– Enterprise
– Wyndham
– Metlife
– Hertz
– B… 2018-02-23
Most data resides unused on corporate servers—information that, if unlocked, could create sustainable competitive advantage. That’s why smart companies are looking for digital change leaders to guide their organizations through the transformation around using data rather than just collecting it.
We’ll call these change agents Chief Data Officers, or CDOs, although they aren’t always called by that title—they may be the CIO, Chief Digital Officer, or lead a line of business. It might be you. Whoever it is, the organization is relying on this person to lead change that probably includes parts of all of these:
This all means big issues, lots of moving parts, and a high-risk challenge for your career. In all likelihood, the board is watching intently.
If you’ve been asked to take on this challenge, where do you start? We believe CDOs have 100 days to get this digital transformation rolling downhill and towards a successful conclusion. If the basic building blocks aren’t in place and moving towards real progress by then, there is trouble ahead.
But before we start on how to make this crucial transition happen, we need a brief look at why it is occurring now.
Chances are the company you work for has evolved dramatically over the last decade, spurred by transformational shifts in technology. These shifts started with open source software, allowing enterprises to access innovation at lower cost and without having to invest in long-term platform decisions. Then, the development of public and hybrid clouds enabled companies large and small to compete at scale and reduce time-to-market for technology solutions. And now, the primary change-driver has shifted once again: to the availability, analysis, and use of data.
Data is everywhere—it flows in from products reporting on their own performance, from digitally-linked business partnerships, from video of shoppers’ traffic patterns around a store, from IoT sensors around the world, and from field reps using mobile apps to retrieve up-to-the-minute customer information before making a call.
Companies are rushing to digitize big swaths of the business that haven’t been previously, instrumenting more and more business processes to capture the data they need to fuel improvements and make better decisions. Some have realized they know more about how a user moves through their website than they know about how a new product makes it to market.
Because of these technological earthquakes, the expectations and skills of today’s knowledge workers around data have evolved rapidly. Individuals who are data-conversant and understand how to connect data, systems, and decisions together are in high demand—just ask GE, which last year moved its headquarters from Connecticut to Boston primarily to be closer to technical talent.
A CDO’s first 100 days should lay a foundation for iterative—but fast—action. The change agent must educate leadership about what needs to be done, and drive home the idea that nothing less than a new way of thinking about achieving business goals is being introduced.
The CDO needs to be an evangelist for the art of the possible. What business actions and decisions can be improved by better data? What unused data can be put to work? By evangelizing what can be done and the ensuing benefits, the CDO helps people overcome their resistance to change.
The value of experimentation is another critical message of this evangelism. Enterprises that can formulate a new hypothesis, construct capabilities to test the hypothesis, gather data about outcomes, and then iterate and try again, have a strategic advantage over their competitors.
One hundred days is not a long time. Let’s get down to specifics. Starting on the road to implementation requires understanding of three things:
First stop on this journey is understanding how data advances business goals. In short, what does the business need to accomplish? All explorations of data and capabilities should be grounded in what the business needs to ensure you aren’t building capability for capability’s sake. How will you demonstrate to the business the impact you’ve made without an eye on what matters to them?
Once you know the business goals, you’ll also have a good idea of important stakeholders to invite along. Establish strong partnerships with them. These will likely include internal business owners, IT experts, and outside business partners.
Get started conducting data audits and catalogs—but delegate this and don’t let it distract from your true purpose. (In our experience, some people think an inventory of assets is the entire job. Believe me, that is just the start.)
More than just finding data, you need to comprehend what it can do. Does it have the predictive power needed to meet the business goals ascertained above? You’ve also got to turn that question on its head. It’s tempting to start from the data, but the big question is: Does the enterprise have the data needed to accomplish its business objectives? If the business wants to improve the customer experience, are you collecting the data that tells you where customers are frustrated today?
Knowing where data gaps exist, by business objective, is a great starting point for your action plan. There’s a good chance your board wants to know, too.
