Analytic business competitors apply smart data science to support their distinctive capabilities and strategic advantages.
Peter Prince

2020-11-24 09:30:00 Tue ET

Many analytic business competitors can apply smart data science to support their distinctive capabilities and strategic advantages.

Thomas Davenport and Jeanne Harris (2017)

 

Competing on analytics: the new science of winning distinctive capabilities

 

Smart data analysis has grown in importance in the modern business world. Many analytic business competitors apply smart data science to support their distinctive capabilities and strategic advantages. Many tech business organizations such as Facebook, Apple, Microsoft, Google, and Amazon (FAMGA) use big data analytics for finance, performance evaluation, risk management, lean operational efficiency, and many more. These data analytics can further be applicable to external systems, processes, and business methods such as sales, logistics, and outside operations. From most prerequisites and detours to feasible smart data analytics, key business organizations must often strive to transform their agile lean analytic operations into iterative continuous improvements, new product feature enhancements, and multi-purpose service innovations. In order to support smart data science, most business organizations must focus on relational databases, cloud software repositories, dual transformation processes, agile lean analytic applications, data visualization tools, and big data deployment operations. In combination with agile lean operations and disruptive innovations, smart data analytics can serve as powerful leverage for key business organizations to gain competitive advantages and distinctive capabilities over time.

 

Analytic business competitors often apply smart data science to optimize particular distinctive capabilities for better product and service differentiation.

Modern business analytics involve the extensive use of data, quantitative methods, explanatory and predictive models, and actionable business insights and decisions. As product differentiation and cost leadership have both become less prevalent in the current techy business climate, core business operations and processes serve as the current frontiers where companies can create competitive advantages over time. Many analytic tech business competitors such as Facebook, Apple, Microsoft, Google, and Amazon (FAMGA) manage to extract great value from core business operations and processes, and these tech titans often embed smart data analytics into their products and services. Analytic business competitors often learn to apply both big data science and econometric analysis to optimize distinctive capabilities in a unique way. Competing on data analytics is easier for tech titans that generate large amounts of transaction data in light of voluntary user content curation.

Analytic business competitors focus on some particular distinctive capabilities and proprietary assets. These core assets and capabilities set the tech titans apart from their closest rivals and other competitors in mobile connectivity, social media, cloud automation, software, Internet search, online advertisement, and e-commerce etc. In the hot pursuit of both sustainable and disruptive innovations, the lean approach to smart data analytics pervades many high-tech business organizations. Analytics are often essential in many different teams of 5 to 7 data scientists and statisticians. In the broader business context, both business leaders and senior managers often commit rare and valuable resources (such as finance and personnel) to the use of smart data analytics. In sum, both business leaders and senior managers embrace smart data analytics to make wise and prescient strategic decisions with both facts and actionable insights. From time to time, analytic business competitors can apply significant strategic foresight to invest in smart data analytics. These investments often turn out to be substantial enough to cause iterative continuous improvements in sales, profits, customer interactions, and blue-ocean market developments.

At the early stage, analytic business competitors understand the value of big data analytics for both business leaders and senior managers to apply better actionable insights and strategies etc. When these analytic business competitors pull together agile lean teams and committees to overcome several small hurdles, these analytic trailblazers continue to use big data analytics to make wise and prescient strategic decisions. These decisions can lead to sustainable and disruptive innovations. The former focus on incremental service and product feature enhancements, whereas, the latter emphasize technological advances that revolutionize future industries (or even consumer behaviors) in due course.

Many high-performance analytical business competitors apply smart data analytics strategically to their day-to-day core operations. First, these business competitors gather unique forms of customer data that most other rivals cannot duplicate over time. Second, these analytic business competitors organize and transform big data into novel forms such as pivot tables and relational databases etc for subsequent rigorous data visualization and econometric analysis. Third, such analytic business competitors develop proprietary algorithms, quantitative tools, and key systems for the seamless integration of customer data into distinctive capabilities, operations, and other business processes. The core business operations and processes span finance, performance evaluation, risk management, operational assurance, talent retention, and regulatory compliance. Sometimes these data analytics can further be applicable to outside systems, processes, and business methods such as sales, logistics, and extrinsic operations.

