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Going Beyond Traditional BI: Jay Militscher on Data and Discernment

For over 25 years, Militscher has created value in organizations by helping them improve the way they work, improve their existing offerings, and sell information solutions to a market. These days, he’s leveraging data and AI to augment traditional business intelligence with real-time analysis and automated decision-making capabilities.

DATA LEADERS NETWORK: For those unfamiliar with your work in the data space, briefly describe what you’re working on and how you envision the space in 1-3 years.

MILITSCHER: Working with people and companies to help them leverage data responsibly to make money, save money, and reduce risk is top of mind for me. That sounds simple, and as a concept it is. In practice it means organizational commitment, starting at the top. I like to think of it as a business strategy that’s fueled by data and AI, not a separate data strategy that somehow has to align with the company’s strategy reactively.


DATA LEADERS NETWORK: For brand, product, or creative-focused companies, data integrity is usually very low on their list of investment priorities. What are some of the key points you’ve used to convince companies that investing in high-quality, usable data should be a priority?

MILITSCHER: Today, every company is a data company, whether they’re digital natives, tech companies, or CPG, manufacturing, health sciences, or otherwise. Executive teams are increasingly intentional about being data-driven in their strategic planning and decision-making processes. And as generative AI capabilities evolve rapidly, those same leaders are also motivated to innovate on their products, services, and operations to drive revenue growth and profitability.

Solid data management fundamentals enable those firms to do that successfully, at scale, and at speed. Poor data quality means poor decisions and outcomes. Redundant, unused, or otherwise unknown data means wasted expense. All of that means it’s MUCH harder to do anything advanced with data in a sustained way, like AI-based automation or enhancing products with AI.


DATA LEADERS NETWORK: What are the primary ways that data management programs enable organizations to uncover new understanding about their customers, workforce, products, and services?

MILITSCHER: Customer, workforce, product and service, and supplier data are essentially lists of “entities” (a.k.a. “Master Data”), upon which all business is conducted; I call them the four legs of the table, and pretty much all our analytics and AI are standing on that table as a solid foundation.

A leading indicator of success for data management programs is that analytics, operations, and business decision-makers have easy access to well-defined, accurate, comprehensive, and timely information. Ensure these records are sourced (from business apps like CRMs and ERPs) and maintained with some degree of rigor and oversight. Ensure these datasets are made available to all the appropriate analytics functions AND operational business processes.


DATA LEADERS NETWORK: Why is cloud storage becoming the preferred choice for data management?

MILITSCHER: Scale, speed, and integration with operations, analytics, and AI services. On-premise data storage and processing systems tend to require more attention from a wide variety of technical and administrative personnel, particularly around physical data center and networking needs, that cloud offerings avoid. As storage, processing, and speed needs inevitably increase with the functional and speed demands from the business, it becomes increasingly complex to grow and manage this physical infrastructure.

Cloud providers abstract most of this, and offer varieties of scaling options, ultimately allowing effectively infinite expansion as well as the ability to automatically scale up and back down based on demand. Further, the major cloud providers offer fairly seamless integrations with numerous business applications, services, and platforms, whether their own or those of third parties.


DATA LEADERS NETWORK: In your article “Protecting Customers and Your Business with Ethical Data Management,” you assert that ethical data management processes are a nonnegotiable part of any business strategy. Without a national data privacy law in the United States, what motivates business leaders to adopt transparency policies and instill good data hygiene in their organizations?

MILITSCHER: It starts simply with this: it takes years to build trust and just minutes to lose it. Doing the right things with data and AI, especially personal information about individual consumers, fosters trust from those very same people. Connect these ethical approaches to the company’s brand reputation. The way in which the company values and protects its brand can serve as a convenient metric for valuing the ethical treatment of data and AI.

Furthermore, doors of innovative opportunity open with these fundamental practices in place, such as personalized customer experiences, AI-generated content, effective interactive chatbots, and more.


DATA LEADERS NETWORK: What are the critical elements and considerations that must be at the root of ethical initiatives?

MILITSCHER: Transparency. Beyond following the law, there are many mechanical and procedural steps to take, but the most fundamental ethical theme regarding data, analytics, and AI is to provide easy-to-understand transparency to stakeholders.

Make it clear what data is being collected and used, how it’s being protected (i.e. anonymized personal information), how it’s being treated in AI models and analytics, what human oversights are in place to guard against misuses, and what the intended purpose of the use case is (i.e. the benefit to each stakeholder in the mix).


DATA LEADERS NETWORK: What are the most compelling discussions happening around ethical AI right now?

MILITSCHER: The European Union’s AI Act is nearing formal completion and approval, expected in springtime of 2024. It establishes guardrails for the safe use of AI to foster innovation while protecting society. In addition to the law itself, the discussions, debates, and updates to it along the way have been eye-opening and thought-provoking. Leveraging the OECD’s Framework for the Classification of AI Systems, the AI Act takes a risk-based approach to identifying systems that will be prohibited, and providing rules around high-risk AI systems.


DATA LEADERS NETWORK: Many organizations have specific data leader functions that act within the role of the CDO but aren’t called CDOs or Head of Data. What insights or advice do you have for these leaders regarding how to make AI successful in their organizations?

MILITSCHER: Elevate that position to where strategy happens. Companies eager to leverage data, analytics, and AI as a strategic asset succeed when those specific leaders are incorporated into strategic planning and execution. Empowering that executive position with budget, technology authority, and autonomy, in close collaboration across the full C-suite, is really the key.

It’s incumbent on those data leaders to speak the language of business, of course. By infusing the language of data into the C-suite in this manner, we avoid creating a separate data strategy from the business strategy; Instead we have a single business strategy that’s fueled by data and AI.


DATA LEADERS NETWORK: What’s the most successful way you’ve seen for data to find its way onto the balance sheet or income statement to quantify results?

MILITSCHER: The best examples I’ve seen come from extensive case study research done at MIT by Dr. Barbara Wixom. Organizations create value by either improving the way they work, improving their existing offerings, or by directly selling information solutions to a market. Realizing that value as dollars on financial statements requires organizational commitment. For “improvement” initiatives, it means attributing expense reduction.

Recent trends with leveraging generative AI to automate previously manual human effort would be an example, repurposing workforce toward other tasks that reduce the need to add staff. Raising product prices for those with added analytical capabilities would clearly be seen as additional revenue. And, of course, directly selling information solutions also appears as income.


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