Even if you already know what data is available, is your organization capable of using it effectively, given your current level of data maturity? This is more than grading technology—it’s understanding your complete capabilities: people, process, and systems.
One of your tasks is to evaluate how data, information, and insight are put to use (or not). This is more difficult than it sounds, because the opinions you hear will be influenced by their holder’s place and experiences in the organization. We’ve all heard the story of the CEO who, when asked to describe how a process works on the manufacturing floor, gives an answer that rolls the eyes of the shop foreman. It’s vital that both voices are heard.
It is also vital to identify where talent is misdeployed. You might have the best and brightest PhDs on your machine learning team, but if they aren’t working on problems that impact the bottom line, then why are they there?
The capability to turn data into decisions is not something accomplished by a single department. It’s not the business side of the house that does it, and it’s not IT that does it. It’s both, along with a cast of supporting players that can include engineering, your data science team, business analysts, floor managers, and top executives. Having the right data available at the right time empowers potentially everyone in the company to make decisions quickly and confidently.
The final step in the first 100 days is commonly called putting your money where your mouth is. Saying, “We are becoming a data-driven company” but not asking for data and analysis to support key decisions is pointless and also demoralizing to those who have signed on to follow your lead. The CDO has to evangelize for a new culture that values actionable data as much as previous business models prioritized cash flow.
As you come to the end of the initial assessment phase, it’s time to apply what you’ve learned.
Start by translating your technical and business assessment into a formal action agenda. Without a battle plan, you’re wasting effort. Use business results as a way to focus on what’s important. Finally, think of your action plan as more of a roadmap, where occasional course corrections are inevitable to stay up to date on changes in the competitive environment, internal changes in strategy or tactics, and financial realities.
Here is what you should be able to show stakeholders at the end of the period:
With specificity developed around these three areas and links drawn between them, you should have a compelling story for stakeholders and the executive suite to win support for the next steps, whatever they may be.
As your initial three-month run comes to an end, set goals for yourself and continue generating momentum. When you hit 100 days, pause, take a deep breath, reflect on your accomplishments, and visualize where the road leads next.
Originally published on LinkedIn on June 2017
In today’s hero culture, failure can lead to demoralization, loss of status, or loss of job. Teams that fail too much lose resources and ultimately fall apart. Fear of failure may be especially acute among company leaders, those women and men who are paid handsomely to produce success stories. So, yes, failing hurts and has real consequences. No wonder so many of us fear failure and manage our projects with safety nets and guard rails that rein in risk.
But failure has a bad rap. Failure is not only inevitable, it’s something to be embraced. And in almost all cases, success springs from a series of failed experiments, design iterations, or chances taken that led to true innovation—the ultimate market conqueror. Thirty-nine versions preceded the spray lubricant megahit WD-40.
As leaders work toward building their own experimental enterprise, it’s important to have a perspective on success and failure. You can’t avoid failure, but you can learn to cope and make the most of it. In this post, we’ll look at three ways you can embrace failure:
In the early 2000s, the first three rocket launches made by Elon Musk’s SpaceX were spectacular fiascos, and left the company one more failure away from bankruptcy. Test four, of course, succeeded, thanks to the culture he built in his company of learning from mistakes and iterating forward to build a better product. SpaceX eventually got its first break: a $1.5 billion contract with NASA. Innovative leaders like Musk, Richard Branson, and Jeff Bezos probably have had many more failures than successes—but their wins changed the world, and they didn’t get there on the first try
It’s easy to comprehend why we avoid talking about failure. For example, we may fear seeming negative in a culture of “positive thinking.” In truth, though, planning for negative outcomes is just good planning. Even CEOs don’t control the world as individuals, and we can’t make everything go our way.
And sometimes, if you look closely enough, failure really isn’t. A technology client of ours, a global travel integrator, asked if we could predict costly resource-allocation failures from their business and systems data. Weeks spent on an analysis concluded that we couldn’t predict any failures. It turned out that the data we’d been given didn’t have enough breadth to cover all the required variables. Finding nothing is an uncomfortable situation for any consultant, but the insight was that the data wasn’t correlative with the predictions that the client wanted. We shouldn’t be afraid to say that to our client. And our client shouldn’t be afraid to say that to the boss. The true failure would be not deciding what to do next.