For instance, key mega banks hire smart data scientists, financial econometricians, and quantitative risk managers to run logistic regressions to gauge the probabilities of credit default for a wide variety of loans such as first-lien mortgages, home equity loans, home equity credit lines, auto loans, credit cards, specialty loans, small-to-medium business loans, wholesale large corporate loans, commercial real estate loans, and asset securitizations etc. Each default probability is one of the main risk parameters that determine both the regulatory and economic capital requirements for bank loan portfolios. From time to time, the credit default probability reflects the pervasive borrower exposure to macrofinancial risks and economic recessions etc. Key macrofinancial factors and indicators include the S&P 500 stock market return performance, prime interest rate, Treasury term spread, corporate default spread, aggregate stock market dividend yield, retail fund growth, institutional fund growth, money supply growth (or CPI inflation rate), wage growth, and industrial production growth. Sometimes the proper macroeconometric adjustment for credit risk capital involves hundreds of billions of dollars across the entire global financial system. In a nutshell, the total capital shortfall manifests in the extreme losses that can arise from rare times of severe macro stress such as the Global Financial Crisis of 2008-2009, the European sovereign debt debacle, and the recent rampant corona virus pandemic outbreak. Data scientists, financial econometricians, or quantitative risk managers can often contribute to the high-skill risk factor discovery. This discovery shines light on the current loan-to-value ratio, behavioral FICO credit score, debt-to-income ratio, delinquency status, and many more. These data analytics can help inform financial risk capital quantification for both regulatory compliance and bank loan interest rate evaluation over time.

Another example relates to macroeconomic data analytics. The neoclassic school of macroeconomic thought suggests that the welfare cost of inflation is low or no more than 0.1% to 0.3% of total real GDP per year. Also, Keynesian search theory indicates that the steady-state unemployment rate can often be indeterminate as a result of labor market frictions. In this fresh light, the self-fulfilling beliefs of stock market valuation help inform the long prevalent unemployment rate in equilibrium. In a unique fashion, the New Keynesian Phillips Curve has substantially flattened since the Great Moderation of both low inflation and low economic growth around the turn of the new century. Since the early-1990s, there has been no mysterious and inexorable trade-off between inflation and unemployment. Further, there has been almost no or little effect of the monetary policy interest rate on the output gap since the mid-1980s. In a nutshell, both the Treasury and Federal Reserve System must coordinate their fiscal and monetary stimulus to sustain most macro financial markets for stocks, bonds, currencies, and commodities as the real economy goes through rare disasters such as the subprime mortgage crisis of 2008-2009 and the recent rampant corona virus global pandemic outbreak.

Key empirical analysis demonstrates that macro eigenvalue volatility helps predict some recent episodes of high economic policy uncertainty, recession risk, or rare events such as the global pandemic outbreak. This statistical analysis helps gauge both macrofinancial conditions and economic relations. Stock market valuation can inform fiscal and monetary policy reactions for the Treasury and Federal Reserve System to achieve maximum employment, robust economic growth, price stability, asset market stabilization, and so on. There is often an informative, persistent, and econometrically significant negative relation between the long-short decile return spreads for value and momentum. Neither the q-theoretic rational risk models nor the behavioral mispricing models can explain this ubiquitous phenomenon. In fact, this empirical phenomenon prevails not only in the American stock market but also in most global markets for stocks, bonds, currencies, commodities, and many more. Perhaps professional arbitrageurs can decipher value portfolio returns to improve momentum portfolio returns and vice versa. Dynamic conditional factor premiums exhibit mutual causation with macroeconomic innovations. This causation can thus serve as a core qualifying condition for fundamental factor selection. To the extent that macrofinancial innovations often cause average asset return fluctuations, this causation can help us demystify the long prevalent puzzle of an empirically robust trade-off between value and momentum.

