined the importance of data and analytics and what he calls Artificial Intelligence to the modern enterprise.

Peter’s article is an intelligent overview of some companies that he identifies as playing a major role in providing the data intelligence that will give the modern enterprise competitive advantage. He makes some insightful comments about how enterprises will use Artificial Intelligence and Machine Learning to build this advantage.

In the article Peter illustrates how workers in the modern enterprise connect to and utilize data in many different applications or platforms. The nature of the distributed enterprise and the distributed nature of enterprise data is illustrated very well. The number of applications shown in the article also illustrates that data can be used in a variety of ways and across applications. The distributed nature of applications and how the data they use resides in multiple locations and multiple clouds is obvious to the reader. Peter did an excellent job showcasing the landscape of Artificial Intelligence technology, platforms and how they will be used commonly in the future.

Pondering this new “fact of life” it occurred to me that in speaking about analytics, artificial intelligence and machine learning, some of the aspects of how to make data useful get lost in the discussion. For example, making data available to a number of applications and securing it in transit as the data changes locations is now important. Ensuring that the data is in a format that can be consumed by multiple applications while maintaining security of the contents and ensuring only authorized access to the data must also be a consideration. In the past (and in many non-cloud environments) this was not the case. Now such considerations are common business requirements.

Stage OneA Good Start

In actual practice though, I think that the evolution of the data analytics “industry” is in stage one of its development. There are great platforms and services that have been built, but the second stage of industry evolution will be when we can use platforms seamlessly across data sets, transforming the data as necessary to allow the “best of breed” applications to work together. In actual practice, most enterprise applications rely on their own databases and access paradigm. Data is not universally useful and fungible across different platforms.

When a manager in Human Resource Management can access data selected (and made anonymous) from the communications sent within the company (without a manual step being necessary to make the data anonymous) to run analysis that will indicate workplace satisfaction trends, we will have begun to make use of data. We of course have to ensure the platform will maintain the security of the data and anonymity of the senders but this would be a great benefit to help management understand what to improve about the workplace environment.

Like oil, data has to be extracted from its source, refined, moved to where it is useful, and used in a productive way. It has to be protected while it is stored and there are regulations for how it can be stored as well. So in a lot of ways, data is like oil in that it is powerful and precious and requires special handling to be useful.

Given these facts, it occurred to me that there is an emerging model for how to think about data and data analysis technology. As the networking world defined the seven layer “OSI model” of how network technology is divided into layers of functionality, there is a similar (but different) model emerging for evaluating data handling and analysis software. I have named this model the “Intelligent Data Plane” and compare it in some ways to the OSI Seven Layer stack defined for networking.

The Intelligent Data Plane

A new “Intelligent Data Plane” is necessary to connect and identify all the data moving through an enterprise and help prioritize and manage it. Data from users, manufacturing locations, supply chain partners and cloud services must be accounted for in the Intelligent Data Plane. This intelligent layer of SW is illustrated in the following diagram:

At a high level of abstraction it is important to visualize that the modern enterprise is distributed and data comes from many different applications. Data is sent from different users and is often stored in diverse systems at multiple locations. Given what is in the data itself, who produced it and where it originated, the data will have certain value and will require certain types of handling. Data should reside and be processed in either the “cloud”, “hybrid edge”, “edge computing”, or pure on-premise locations, depending on the security and processing requirements of data items.

A set of components implementing the “Intelligent Data Plane” of a processing stack provide the necessary identification, indexing, labeling, encryption and authorization functions to protect and use data in a cross-platform, multi-user environment.

OSI Stack for Networking

The following diagram illustrates how the OSI stack for networking delineates the required functions to supply services from applications running on a TCP/IP “stack”.

Intelligent Data Plane (Functional View)Stack” Components

The Intelligent Data Plane, in a way somewhat analogous to the OSI model for networking, has defined functions. The functions work together as data passes through this “data analysis stack” to provide data that is valuable to users through applications. Such a stack can work together as a pipeline to provide data that can be useful and securely available to given sets of users and applications. The stack enables users to “interact” with the data and analysis algorithms to refine the classification capabilities of the software.

All of these components need to be present to capture, filter and safely store data that can be analyzed and used. As with the OSI layers of a networking stack, each layer of the Data Analysis stack has to perform its function to make the data relevant, meaningful and secure.

Stage Two – Extending Capability

Note that data can move through the components of such a data plane, or processing stack in pipeline fashion, adding value to data items as they are processed by applications. Stage two of the data revolution will be achieved when applications can use components implementing these functions as they need them. This will allow an application to be built quickly, utilizing whatever components of the intelligent stack are required for processing and analyzing then presenting data of interest to users.