It’s not enough to accept failure if it happens; you must build in the possibility at the start of a project. When preparing, pretend there are binary outcomes—a positive and a negative. You must be prepared for both. What must go perfectly for the whole project to come together? What are those things so critical that, if they go wrong, the project craters?
But it’s not just risk planning around individual projects that we need to be concerned about. Pivotal personnel and organizational components must be addressed to create a culture around learning from mistakes.
Who should be spearheading this methodology? In traditional project management, the project manager better be thinking about both sides of what you are trying to execute. I think the true issue is the leader—the sponsor, or the person that is responsible and accountable ultimately for the investment being made.
Roche CEO Severin Schwan told an interviewer that he holds celebrations for failed projects to underscore that risk taking is endorsed from the very top of the organization. He said, “I would argue, from a cultural point of view, it’s more important to praise the people for the nine times they fail, than for the one time they succeed.”
Another way to overcome that fear is to make failing the objective. In the true agile sense, you do want failure–you want to test fast and find out what doesn’t work so you can go down the positive path. (As my former colleague, Mike Bechtel, puts it, “Failure isn’t what you’re after. You’re after big, honking, 100x success.”) We’ve spoken extensively about working with agile teams.
If you are truly agile and everything goes without a hitch to, say, roll out a new user experience, then you probably haven’t really tested the boundaries of where else success might be found. Failure can propel you to grow faster by accomplishing what innovation consultant Mike Maddock terms “failing forward.”
There is a right way to fail forward; I think of what scientist Max Delbrück called the Principle of Limited Sloppiness. He meant that your research and development activities should be open to and encourage unexpected serendipitous possibilities that appear out of nowhere. But you shouldn’t be chasing rainbows to the point where the results can’t be reproduced.
This isn’t about dressing up failure—just saying, “Well, I learned a lot!,” and moving on. You must dig deep into exactly what you learned. Maybe you discover from a project that you are not especially good at building product or increasing sales, and you need to either strengthen those skills or team up with the right person next time. Rather than throwing up your hands and saying it was out of your control, you must use your perspective to empower yourself to do better in the future.
Great leaders shouldn’t penalize well-conceived risks; they should penalize not taking risks or making the same mistakes twice.
You’ve acknowledged that failure must be planned for, and you’ve changed your perspective so that you see failure as an opportunity to learn. That’s great, but moments of anxiety will still pop up. How can those doubts be mitigated?
As Dr. Guy Winch says, focus on what you can control. Specifically, “Identify aspects of the task or preparation that are in your control and focus on those. Brainstorm ways to reframe aspects of the task that seem out of your control such that you regain control of them.” Have a plan and iterate quickly—that’s what gives you more control. Planned iteration helps you knock out unknown parameters and move to success (or failure) with speed and certainty.
Also, try visualizing your obstacles, just as an Olympics luge driver visualizes shooting down the icy, twisty track at 90 mph. In that early planning phase, when you’re acknowledging what could go wrong, really sit with it. Think about what it will mean for the project, the conversations you’ll need to have, and the solutions you’ll need to develop in order to get down your own critical path.
Becoming comfortable with failure is not easy, not for you or for the organization you lead. It won’t happen overnight. Use the skills we’ve detailed here to fight the knee-jerk reaction of fear, so you can commit yourself and your team to leverage failure into ultimate success.
(For more on the experimental enterprise, and how to build your own, watch our video.)
Originally published on LinkedIn in December 2015.
At Silicon Valley Data Science, we believe in building an Experimental Enterprise that is capable of fantastic learning and growth. In my work with our team of data experts, as I interact with the senior management of our current and future clients, we discuss important considerations and traps to avoid as they become more sophisticated at using data to drive their businesses. We focus on business decisions and philosophy, rather than on individual technology choices. This post provides a window into some of the frequent topics of these discussions.