The new data economy grows fast in terms of the exponential pace of digital data growth. A genetic research firm 23andMe acquires more than 10 million customers, and each of these customers provides his or her unique human genome sequence of 3 gigabytes of data. One of the big 4 banks analyzes almost 5 petabytes of retail consumer data for retail risk capital quantification for mortgage loans, credit cards, auto loans, specialty loans, and small business retail loans. The new autonomous vehicles produce almost 30 terabytes for every 8 hours of real-world transport on the road. A global market research firm IDC expects the modern world to generate 90 zettabytes of data per year from 2020 to 2025. This exponential data generation exceeds the vast amount of electronic data since the advent of personal computers. Smart data analytics help better inform several strategic decisions with actionable business facts and insights.

 

Analytic business competitors should strive to hire high-skill econometricians, data scientists, and software engineers etc to ensure data integrity and consistency.

Business organizations must strive to evaluate their proprietary data environments. These analytic business competitors often hire high-skill data scientists, software engineers, and statisticians to ensure data integrity and consistency. From time to time, high data quality supports better business decisions and strategies. With data quality assurance, analytic business competitors can learn to embrace data-driven facts, insights, and decisions. These wise and prescient decisions often arise from the broader business context in light of data-driven facts and corporate strategies. Both business leaders and executive managers must make time to assess whether they gain sufficient culture changes, skills, and IT competencies to bolster analytic business competition. After these leaders evaluate their distinctive capabilities, the key teams must straighten the corporate path toward analytic business competition. If this approach is a top priority, both business leaders and senior managers must strive to make significant progress in the medium term.

Some companies prefer to test the proof of concept that smart data analytics can be valuable through small-scale pilot experiments before these analytic business competitors delve into the more granular technical details. This approach generally adds another 2 years to the reasonable time frame of analytic business competition. These pilot experiments and iterative continuous improvements can allow key data scientists, statisticians, and software engineers etc to learn from small steps, tests, mistakes, and even epic failures. Success often happens when opportunity meets preparation. In practice, success is not final; failure is not fatal; what really counts is the courage to continue with great patience and perseverance.

Both business leaders and senior managers develop world-class data analytics to support key distinctive capabilities, competitive moats, and first-mover advantages. At any rate, the data science teams must remain vigilant to avoid complacency. In the modern business world, most agile lean tech enterprises learn fast to break the mold. In a fundamental view, smart data analytics often defy conventional wisdom, lean against the wind, and empower key disruptive innovators to advance the next high-tech intellectual capital accumulation in due course.

 

Analytic business competitors can aim to extract new actionable business insights from smart data science and creative data visualization.

Professional data scientists and econometricians often interpret and communicate both the key quantitative results and qualitative conclusions of smart data analytics to business leaders, senior managers, and other decision makers etc. Explanatory data analytics tend to emphasize the fundamental factors and their implicit reasons for explaining why new actionable insights and suggestions arise from the broader business context. In comparison, predictive data analytics focus on the quantitative implications of specific ranges of forecasts that inform the current key decisions in preparation for both the best-case and worst-case scenarios in the future. It is often quite helpful to use a reasonable range of creative data visualization tools such as barcharts, Box-Whisker diagrams, scorecards, regression lines, polynomial curves, scatter plots, and so on. Top tech titans tend to embed cloud data analytics in their prime products and services. In summary, most analytic business competitors can continue to outperform their rivals and peers in the open blue-ocean markets.

 

 

This analytic essay cannot constitute any form of financial advice, analyst opinion, recommendation, or endorsement. We refrain from engaging in financial advisory services, and we seek to offer our analytic insights into the latest economic trends, stock market topics, investment memes, personal finance tools, and other self-help inspirations. Our proprietary alpha investment algorithmic system helps enrich our AYA fintech network platform as a new social community for stock market investors: https://ayafintech.network.

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