The Intelligent Data Plane is software that provides the functions of:
Intelligent data acquisition (source-specific connection and object/attribute filtering of data). Examples would be reading files from a specific Amazon S3 bucket or receiving emails from a certain mailbox location where customer inquiries are stored. This is largely present in Stage One platforms and is mentioned here for completeness.

Filtering rules/initial analysis to collect only relevant data. Examples of this would be collecting files that have been stored within a specific timeframe, or emails that are sent to a specific mailbox address, or retrieving database records with a certain range of identifiers in a certain column of a table. These functions may not be as “automatic” as they need to be in today’s stage one environment.

Data Transformation into objects that support analytics. An example would be parsing a PDF file and determining if it has a table, the table within the PDF has data with certain values, and transforming the data into a JSON object (document) and storing those objects for further analysis by another component within the Data Intelligence stack. Stage two platforms will provide standard mechanisms to normalize and share data among applications/platforms.

Movement of data to third-party modeling and analysis software or modeling software within the intelligent data stack itself. Identifying data with sensitive personally identifiable information (PII) and labeling the data so that these data items can be protected. Movement of data and classification and labels (while maintaining user context of who defined the labels) is largely platform specific in stage one products. Stage two would make these a universal attribute of data analysis systems.

Data Protection (encryption, obfuscation). Examples would be encrypting objects or documents that have sensitive data. Stage two would make the encryption and key management transparent to users, and encryption/data protection a user-centric or community centric attribute of classified data sets.

Data Analysis/Classification – with “human in the loop” capability to aid and improve classification performance over time. Examples would be a user interface and data presentation layer that allow a user to reclassify data that the classifier has mis-labeled. Such data would be reintroduced to the training SW to allow the model to be updated and allow the classifier to work more accurately in subsequent classification runs. Stage two would evolve to include this as base functionality. Stage one products add this today platform by platform at the application level.

Data Storage and Control (storage plus indexing for eventual retrieval). Examples would be functionality to index data items, store data items and labels such that documents can be searched and retrieved by either keyword attributes of the documents, or classification labels associated with given data items. Most Stage one platforms have this today, or provide on-demand indexing based on meta data attributes for deeper content searching. Stage two platforms would enable association of user-defined labels with certain sets of data items and allow them to be searched along with strict data attributes.

Data Presentation and User Interface Layers for Viewing Data and the results of classifications.

The Data Presentation layer must be backed by APIs and technology that can store the labels for multiple classifications and present them in the proper context for a given user. All documents that are classified accurately and given a specific label should be able to be associated and presented to the user who has the authorization to view them.

The Intelligent Data Plane is a reference architecture defining layers of functional components that aid the analysis and management of data in service platforms. The evolution from stage one platforms that provide great value but that may not be built with all of these functions was explained. The different layers of the architecture were presented to allow the reader to understand what functions can be built to extend the platforms that exist today.

The ability of the architecture to supply “pipelined” processing flows that allow multiple platforms to securely share data and provide their own best of breed functionality into a greater benefit for customers is the end goal of such an architecture.

Data Processing & Analytics Economy and Business “Liberating Data, Accelerating Intelligent Business Decisions”

In one of my recent posts, I defined what I called “The Intelligent Data Plane” and the SW components that exist within a data intelligent processing “stack” to make data produced within an enterprise “useful”.

Key components of the Intelligent Data Plane are automation of the processing of data items (email, documents), automation of analytic processes and transformation of data into useful formats for inclusion of data into “downstream” processes. The Intelligent Data Plane architecture also defines an intuitive user interface ability so that users can interact with data to identify data attributes to be used by the classification engine. In addition, the product should contain an ability to refine the performance of its classification and analysis algorithms through human review. is an exciting company doing great things in all of these areas of the Intelligent Data Plane architecture.

Intelligent Data Plane Accelerating Business Decisions, is a company located in New York City. It automates the processing of many types of data, unstructured file data as well as semi-structured email data. The platform eliminates the manual overhead of evaluating emails, documents, forms and the content they contain. It enables the integration of its automated data processing and classification engine with “downstream” processes as well.

Through an intuitive user interface, it allows business users to interact with data and the system to build data models without requiring them to be data scientists. The system then identifies and processes documents and emails, delivering them to “downstream” workflows with minimal human intervention. The system eliminates untold hours of tedious and error prone manual labor and speeds the processing of data included in email and documents.

Deep Compliance and Financial Markets Expertise

The business team has extensive experience in the financial services and compliance market. They have recognized that automating manually intensive business processes will provide unprecedented efficiencies to previously tedious and error-prone operations.

