Corporate America is Still Building-Out Big Data

Five of the six largest market cap companies in the U.S. are highly reliant on (and built by) Big Data. This should come as no surprise - at least not to experts like Laura Veldkamp, a Professor of Finance at Columbia University, who delivered the keynote speech at the 2023 Long-Term Asset Management Conference in Chicago. She confirmed the build-out is still very much alive and well when she brought up the recursive feedback loop of data that propels the firm to keep building out more data... her words:

“All of those reasons that a firm might be more profitable with more data would encourage the firm to grow bigger, and do more transactions, and that in turn generates more data. This is an increasing returns feedback loop.”

The self-perpetuating cycle Veldkamp described is well known by data-driven giants like Apple, Microsoft, Amazon, Alphabet (Google), and Meta (Facebook) who have long embraced data analytics and AI, and have the bottom line success and sustained growth to prove it. In their own unique ways, each of these trillion-dollar companies has monetized their data in extraordinary ways.

The undeniable reality of the Big Data advantage should serve as a wake-up call for businesses who have yet to hop onboard the data train. “That three symbol equation system could explain data-fueled monopolies,” says Veldkamp.

New players in the data game

“Data and technology are driving business change,” said Randy Bean, the founder/CEO of data analytics firm, NewVantage Partners, in the Harvard Business Review. “The ability to deploy data as a competitive business asset is what has distinguished a set of well-established, data-rich companies who have reigned as market leaders over the course of the past several decades.”

When JP Morgan CEO Jamie Dimon was asked about the upstart, data-driven Fintech players that have recently thrashed the traditional banks, Dimon had one message to his management - “Be frightened.” Global competitors like PayPal, Square, Stripe, Ant Financial, and WeChat, as well as many of the aforementioned top market cap corporations, have jumped on the data analytics bandwagon. In some cases, new players are being flagged as examples of unfair competition. Dimon called out bank verification company Plaid as among the “people who improperly use data that’s been given to them.”

The myriad of ways data can be abused by companies of any size or type include:

• Selling customer data to third parties without explicit consent.
• Using collected data for targeted advertising without user opt-in.
• Sharing sensitive information with government agencies without user knowledge.
• Analyzing personal data to discriminate against certain demographics in hiring or lending decisions.
• Utilizing data to manipulate consumer behavior or opinions without transparency.
• Storing data insecurely, leading to breaches and exposing user information.
• Employing deceptive practices to obtain more data than necessary from users.
• Monetizing data by creating profiles of individuals for sale to advertisers or other entities.

Data done right

“The nature of information is that it’s a way to resolve uncertainty or reduce risk, help us predict things better so we’re less surprised by them,” Veldkamp went on the explain in her keynote speech. “We need to bring in our knowledge on how to model uncertainty and risk, how to price risk, how to measure risk, and this affect could be big.”

Predictive analytics is a broad term describing a variety of statistical and analytical techniques used to develop models that predict future behavior and events. Not surprisingly, firms like Amazon, Google, and Microsoft are also at the forefront of the artificial intelligence (AI) new frontier. Predictive analytics leverage the combination of AI, machine learning, and data mining to analyze current and historical facts, then make predictions about future events.

As any insurance company actuary will be quick to tell you, the more data you have, the more accurate your predictions will be. Bean and Gupta of the Harvard Business Review explained the impact of data on insurance companies in greater detail: “In contrast to traditional insurance companies, which have been data-rich but have customarily relied on actuarial approaches, startup competitors like Lemonade and Traffk are employing machine learning analytics and drawing upon thousands of data elements to provide personalized analysis and drive insurance purchases.”

How the top data dogs stay on top

To retain a leadership position, traditionally data-rich companies need to evolve their data and analytics processes to integrate the latest technologies, or run the risk of falling behind. This means fully embracing Big Data, AI, and machine learning, and implementing five high-value tactics:

1. Know your business, and prioritize your data.
2. Link technology investments to high-value business objectives.
3. Centralize data infrastructure, decentralize customer management.
4. Teach C-Suite executives about the business value of AI.
5. Start small and look for quick wins, while also recognizing transformational change is often measured in decades.

