Today’s news, advertising, and daily conversations often discuss AI (artificial intelligence), generative AI, deepfake photos, and videos, and the mad scramble among technology companies jockeying for position with these disruptive developments. Almost every software vendor now proclaims that they embed AI in their offerings. As business leaders, we must remember that “… maturing digital businesses are focused on integrating digital technologies, such as social, mobile, analytics and cloud, in the service of transforming how their businesses work.” (MIT Sloan Management Review, Summer 2015, “Strategy, not Technology, Drives Digital Transformation”)
For all the talk and fascination about AI, an organization’s board of directors and executive management must reflect on whether their organization is making the best commercial use of the data they already have and constantly get. How would an organization begin to use analytics that results in commercial benefits?
Don’t get me wrong: I believe that AI is just beginning to disrupt the nature of work. AI is accelerating information dissemination, quickening judgments, creating new forms of entertainment, and even providing science with new tools for discovery. We may not be aware of it, but we encounter AI daily (for example, to discern interests when you use social media, when you go through immigration, or when you use your credit card). Whether for better or worse, only the future can tell.
That aside, let’s go back to the basics. In their groundbreaking 2007 book Competing on Analytics, authors Thomas Davenport and Jeanne Harris lay down the foundations: “By analytics we mean the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. The analytics may be input for human decisions or may drive fully automated decisions.” There is continuing tension between the voices and opinions of those higher up in the corporate hierarchy versus the facts on the ground revealed by analytics. And evidence from analytics competitors show that the best decisions come from letting the data speak for itself.
From my experience as a telecom executive whose role included the creation of an enterprise data office, starting an analytics journey that benefits the business, and constantly evolving our capabilities, I can offer some starting point questions.
1. What data do we have? Organizations need to realize they already have massive amounts of data and collect more of it daily: customer information, details of transactions, visits to stores, POS data, customer support information, supply chain data such as POs, invoices, and payments. You would also include internal process documents, regulatory filings, and employee and partner data. They even have phone numbers and addresses of customers, employees, and suppliers. And in this era, you can include people using your website or mobile app, and posting feedback or reviews on social media.
2. Is our data clean and up-to-date, and how do we get, record, and process that data? An organization would set up “data governance,” which relates to the roles, responsibilities and processes for data accuracy, integrity, completeness, security, and compliance with regulations.
3. Have we started with a few projects demonstrating the commercial value of data and analytics? You do not have to wait for clean data and data governance to kick in. You can solicit ideas from various parts of the organization and different roles. All they need to do is to ask, “What if I could have x so that I could do y?” Examples might be, “What if I could segment my customer base to target the top customers with an offer that increases their spend?,” “What if I can understand the reasons for customers leaving so that I can improve retention?,” “What if I can watch payments in near real-time to better prevent fraud with abnormal behavior?,” “What if I can improve my B2B leads generation and closing success rate?,” “What if I can predict how many other customers are unhappy based on current hotline calls?,” “What if potential fraud or cybersecurity events that are humanly impossible to filter can be prioritized for review or immediate action?” Analytics is an excellent tool for examining the revenue and cost sides of an organization and finding answers to such questions will show its business value.
4. What are our next steps after becoming more adept with analytics? Do you now go “big bang” and create an enterprise-wide team armed with various platforms? Or do you move forward based on use cases?
5. Do we have the tools to begin an analytics journey? Don’t just shop for consultants or software platforms. Organizations can start with simple processes: find ways to extract the data and find analytical software to begin answering questions. You can even start with spreadsheet software that already has decent analytics tools. As the organization progresses, you might move to “canned” data management and analytics software, evolving to even more powerful tools that demonstrate the power of analytics. With greater sophistication or scale, you might develop your data architecture to consider batch and real-time data, machine learning, and automated decisions and actions. Advanced organizations use open-source software and cloud-based platforms for speed, scale, flexibility, and cost advantages.
6. Do we have the people with the skills for this analytics journey? You can start with a working team from the technical and commercial units. For the best results, set up a cross-functional team composed of IT, data engineers, analytics experts, and customer-facing or revenue people to begin answering questions. As the journey continues, you may have to complement your people with outside experts and even data scientists, albeit these are highly sought (expensive) skills.
7. Have we decided on the organizational model? Will we have decentralized analytics teams embedded in various groups or departments, or will we have a centralized team serving everyone, or will we adopt a hub-and-spoke model with analytics teams in various areas supported by a center of excellence?
The business value of an organization’s analytical journey arises from its nature as a never-ending and constantly evolving journey. Strategies, capabilities, technology, and customer and transaction data change with time. Making the best use of data is an iterative process, one where we are constantly learning about our customers, supply chain, and internal processes, asking questions about our data, and finding out if there is any commercial value to answering our questions.
Remember, starting small, demonstrating business value early, and progressively advancing your analytics capabilities are essential. Regularly reassess your analytics strategy to align with evolving business objectives and technological advancements.
And what about AI? Being better at analytics in business decisions will result in a better understanding of AI and what it promises (or does not).
Gil B. Genio is a retired Ayala and Globe executive. His last role was Globe’s chief technology and information officer from 2015 to 2021, which included the Enterprise Data Office, and Information Security and Data Privacy. He is currently an independent director at publicly listed companies GT Capital Holdings and Puregold Price Club. He is a member of the Management Association of the Philippines and a fellow of the Institute of Corporate Directors.