Clear Technical Advantage

The technical team has deep expertise in document processing, machine learning and computer vision. Together, they have built an impressive platform that can evaluate email, documents within emails, and documents at rest to classify and normalize their business content. The system can then deliver the documents that are most relevant to pre-specified business criteria to the next step in a production workflow. The automated classification of relevant documents is extremely accurate and eliminates costly errors that could occur with manual review alone.

Using both machine vision and machine learning,’s extremely capable team has built a system that identifies and classifies data of interest to a business process user, extracts the relevant data into a structured payload (transforming it into a standard format for further processing). Allowing relevant data to be extracted and normalized into a database, the content and the documents that contain the data can remain associated for later retrieval. have named the latest version of their product “Patterns” after the concept of having a user define a pattern in the data that the platform can use to identify and classify documents. Patterns Overview

Alkymi describes their process in this illustration from the Patterns Product Brief ( The interface allows a user to select relevant data from given documents and the system identifies the selected items as attributes to utilize for identifying documents of that “class”. The documents are then analyzed against the patterns and labeled within the system. The combination of machine vision and text analysis is very powerful and differentiating from a technical standpoint.

Defining Patterns/Models

Applying Results to Workflows

Apply Pattern Results to a Workflow

With the system, data can be promoted to the next stage of the business process easily. This fits my thesis of a component within the Intelligent Data Plane architecture allowing data to be transformed and moved to another business process in a workflow. Data moves from an automated location or email box into the system where it is processed according to the pattern defined by the customer. The useful data is extracted and moved along to the next stage of the process.

“Human in The Loop” – Refining Accuracy of Classification

One very important aspect of the system’s team built is the ability for users to refine the results of the automated classifications. By allowing users to mark and refine the data attributes used in a classification the system gets “more intuitive” over time.

Standard Document Automation Scenarios have built solutions that have automated the identification of many types of standard documents in various industry sectors.

  • Portfolio summaries
  • Performance reports
  • Fact sheets
  • Allocation exposure & sector reports
  • ESG related compliance documents

Visit for more industry concentration areas.

Summary is an exciting new company building a second-generation data analysis platform (a generation ahead of other vendors). It incorporates an intuitive interface to allow data to be collected and categorized against the criteria important to business users. This eliminates the tedious and error-prone process of human beings having to open and read thousands of emails to determine which contain relevant documents or data. The SW also meets important criteria that was defined in the Intelligent Data Plane architecture of automating the flow of data into other workflow processes. Additionally, the system allows a human to refine the results of the data classifiers by reviewing results and adjusting the attributes being used for classification. This is a very impressive company and platform.

Economy and Business Politics and Public Policy

The Biden Tax Plan and Unintended (Negative) Consequences

While I watched President Biden’s speech to both houses of congress last night I remained convinced that he is a good and decent man who truly wants to help the American people. I was also convinced that he is a product of Washington politics who has never done anything but be a politician. Therefore he does not understand the negative impact his policies will have on innovation and young start-up and private companies in general.

In addition to pursuing policies that will stifle innovation, he is using a lot of rhetoric to sell rather grandiose plans that he and his advisors may think are popular with some voters, but that in the long run will lead to more political division and strife within our fragile democracy. His administration does not seem to understand that what he proposes will not only harm innovation, but drive a great deal of cost into company operations that will stifle job growth. He clearly wants to help the “middle class” as he calls it, but he seems to have a 1950’s and 1960’s style outlook on how to provide that help.

Large scale government programs that will have to include private company involvement and much higher taxes may have worked in the 1960’s, but in a globally competitive world these ideas are no longer relevant. For example it is unclear how advocating union membership is going to help members of the middle class fit into a world of Artificial Intelligence, Machine Learning and Cryptography where advanced computer skills, not manufacturing assembly skills are the most important ones for citizens to obtain.

While helping people is a good goal, it seems like pushing such large plans through the legislative process in such a short time will lead to resistance that will drive the country farther apart. Given that a lot of the programs may not do much to aid the economy in its global modern form, pushing them through with a slim Democratic majority will also foster ill will. In describing the plans and what they are intended to address, the language used appears to vilify business and business leaders. The tone and rhetoric used to describe them, and the plans themselves seem quite harmful to not just the economy, but the state of political discourse overall.

In this post I want to explain how the tax plans President Biden proposes can stifle capital investment in start-ups, hurt small businesses of all kinds and lose jobs instead of “create millions of jobs” as he projected in last night’s speech.

