Degenerative AI
Ethical Issues to Consider When Using ChatGPT

Why You Should Have Invested in Enterprise Data Yesterday

Think: Data and Analytics

Blog prepared by Gregory Hahn. Greg is a Senior Manager - Data Strategy, Operations and Transformation Senior Manager – at VMware. His LinkedIn profile is available at: @Gregory Hahn.

“If a company has not yet prioritized investing in enterprise data, I would strongly recommend that they consider doing so, as it has become increasingly important in today's data-driven business landscape. Failing to invest in enterprise data may result in missed opportunities, decreased efficiency, and falling behind competitors who are leveraging data to drive their businesses forward.” –ChatGPT

ChatGPT provided that response above in less than 2 seconds. Do you know why ChatGPT can answer this question so easily? Because OpenAI made early investments to holistically organize and prepare data.

Companies like GoogleAppleThe Walt Disney CompanyCostco WholesaleNetflix and many others have done an amazing job at harnessing the power of their data to connect to their customer. You want to know their secret? They invested significantly in data and analytics at the enterprise level. With some companies already surpassing many others from a data and analytics standpoint, customer expectations are significantly higher. While many companies will layer on new technology to keep up with expectations, the most effective solution is to slow down with the right, enterprise-wide governance policies to move fast later and meet expectations with customers. I’ve recently been sharing the phrase, “slow down, to speed up” with my peers internally and externally.

You're probably saying, "Greg, we're doing this right now." I'd respond: Are you doing this in silo or are you actually doing this enterprise-wide?  My guess is and what I'm finding from discussions with multiple companies, across various industries, is that unfortunately the latter is true and the current focus is not working properly.

But don’t worry! If you’re reading this and saying we’re not at the enterprise level yet, this does not mean that you are failing or that your company can’t understand its data. It probably means that you’re working significantly harder to answer questions that should take a day to respond to. Instead, those questions require a week or weeks of preparation to answer. 

Enterprise Data is an incredibly hard thing to achieve and there is absolutely still time to alter your company's focus. One of the easiest selling pitches you can make internally is the fact that regulation is on the way. Take a listen to Data Security At Risk: Testimony from a Twitter Whistleblower and you will clearly understand that regulation isn’t something that might happen, it’s inevitable. 

If you think I'm wrong, check out what the FTC just did to Meta. With proper data controls in place, issues like these wouldn’t happen because they would automatically be triggered by a policy violation. If companies focus on data and analytics, they can prevent things like these from happening. 

When focusing on our data and analytics strategy at VMware, I incorporated some of the digital transformation program work I focused on including collaboration across business units to increase efficiency, how to analyze data in new ways to make better decisions, building teams that will excel in a digital culture and applying AI/ML tools and approaches to successfully serve the organization. I also incorporated some of the work I’ve done with Simplicity and made sure to “Speak Human” as I shared this.

And if you don't already know, you should be focusing on the following, enterprise wide: Enterprise Data

  • Data Management Program – defining, establishing, justifying, and funding a company’s vision for data management.
  • Data Governance – establishing policies, defining and implementing standards, controls and best practices of data management in alignment with strategy.
  • Data Quality – detecting, assessing, and remediating data defects to ensure fit-for-purpose for intended uses in decision making, planning, and general operations.
  • Architecture – designing an optimal data layer to meet present and future business objectives with a platform and supporting technologies that meets scope and performance requirements and manages historical data effectively. 
  • Analytics – examining data sets in order to deliver business insights to employees, partners, and customers with the right information at the right time.

  There are many other capabilities out there that you should consider for your foundational and transformation data and analytics building blocks. The EDM Council has a fantastic framework that can be leveraged if you’re just getting started and want something to look at. Their Data Management Capability Assessment Model (DCAM) can help you unveil what is happening across your organization with data and analytics.

For those of you who think this approach is too academic in nature, I urge you to re-consider. Many large organizations, like Microsoft, have already taken this seriously and have taken the next step to automate their data management practices.

If we don’t embrace this mentality, our data and analytics will continue to suffer, we’ll be providing incorrect answers to business questions and customers will end up going elsewhere. It’s time for us to embrace the phrase “slow down, to speed up” and finally give data and analytics its proper focus within an organization.

How are you handling enterprise data today?

Gregory Hahn (@Gregory Hahn) is the author of today’s blog. Greg is an accomplished professional in the digital transformation space. Greg gives back to the community, which is an ethical value we all should strive to achieve. He is the Founding President of the Young Professionals Council (YPC) for the San Francisco Marin Food Bank (SFMFB). In that capacity, Greg developed a strategy for the organization and participates in planning across events, marketing and giving.

Greg’s blog is being posted on the “Ethics Sage” website of Dr. Steven Mintz on May 22, 2023. You can sign up for Steve’s newsletter and learn more about his activities on his website (