I recently found myself attending the 17th CDOIQ Symposium, an annual gathering of data professionals and thought leaders at the Hyatt Cambridge in Boston. My firm, USEReady, was one of the event sponsors which afforded me the opportunity to listen to and engage with some of the top data minds in the industry. Among these were four speakers: Prof. Tom Davenport, Randy Bean (CEO at Wavestone), Captain Brian Erickson (CDO, US Coast Guard), and Jamie Holcombe (CIO, USPTO) who, during the event, spoke on different aspects of the job that drive Chief Data and Analytics Officers (CDAOs) toward success. Per them, tenure, revenue orientation, product mindset, people empowerment and aligning with Line of Business (LOB) were key.
I have been a practitioner and leader in the data analytics space for 20 years. This experience has given me a front row view of how this industry has evolved. Today, as we stand on the cusp of the next big wave of Artificial Intelligence (AI), I see a few fundamental challenges facing enterprise data programs. In my view, very few organizations have a good handle on these challenges I’m about to highlight.
Challenge 1: Data Technology Rationalization:
The last decade witnessed a tsunami of data value chain tools. The market has flooded with a plethora of tools, with very little differentiation, striving to democratize data for businesses. This, coupled with tools from earlier generations, has created an urgent need to rationalize the Business Intelligence (BI) and Data landscape. The turmoil induced by the aftermath of Covid-19, where we saw huge employee turnover, exacerbated this challenge with many firms bereft of employees proficient in these tools. Naturally, it is a cost that, if rationalized, can help improve the bottom-line for many organizations.
Challenge 2: Fostering Data Literacy and Data Culture:
Decisions cost money. Decisions without data are mere opinions. Modern enterprises and knowledge workers face this new imperative that data literacy is as important as the common literacy we’re familiar with. The CEO-CFO conversation epitomizes the core of this challenge – Investing in people may result in them leaving the organization, but neglecting their development could lead to them remaining with the company. Data illiterate employees will be a drain on organizational resources and there’s an urgent need to foster a data culture at most businesses.
Challenge 3: Balancing Data Quality, Security, and Governance:
There’s another, less talked-about side to democratizing decision-making process with data – governance. Regardless of the operating industry or function, to enable accurate decisioning process, data governance, quality and security must be established. Without these, the foundation of data-driven decision-making remains unstable.
Challenge 4: Developing Innovative Data Products:
Creating a product mindset to data is a new paradigm in the world of data practitioners. It draws from the real-world experience of how we purchase and pay for products and expect a certain level of quality, finish, packaging from the seller. In much the same way, data producers need to acknowledge their responsibility to data consumers. This philosophy creates a balance to the enterprise data environment. We all agree that data is an asset, but can we extend it to say that data is a product? Such holistic principles improve the value of data and thereby the quality of decisions made with data.
These four challenges may seem plain and obvious to many, but the real question is, how do enterprise CDAOs and their data program respond to them? Are there any frameworks, cohesive playbooks that can be implemented? It’s clear that this is not just another “tool problem” and addressing them requires a careful evaluation of the culture, processes, people, technology, and the business domain of the enterprise.
I prefer to segment these challenges into three categories viz., Migration, Optimization and Modernization. Once we segment these, we can start establishing a budget and ROI for each activity.
Finding a budget for modernization is relatively easier than legacy migration. But who wants to pay to replace something that is possibly in a working condition? I believe that this is an opportunity for ISV (Independent Software Vendors). If they can subsidize the migration to gain the market share from the competition, it can create a win-win situation for both the customer and the vendor.
Sometimes optimization of an existing investment makes more sense. A good example of this are investments worth hundreds of millions of dollars in the world of human led data science initiatives. Essentially this was an army of semi-skilled labor behind an excel working out of analytics shops in India. Modern data stack applications such as AIBLE (www.aible.com) have eliminated the human quotient from these data science programs.
Other areas of optimization are improving data literacy levels and implementing data governance initiatives across organizations. These are especially tricky due to the amorphous definitions and nature of data literacy and governance. A good starting point for these would be establishing an enterprise data roadmap or blueprint. Personally, I like the word “manifesto” but it may seem too tired and worn out for our time.
While modernization has gained the lion’s share of the budget and minds in the last decade, I think it’s time we took a pause and reviewed the same. As AI leaps ahead, we must ask ourselves: do we really need traditional databases, and worry about data models or maintaining information in relational structures? Machines don’t care if the data is structured, unstructured, modeled, or SQL friendly. All they crave is raw, meaningful input.
I look forward to the day when I can finally forget my SQL skills.
By Uday Hegde is the co-founder & CEO at USEReady
Uday Hegde is the co-founder & CEO at USEReady. Over the last decade, Uday has steered USEReady to be a leader in Migration, Optimization and Modernization to help organizations succeed with data. Uday’s vision is to scale USEReady as an enterprise CDO, CIO partner with unique solutions like MigratorIQ, Pixel Perfect, Data Cloud Watch that have gained customer adoption in Salesforce, Snowflake, AWS, Azure markets.
Uday has spoken at several industry summits, received awards for the excellence at USEReady and carries a deep passion for the business and community. Uday believes in building businesses that practice the values, Customer Centricity, Community, Continuous Improvement, Integrity, and Humility.
Since 2020, Uday has been serving as Governing Body member at IIITB Bangalore. Uday is a graduate of HBS (Harvard Business School) OPM Program and IIITB in Bangalore. Uday is a resident of Princeton New Jersey and regularly travels across eight offices of USEReady.