Revenue Plans
Here are some of his plans that raise taxes to “pay for” some of the initiatives:

  • Raise the corporate income tax rate to 28%
  • Institute a “minimum corporate tax” of 15% on corporations over a certain size
  • Raise the top marginal tax rate to 39.6%
  • Raise the capital gains tax rate to be equal to the top earned income rate (plus the 3.8% Medicare surtax added due to the Affordable Care Act), placing the top rate for selling long term assets to 43.4%. Raising the capital gains rate to one higher than that paid on earned income

Problems with The Plans

-Raise the corporate income tax to 28%

The problem with raising the corporate tax rate is that under the Obama administration (when rates were 35%) we were seeing a number of corporate inversions where the headquarters of a company and a corresponding amount of investment was moving outside of the United States. After lowering the corporate tax rate to 21% in the 2017 tax reduction act this practice almost stopped. We have seen a boost in investment in the United States since the top corporate rates have been lowered and the talk of inversion is now non-existent.

We run the risk of losing investment in our own country by charging rates higher than they are now (21% nominal rate). Compared to countries in the EU that have educated workforces and a “business friendly” climate (like the Republic of Ireland) our top tax rate being higher would give corporations the incentive to move operations off shore again.

The argument Democrats make is that we “don’t want to precipitate a race to the bottom” (lowering corporate tax rates to levels of other countries like Ireland at 12.5%). However, CEO’s of public companies have to investigate the possibility of producing their technology and products in the most efficient manner possible. High tax countries will be less advantageous to certain companies than countries with lower tax rates where their products can be developed for those companies.

According to this source (tax the European OECD countries levy a 21.7% average corporate tax rate. Germany and France are at 29.9% and 28.4 percent respectively, with Portugal the highest in mainland Europe at 31.5%. So running our tax rate higher could precipitate a loss of investment to overseas locales. Far Peak Acquisition Corporation’s Tom Farley, past CEO of the NYSE, made the point that raising corporate rates is a very bad idea as capital is fungible. He mentions that it would be a breach of fiduciary duty to shareholders for a CEO of a public company to not pursue off-shore investment as an alternative to on-shore if the rates increase as proposed under the Biden plan.

-Institute a “minimum corporate tax” of 15% on corporations over a certain size.

This seems to be the “Amazon tax” because President Biden always uses Amazon Inc. as the “bad example” of the corporation “paying zero tax”. He states this in a way that implies they are cheating on their taxes (which they are not; they are following the law). The reason they don’t pay a lot of taxes is that they invest large amounts of their profits into capital equipment to grow their operations and make them more competitive. They build data centers, warehouses, distribution infrastructure, they buy trucks, planes and delivery vehicles. Amazon employs more and more people as a result of this investment. If the 15% of a huge ($54 billion) number is given to the government, then that is 15% not spent on expanding and hiring employees to work in Amazon facilities. I also am skeptical that the government will spend these funds as wisely as Amazon will.

Joe Lonsdale, co-founder of Palantier Inc. and founding partner of 8VC (venture capital firm) also has some interesting views on corporate tax hikes and their impact on start-ups and growing companies like Amazon who invest heavily to maintain growth and employment. Mr. Lonsdale states that rates are best left alone.

So my belief is that we should leave corporate income tax rates where they are and let the for-profit sector build as much infrastructure as they can to grow their businesses.

-Raise the top marginal tax rate to 39.6%

While I think that this is a generally bad idea, if this were the only thing done it may not be as damaging as the other parts of the plan. The next item where capital gains rates are raised beyond the rate of ordinary income is the problematic one.

-Raise the capital gains tax rate to be equal to the top earned income rate (plus the 3.8% Medicare surtax added due to the Affordable Care Act), placing the top rate for selling long term assets at 43.4%. This is a corporate tax rate higher than that charged on earned income

Unintended Consequences

Removes Incentives for Risk Taking

Having the capital gains rate to be higher than earned income undermines the entire venture investing model. It is common for founding members of venture backed start-ups to work literally for years for no or very little salary so that they can build their businesses into something valuable enough to invest in let alone sell for a capital gain. Then it can be years before they earn even a competitive salary that they would have had working at a job with Google, MicroSoft, etc.

My previous start-up was self-funded and I put all the money into the company from past investments I had made without ever taking any salary. For nearly three years I worked essentially “for free”, supporting five other families. My “payoff” occurred when I sold the business. The incentive was to make a good return on my investment. If I had to pay more (in taxes) than I would have had to pay if earning regular (W-2) income I would have been much worse off when I sold the business than I was at the end of the three years when I sold the business to Bloomberg LP. The capital gains rate being lower than the earned income rate enhances the incentive to take risk and innovate on behalf of the economy.

