Leading digital transformation — Imagine, build, and run to the future

 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.

Photo by Julia Raasch on Unsplash

Calling Bullshit on Big Data?

After nearly 20 years in the public discourse, the term “Big Data” is still teetering on the cusp (or maybe stumbling over the threshold?) of true ubiquity. Yes, you see the term everywhere. But how well is it understood? Has the era of “Big Data” finally arrived? Or is it over? What’s the “big” deal anyway?

The headline and photo caption for Michelle Nijhuis’s piece in The New Yorker, “How to Call B.S. on Big Data: A Practical Guide,” are tantalizingly dismissive: we’re overwhelmed and “information-addled” and “Big Data” is routinely used to confuse and deceive the public. Nijhuis’s actual article is a bit more nuanced, and the University of Washington course that she profiles is even more so: bullshit is pervasive and nothing new; “Big Data” just provides some new tools for crafting and expressing it, potentially more convincing than others because of their supposed objectivity. As Nijhuis summarizes: “While data can be used to tell remarkably deep and memorable stories, Bergstrom told me, its apparent sophistication and precision can effectively disguise a great deal of bullshit.”

The course that Carl Bergstrom and Jevin West designed for the University of Washington Information School is actually meant to address the phenomenon of bullshit “in the age of Big Data.” Their explicit goal is to equip students to “Remain vigilant for bullshit contaminating your information diet,” and “Recognize said bullshit whenever and wherever you encounter it.” But Bergstrom and West acknowledge that data can illuminate as well as obscure meaning. They expect their students to be able to meet data scientists on their own terms—knowledgeably and critically.

So what can a business leader take away from this discussion? Is Big Data just a load of B.S.? Here are some key points to keep in mind:

  1. It’s not easy.
    Peter Drucker, as insightful as he was, spent three decades trying to understand the role of information workers, and went to his grave still searching for ways to grasp and refine these new ways of working. If harnessing information for business value were easy, Drucker or someone like him would have solved it already.
     
  2. Just because we haven’t solved it yet doesn’t mean it’s not worth doing.
    Rome figured out how to deliver water effectively. Boston delivers water effectively. Some of our technology is remarkably similar to the Romans’; some isn’t. In a spirit of progressive elaboration, we continue to tackle this very old problem, and every little investment pays benefits down the road.
     
  3. Don’t get hung up on names: focus on the goal of unlocking value.
    The phrase “Big Data” isn’t really a description of a particular size or kind of data. It’s a narrative device, telling us that we’re looking at a new dimension of opportunity. Think of it as a hook to help us as business leaders envision the possibilities embedded in the information we have on deposit. The goal is to keep focusing on the goals of value and alignment.
     
  4. Healthy skepticism is good…
    …and it’s important for colleges and universities to emphasize that. We need to be able to tell the difference between snake oil and real medicine—and we assuredly want to pass that skill along to our kids. At the same time, though, don’t let skepticism turn into a Jedi Mind Trick—the droids you’re looking for are really there. Call bullshit on the bullshitters, but remain vigilant for real value.
     
  5. B.S. is, was, and always will be.
    Where there is information, where there is communication, there will be bullshit. But guess what? There is real value in information as well—always has been, and always will be. And our job is to find it.
     

The bottom line is this: in business we don’t yet have a good solution for extracting the value of information on deposit. Terms like “Big Data” describe our current understanding of that information, and they can help galvanize us for future. Yes, that information can be aggregated in ways that are deceptive and/or deliberately confusing, but that doesn’t mean it’s worthless. Don’t lose patience; don’t lose nerve—true value is out there!

 

References

PIER Working Paper 12-037, “On the Origin(s) and Development of the Term ‘Big Data,'” by Francis X. Diebold, Penn Institute for Economic Research, September 2012.

“I’m A Data Guy And I Don’t Get Why Everyone’s Obsessed With Data” by Alex Kirk on LinkedIn Pulse, December 2016.

“How to Call B.S. on Big Data: A Practical Guide” by Michelle Nijhuis in The New Yorker, June 2017.

“Calling Bullshit in the Age of Big Data” by Carl Bergstrom and Jevin West, 2017.

“Calling Bullshit in the Age of Big Data: Syllabus with links to readings” by Carl T. Bergstrom and Jevin West, University of Washington, Spring 2017.

 

Photo credit:
Adam Sherez