“One of the greatest assets for any business is the data it keeps on its customers, including customer interactions, transactions, and behavioral history,” according to Bean and Gupta. Organizations need to be open-minded and take a holistic approach to data. Too often, data that one department deems useless is discarded even though another department might see value in it. In other words, successful data-driven companies realize one man’s data garbage might be another man’s data gold.

Companies that sustain a leadership position also understand that technology is a tool, not a solution. Innovative companies invest in capabilities that provide a unique competitive advantage while teaching their teams to become experts in quantitative decision-making. In yet another example of a cyclical model, they then leverage this employee knowledge to improve the the analytical models they build.

The undeniable AI advantage

Enter AI. Now, with the help of AI, companies are able to do what they simply could not do before, and do it faster. They can identify contextual information from their data sets and use it more advantageously. The concept of using automation in analytics is sometimes called augmented analytics, or smart data discovery. AI reduces the reliance on human time, talent, and judgement by identifying patterns and relationships in data sets that might otherwise go unnoticed. Identification of these nuanced data relationships can also supercharge the increasing returns feedback loop.

According to a Databricks survey, 90% of the respondents believed that unified analytics — the approach of unifying data processing with ML frameworks and facilitating data science and engineering collaboration across the ML lifecycle, will help to smooth the integration of AI and corporate Big Data. However, 96% of the respondents also cited multiple data-related challenges when moving projects to production. This signals that while AI is clearly the way of the future, successful integration is not assured.

In a 2023 study, McKinsey Global Institute found that AI has the potential to generate value equivalent to $2.6 trillion to $4.4 trillion in global corporate profits annually. Each of the 63 use cases included in the study relies on voluminous data to power learning algorithms and improve data quality and effectiveness.

While Big Data may have built the top corporations of the 21st century thus far, those that innovate and integrate AI successfully will build the next generation of high-powered mega-corporations. And here are the targets for eager AI / Data Vice President ‘new hires’:

• Accelerate Decision-Making: As a new executive, prioritize the implementation of AI to automate and accelerate complex decision-making processes, enabling more agile responses to market changes and business opportunities.
• Elevate Customer Engagement: Focus on using AI to deeply analyze customer behaviors and preferences, aiming to deliver highly personalized and engaging experiences that boost satisfaction and loyalty.
• Streamline Supply Chain Efficiency: Champion the adoption of AI for predictive analytics in supply chain management, to foresee disruptions, optimize inventory, and ensure a smooth, cost-efficient supply chain operation.
• Drive Product Innovation: Leverage AI's rapid data analysis capabilities to identify market trends and consumer needs, facilitating the development of innovative products that meet demands and open new revenue streams.
• Secure Competitive Advantage: By integrating AI into your business strategy, set a vision for a more dynamic, efficient, and customer-centric operation that distinguishes your company from competitors.
• Enhance Risk Management and Compliance: Initiate the use of AI tools to proactively identify and mitigate risks and ensure compliance with regulatory standards, safeguarding the company's reputation and financial health.
• Boost Employee Productivity and Morale: Advocate for AI's role in automating mundane tasks, freeing up your team for higher-value work, which can lead to increased productivity and improved job satisfaction.
• Address Data Management Challenges: Acknowledge and tackle the challenges in data management and AI integration head-on, by establishing robust data governance and IT infrastructure strategies that support the seamless adoption of AI technologies.

Staying the Big Data course

In the evolving corporate landscape, the dominance of Big Data has become not just a trend but a fundamental pillar of success. With giants like Apple, Microsoft, and Amazon spearheading this revolution, it's clear that the strategic utilization of data is a key to achieving market supremacy. Top companies also recognize how the cycle of data collection-profit-better data-more profit continues unabated when data is managed successfully. However, as the adage goes, with great power comes greater responsibility. From unauthorized data sharing to discriminatory analytics practices, the gravity of the potential risks calls for stringent ethical considerations.

With predictive analytics and AI at the forefront, more businesses large and small are recognizing the unprecedented insights and advantages data can provide. Success in this new era hinges not just on technological prowess, but on a commitment to ethical data stewardship and continuous innovation. As we enter the next phase of our data-driven future, those who navigate this landscape with integrity and ingenuity will undoubtedly lead the next generation of successful businesses.