Mr. Lonsdale was again recently on CNBC squawk box explaining that he believes raising the capital gains rate removes incentive for people to leave jobs with large established Silicon Valley firms (Google, Facebook etc.) and take a chance on a start-up. Without a promise of a payoff beyond what one would get earning regular income, most would not choose to take the risk of a start-up and do something innovative. He recently moved his firm to Austin Texas because as he stated: “Silicon Valley is becoming filled with risk-averse individuals”.

He mentioned an example where a person making $3 million per year at Google would have little incentive to join a start-up and to help build something innovative if there were no favorable capital gains treatment. They would have more incentive to stay and earn regular income in such a case than to take a chance and help build a new company.

It takes a lot of courage, commitment and hard work to build a successful start-up and there is risk involved. Therefore, favorable tax treatment is a justifiable incentive to include in the tax code for companies seeking to innovate and build new products. Without this incentive, many jobs will never be created in new industries and the entire economy will suffer.

Makes Established Tech “Giants” even Larger and More Powerful

With less incentive to start companies, the large technology companies will get larger and more powerful. Now, even with a capital gains rate advantage, start-ups cannot compete with the salaries that Google, Apple, Facebook and others are able to offer employees. If this capital gains tax rate is hiked as President Biden proposes, then the technology companies will only get more powerful. Someone on Mr. Biden’s staff should go find Amy Klobuchar (Senator from Minnesota) and break the news to her that they are going to make her job even harder (she wants to break up Facebook, etc.). If Senator Klobuchar thinks Facebook is too big now, then she will be really surprised how powerful it gets when it does not have to compete with smaller, nimble start-ups.

Not Just Start-ups, but All Small Businesses are Affected

It also kills non-technical small businesses that have relied on “sweat equity” to build value into their businesses for years. When a small business owner decides to sell his or her entity they have often sacrificed salary and time with loved ones for years to make the business valuable. To have the sale of the assets taxed at a rate above the earned income rate, it makes the tax burden of completing the sale onerous. Contractors, plumbers, electricians, dry cleaners, restaurant owners will all be affected. This is not a “Wall Street only” issue. It is a “Main Street” issue as well.

Impact on Racial Inequality

Robert Johnson, founder of Black Entertainment Television and a very successful businessman, explained recently how raising corporate taxes and capital gains tax rates will harm black owned businesses. Mr. Johnson was a guest on CNBC Squawk box explaining a plan he proposes for providing tax breaks for investments in black-owned businesses.

Raising Corporate Taxes Harms Black Entrepreneurs

Mr. Johnson pointed out that raising corporate tax rates just reduces the amount of capital available for black businesses. He was interviewed in relation to a plan he proposes where black-owned business should get tax advantaged treatment. Raising their taxes runs counter to that goal and of course stifles economic prosperity for people of color.

Raising Capital Gains Rates Harms Black Owned Private Equity Firms

Mr. Johnson further explained that the Biden plan proposing higher taxes on capital gains is a major blow to Black-owned private equity firms. Mr. Johnson explained that a number of Black-owned private equity firms are “just getting started” (in his words) so such a change in tax policy could damage or totally obliterate their business models.

Since such firms have raised or are raising capital on the basis of a reasonable capital gains rate below the earned income rate (not above it as explained previously) a capital gains rate increase as proposed by the Biden administration essentially puts these new firms out of business.

There are so many unintended negative consequences from the plans put forth by the Biden administration that it is clear they need to be revised. Hopefully they will be changed as they travel through the legislative process.

In my next post I will propose some ideas for how to help the middle class, and also help with racial inequality. There are market-oriented approaches some of which would not require a dollar of tax increases. These would be preferable to many of the things that have been proposed in this tax plan. These are “new thinking” as opposed to “old thinking” (the New Deal was fine in 1932 but it is now 2021).

Economy and Business Politics and Public Policy

Democratic Economic Plans and their Long-term Implications

Caveat: I am not a Republican or a Democrat. I am writing this and other blog posts about the level of spending being proposed by the Biden administration and the associated tax and societal costs these programs present. I am producing these viewpoints to illustrate the impact the proposed spending may have on the economy and business as a whole.

The Democratic Party, led by President Biden have enacted legislation to deal with the Covid-19 crisis, proposed a multi-trillion dollar “infrastructure” bill that includes spending on things well beyond the traditional definition of infrastructure such as roads, bridges and airports. This bill introduces the concept of spending on things such as in-home elder care, museums, and affordable housing as infrastructure. They plan another large “Family Act” that is also reported to be a multi-trillion dollar proposal.

