Information assets. More and more companies are using this term and recognizing the tremendous revenue potential inherent in the information they collect, both intentionally and accidentally. I call this a company’s “information on deposit”. (I’ll expand on that term in a later post.)
As information is folded into the revenue-generating capabilities of a firm, interest in—and more importantly budgets for—information solutions and technology are swiftly moving to business units, rather staying in IT hands.
Note that this is not the same as that older, observed phenomenon of IT siloing, where business divisions responded to a vacuum in enterprise guidance by investing in their own IT teams and solutions. Now, the expansion of exceptionally user-friendly cloud, SaaS, and consultant-led solutions—along with the elevation of more a tech-confident generation used to consumer-driven, single-use tech solutions—has put direct control of information solutions squarely in the hands of “non-expert” experts. These are the folks we at the enterprise level have been accustomed to calling “the business” or “subject matter experts” or even “our internal clients”.
Regardless of where information is being harnessed and managed, information leaders and CxOs alike need to make every effort to lead by example. The easier the tools become to use, the more critical it is to emphasize that information leadership is a team sport, requiring regular collaboration across teams and disciplines. The challenging work at hand is to describe, classify, integrate, share, and govern (that is, control and manage) information assets in a way that creates business value, regardless of the application or solution.
The easier the tools become to use, the more critical it is to emphasize that information leadership is a team sport, requiring regular collaboration across teams and disciplines.
Here are four points to consider:
1. In our increasingly digital world, the focus is on the customer — and all data matters.
One major key to unlocking revenue is to take a broad, holistic view of the business’s customer. Customer relationships are not just about filling product demand; loyalty comes from being a trusted advisor. Good long-term customer relationships are built on good problem-solving experiences, at the organizational or individual level—optimized to the issue of the moment, but also coherent over time. Consultants know that every experience matters. With larger organizations, cultivating this kind of customer-centric view begins with the recognition that no data is without value. This means acquiring, keeping, managing, and using data that would formerly have been discarded as the byproduct of operations or manufacturing processes, and making that data a key input for services and products. (For business units going their own way with single-use applications, this means a serious conversation with vendors about obtaining data and understanding how the vendor uses the data for its own part.)
2. Operations and analytics are no longer separate: business intelligence (BI) is fundamental.
Analytics is no longer an afterthought to transactional systems—it’s the heart of our future information infrastructure. The mountains of structured, semi-structured, and unstructured data that we’re now storing will likely be retained for 10 or even 25 years into the future. Powerful tools already exist to perform advanced (e.g., predictive and prescriptive) analytics, supporting insights into where business is headed. The next generation of information infrastructures will combine legacy content, sensor data, transactional data, and conventional analytic data (i.e., “big data”) into a single, focused solution-agnostic set of data services or information capabilities. These capabilities will enable people, processes and technologies to leverage information assets in support of organizational goals, drive better decisions, and create value. Developing and deploying these capabilities takes dedication and effort, but the potential is nearly limitless.
3. Embrace the Internet of Things.
From business tools like geolocation in vehicle fleets to internet hotspots and devices like remote door locks or thermostats, the IoT generates actual data about things that we previously understood only through manual-entry logs, periodic checks, interviews, and assumptions. Yes, the data needs to be filtered through and analyzed, but it’s an unbeatable source of intelligence about actual use and ways of working. Properly harnessed, it has the potential to bring businesses greater efficiency and process improvements—as well as other benefits such as improvements to product design. And these benefits accrue up and down the supply chain, as businesses share or sell their data assets. For example, McKinsey & Company has estimated that data and mobility services related to the “connected car” could generate as much as $1.5 trillion in revenue by 2030, as automakers and their partners buy and sell vehicle-specific system, trip, advertising, and other data.
The IoT generates actual data about things we previously understood only through manual-entry logs, periodic checks, interviews, and assumptions.
4. Quality through governance
As technological capabilities grow more powerful, it is more important than ever to pay attention to perennial obstacles impeding business intelligence and analytics. I’m referring to the challenge of integrating multiple, disparate sets of data, so that they are fit for use. Data sets that can’t be used—or trusted—are nearly worthless. Governance capabilities and solutions are the key to reliable integration between sensor data, traditional analytic systems, unstructured data, and transaction systems. Data and information governance strategies help define the controls and monitoring structures that ensure access, utility, and quality. A sound data governance strategy is the foundation that ensures that information assets are accessible, usable, and of the highest quality and reliability—in other words “fit for use.” (Once again, this should spawn a serious conversation within the business before adopting a single-use, problem-solver app, to assess the requirements and cost of integration as part of adoption process.)
What have I missed? Add a comment below to share your perspective.