The spending enacted or proposed by Democrats is supposed to transcend $6 trillion. This is an amazing amount of stimulus to add to the economy and may spawn wide-spread inflation in the future. The associated tax plans being proposed (a subject for another blog post) to accompany this spending can have deleterious effects on capital formation in general with a large negative impact on entrepreneurial activity of all kinds (startups in technology markets as well as contractors, plumbers, electricians).

Not All Spending is Bad

The first bill signed into law included a great deal of immediate help for people who have been thrown out of work by the pandemic. This (Covid-19 relief) seems a compassionate and wise use of public funds, and does a lot of good for those affected. Workers from the hospitality and travel industries were displaced economically through no fault of their own. I fully support helping them and wish that politicians had not waited until the Biden administration came into office to help them.

The magnitude of the $1.9 trillion bill was debatable but in the end it was probably wise to make sure help was given to those in need despite the size of the bill. The bill had $350 billion for state and local aid (that many thought mainly benefited highly Democratic areas), but again with the pandemic crisis hitting tax revenues in so many places, it is probably best to err on the larger side of caution and to get help to beleaguered communities.

Some Spending is Debatable but Could be Infrastructure

The infrastructure bill is another matter. The bill contains a lot of good and justifiable projects (Amtrak, roads, bridges, electrical power grid). It also contains many things that are well beyond the definition of infrastructure. It is hard to understand how hundreds of millions of dollars for museums (even though I love museums), Native American language preservation, and underground transit in Silicon Vally (a known earthquake zone) are valid infrastructure projects.

The infrastructure plan also includes items that are aimed at environmental causes and social problems. I think it is fine to expand the definition of infrastructure to broadband network expansion, electrical grid improvements and even projects involving highway infrastructure for electric vehicle charging.

Encouraging the use of electric vehicles may best fit into a transportation bill rather than an infrastructure bill, but I can see it related to electric power grid and vehicle charging projects (which can be seen as infrastructure along with bridges, roads and airports). Including these electric vehicle projects in a transportation bill instead of an infrastructure bill could allow them to be funded out of fossil fuel taxes instead of general tax increases. This may better fit the climate change agenda by trading fossil fuel disincentives for electric vehicle incentives. It may also ease the burden on income-tax payers overall while encouraging more ecologically responsible behavior.

Some Spending is Debatable and Not Really Infrastructure Anyway

Plans that are purely social programs (elder care for $400 billion and affordable housing, the aforementioned Native American language preservation, etc.) are totally suspect and seem disingenuous (when included as infrastructure). The cost of these and the fact that they are included with infrastructure when they are clearly social programs makes we analyze the intent of including them in an infrastructure bill.

Calling elder care “infrastructure” and now coining the term “human infrastructure” to describe them seems intellectually dishonest. Arthur Brooks of Harvard University and previously of the American Enterprise Institute made that statement on CNBC on April 19th of this year.

Mr. Brooks’ point was that something like elder care should be in “other bills” and that we should fairly and honestly debate them on their own merits. Including them in an infrastructure plan and then pushing this through with budget reconciliation and Vice President Kamala Harris casting the deciding vote (should all Democrats support the plans) seems to fly in the face of true bipartisan leadership. It looks more like a calculated political strategy to push through a far-left agenda.

Including them with an infrastructure bill seems excessive and purely political. In the end the bill may get negotiated to something more traditional and less expensive.There may be some bipartisan support if the social programs are removed and the infrastructure bill is amended to include more traditional projects. But it does seem likely that Democrats will push this through which can sow the seeds of discontent that may cause political problems in the future.

We have to wait and see on the other proposal for American Families. Many of the things that have been advertised to be in the proposal for “American Families” are noble and worthy of consideration but as our country is still dealing with the economic problems of a pandemic induced recession the timing and size of these plans concern me. Many of the things in the infrastructure plan and the American Family plan seem like good things to do, I just worry about their cost and the inflationary impact that they may have in the future.

Aggressive Agenda May be “Pyrrhic Victory”

Pushing these large spending programs through with budget reconciliation “because they can” will probably come back to haunt the Democrats at a later date. The political pushback of appearing to push an agenda that seems excessive and liberal may not be worth the good they think that they are doing. Not everyone in the country buys the “invest in America” message being espoused by the Democrats. Not everyone believes that bigger government works equitably and efficiently. We have seen over time that many of these liberal ideas can be reversed in a subsequent election. So rather than getting something with a more traditional set of legislation, this “swing for the fences” and spend trillions of dollars approach can inevitably backfire on Democrats.

Overall Impact on Economy – Inflation

The impact of this kind of spending will inevitably be inflationary. Inflation hits the poor disproportionately hard as it raises costs faster than wages can raise to meet them. It seems like a wiser approach would be to propose more rationally sized bills that can garner some bipartisan support. Having more of us agree on a path even if it leads to a less grandiose outcome than envisioned by Democrats is a good idea.

Economy and Business Politics and Public Policy

Time to Rethink Trillions

Mitch Albom is a thoughtful author and columnist. Recently he wrote this piece for the Detroit Free Press: “Mitch Albom: A trillion dollars is not a billion; why you can’t just print money”. In the article he lays out how spending at the rate we are on large social programs will have a cost, a very large cost.

In the article he explains how politicians are as Rahm Emanual said (para-phrased): “not letting a good crisis go to waste”. I don’t think that Mitch Albom is a partisan individual and don’t think he is favoring one party over another in his analysis of what is historically massive spending on huge government spending plans. The CARES act last year amounted to $2 trillion and was sorely needed but adding on the nearly $2 trillion “American Rescue Plan” last month, followed by a proposal for a $2.3 trillion infrastructure bill described as “The American Jobs Plan” with an advertised $2 trillion “American Family Plan” (soon to be proposed) seems to be incredibly excessive. 

Mr. Albom points out the inevitable devaluation of the US dollar that will result with such an infusion of capital into the economy, and inevitable inflation that will ensue and reminds that this hurts all of us in the long run. Besides higher taxes that always end up hitting the middle class, these programs don’t promote economic growth as they are purported to, and eventually become economic anchors as everyday Americans end up paying more and more of their income to the government which redistributes it unevenly in a way that does not benefit everyone. 

The important points that Mr. Albom makes include that the American Rescue Plan (in Joe Biden’s words) is aimed at “the very health of our nation” with the sales pitch being that it addresses the Covid-19 pandemic. The truth is that (again as Mr. Albom points out) the bill includes more money ($350 billion) for state and local government assistance than it does for direct aid related to Covid-19 vaccinations. Spending on state and local governments that have badly mismanaged pension systems is not directly related to health initiatives. It seems more targeted at helping governments with mostly leaders from the democratic party. This may help ensure democratic electoral success in future elections. This does not seem like wise spending to address “the very health of our nation”.

With the American Jobs Plan (infrastructure) items include $1.5 billion for Amtrak, and hundreds of millions for museums, Native American language preservation, and underground transit in Silicon Valley (a known earthquake zone by the way). The Amtrak expenditure is infrastructure related, but museums, language preservation initiatives, etc. may be lofty goals, but don’t seem to belong in an infrastructure bill. Spending on these types of programs may be aspirational for a minority of the American people, but it does not seem like wise spending in the context of a bill intended to address infrastructure issues.

The infrastructure plan includes more spending on programs to encourage the use of electric vehicles ($174 billion) than on actual construction of roads and bridges. Personally, I am a proponent of electric cars, but this is really an item for combatting climate change rather than something that belongs in an infrastructure bill. Going beyond this, another layer of audacity seems to be the inclusion of $400 billion in spending on senior and disabled person care, affordable housing ($300 billion) and another $35 billion for researching climate change. These are all worthy notions but packaging them into an “infrastructure bill” and selling them on that basis is dishonest. It will eventually lead to more political division as the debt burden will mount on ordinary citizens due to the cost of the programs (which may have no clear tangible benefit).

President Biden is probably a well-meaning and decent man. He has only lived in his adult life as a lawmaker in Washington and does not understand what life is like in the country as a whole. He has no idea how to run a business, meet a payroll, or build an economic entity (a business). He is a politician who believes in big government and wants to preserve power for his party, the Democratic Party. As we have seen over time, the party in power (democrats in this case) always overreach and push too liberal an agenda. In general, all politicians do this by pushing too liberal or too conservative an agenda (depending on who is in power). In this case, Biden is falling into the trap of assuming a mandate that does not really exist. It will lead to more political division within the country.

The Biden administration and the Democratic legislature is pushing a very liberal agenda and acts as if it has a huge mandate. The last election for the house and senate was not a sweeping mandate. The election results not related to our past President were not totally liberal (many voted against President Trump because they wanted a more reasoned and polished approach to government than his administration had provided). Many voting against Trump were not particularly liberal in their views at all. The election as a result did not produce a “Blue Wave” as many in the press and Washington seem to believe. The house of representatives lost seats to the republicans and if our past President had not been so hyperbolic and erratic toward the senatorial runoff elections in Georgia and had not pushed his “stolen election” narrative, the Senate may still be in Republican hands.

 In actual fact the country as a whole is not as liberal as the major east and west coast cities. The large spending and dishonesty over what the infrastructure bills contain will cause a backlash against the Democratic party. Our prior President was such a polarizing figure that it is easy to feel more comfortable with a President who is acting more “presidential” than the prior one. President Biden is much more gentlemanly and behaves in a more “presidential” way than Trump ever did. The mistake that President Biden is making is not just assuming that he has more of a mandate than he does, but also under-estimating the backlash that he is forming within the electorate. If Trump stays off the stage in 2022 then the Republicans are very likely to regain the house and the senate if this liberal agenda continues.

When the tax hikes that President Biden articulated in his campaign rhetoric are proposed, the illusion of a mandate will quickly evaporate. The citizens now are happy to receive checks, get extended unemployment assistance, and other promised government benefits. When the tax bill comes due on ordinary Americans (as it always does and no matter what Biden says it will fall on the middle class) then the electorate will no longer support the wild spending in Washington. The democratic party leadership will have overstepped its bounds and the political tables will likely turn. Biden would be wise to control members of his party who have such an aggressive agenda. Coming out of the “pandemic economy” the country and electorate need time to heal. President Biden is nearly inviting a backlash election that opens the electorate back up to “Trumpian” politics.

So it would seem prudent for President Biden to be mindful that pushing his policies of “trillions and trillions” of spending on an electorate that really does not overwhelmingly support that kind of spending is likely to be a losing proposition in the long run. It will also sadly push an enormous debt burden onto the American people for generations to come. This will inevitably reduce the quality of life here in the long run and sow the seeds of political strife in the short run.

Health and Public Policy

Plans for Covid-19 Vaccine Distribution

In my home state of Massachusetts, the Governor announced a phased plan for distribution of the Covid-19 vaccine to residents today. Other states are likely doing the same thing as they had to submit plans for vaccine distribution to the federal government.

The Boston Globe published the plan here:

The plan was announced as being based on quantities of vaccine that are likely to be approved for Emergency Use Authorization by the FDA soon and that include vaccines from both Pfizer and Moderna. The plan for Massachusetts was announced as having three phases of distribution.

Phase One: December 2020 – January 2021

The first phase, beginning in December of 2020 prioritizes health care workers, long-term care patients, homeless shelter residents, police , fire and other first responders, etc. Groups in this first phase include:

  • Clinical and non-clinical health care workers doing direct and COVID-19-facing care
  • Long-term care facilities, rest homes, and assisted living facilities
  • Police, fire, and emergency medical services
  • Congregate care settings, including homeless shelters, corrections facilities and the staff who work there
  • Home-based health care workers
  • Health care workers doing non-COVID-19-facing care

Phase Two: February 2021 – April 2021

The second phase is forecast to begin in February 2021 and extend to April 2021 for the following groups:

  • Individuals with 2+ comorbidities (high risk for COVID-19 complications)
  • Early education, K-12, transit, grocery, utility, food and agriculture, sanitation, public works, and public health workers
  • People age 65 and older
  • People with one comorbidity

Phase Three: April 2021

The third and final phase begins sometime in April 2021 for “the general public”.

The potentially hopeful thing about this is that Johnson & Johnson Inc. (J&J) and AstraZeneca Inc. could announce data related to the US trials of their vaccines in January 2021. This would add supply to the available quantities that could accelerate the numbers of individuals who could get vaccinated in the coming months.

So the news of these two more (large) manufacturers of vaccine getting supply to market in the first and second quarters of 2021 could be very good news for those wishing to be vaccinated. This development of potentially having more vaccine than is currently planned is speculative but a positive one. Having larger quantities of the vaccine than is forecast to be available from just Pfizer and Moderna alone could enable the vaccination of larger numbers of individuals sooner than currently planned.

So hopefully science is progressing faster than we anticipated and more supply of the vaccine can help us get more people vaccinated than we can plan for today. As many astute observers point out, the vaccine needs to get into a large percentage of the population to be effective at reducing the spread of the virus. Vaccinating a large proportion of the population will take time and the vaccine will take time to build immunity in those inoculated. But getting more people vaccinated sooner rather than later is encouraging.

Science Technology & Economy

CNBC: Vaccine “Glut” May Lead to Normalcy

Jim Cramer on CNBC this morning stated that the production of vaccines that fight Covid-19 may be available in sufficient quantity to have (in his words): “this thing being over in the second quarter of 2021”. The clip of Mr. Cramer making these comments and excerpts of Cramer’s comments can be found here:

Cramer read from a report during the clip that forecast that there may be more vaccine available in the coming months than previously thought. This would indeed be good news for the economy in the new year (2021).