If you are interested in building your own Experimental Enterprise, then consider the following questions. If you answer “no” to any of them, then that’s an area you’ll need to work on.
In facing any business decision, your first instinct should be to look for relevant data in your business systems to help you evaluate the options. Take a look at past decisions that you may have made on instinct alone, and examine the data that was available at the time. How might that data have given you insight into the eventual outcome? Use this research to understand how you can better inform yourself in the future, so that you can make decisions based on information, rather than on luck. Especially when you’re considering complex or controversial business decisions, using data and a fact-based view to help discuss objections or obstacles will keep the deliberation from being derailed by competing anecdotes.
Paying lip service to fast failure doesn’t help. Ultimately, the reason that failure is quite hard on companies and individuals is that it challenges the established mindset that success is the only desired outcome. To test a hypothesis properly in business, you must have a course of action in mind for both success and failure. Plan ahead so that, if you don’t succeed, you know what you need to do next in order to try again, instead of throwing up your hands. As long as you have a good mindset in this way, then even if your experiment fails, you can still move forward to the next branch in the decision tree. You may have to revisit your assumptions, or even take a first-principles view on evaluating an opportunity, which means: don’t be constrained by the things everyone has established as constraints. But have a plan for failure and understand what you need to do next to make that failure a valuable learning opportunity.
Bubba Murarka notes that the ultimate skill of a good product manager is to have a high rate of idea validation, not just a high rate of execution or experimentation. Achieving this requires a mindset shift from aimless exploration (“What is the data telling me?”) to purposeful fact-finding (“What do I want to learn and how do I collect or generate the data to support my thinking?”). In addition to evaluating the outcomes from business experiments, you must also evaluate your specific hypotheses and what led you to create them. To have a high rate of idea validation, you have to question your questions, not just your data.
In becoming an Experimental Enterprise, there are numerous traps that may hinder your progress or force you into sub-optimal decisions. Watch out for the following.
There are no magic tools or platforms that solve everything. We recommend starting with clear intent on a set of business objectives (what do you actually need to accomplish in order to move your business forward?). Then move with purpose to understand how the data you have now, or will have in the future, can assist you in accomplishing those objectives — and what tools you’ll need to wrangle that data. Your choices of technology and analytic platforms should be driven by current and future business needs, not by current fads or legacy constraints.
We live in a world of probabilistic data. Data elements are generated from so many sources and with so much inherent lack of quality that attempting to construct perfect views is a fool’s errand. We used to talk about “analysis paralysis” in the past, but it manifests even more strongly now that data is more ubiquitous. You must avoid becoming paralyzed while waiting for perfect data to arrive to validate any decision. Perfect answers don’t exist. Instead, use the data you have to make a more informed decision — as long as you’re also careful not to conveniently tailor the facts to your hypothesis.
Fifteen years ago, I used to ask clients to just give us their data in the hopes of finding analytic gold. Though this approach can be helpful at times, I’ve come to the conclusion that it’s usually not. Your data can’t provide innovation for your business on its own. It can provide insight into what is happening, or help you predict what might happen in the future, but data only leads to benefit if you have a plan. You must independently develop business intent and ask good questions. There will be times when it’s worth mining your data to inspire yourself, though: sometimes starting small (with an incremental increase in sales, improvements to margin, or a lift in customer engagement) will help you to understand bigger potential actions as well as where your data can help, before you start to dream even bigger.
The world of data is changing. The skillsets of the people who use it are changing; consumer expectations are changing; even employee expectations are changing. In the conversations I have with current and potential clients, they recognize this: the recruits they’re talking to are eager to work with data in their decision-making processes. The fundamental relationship of people to data has changed. To truly take advantage of this fact — to thrive — your business must adapt.
An Experimental Enterprise is, fundamentally, an organization that thrives on change, and that uses data as a catalyst. Becoming an Experimental Enterprise means reshaping the way you and your company see things like failure, the role of technology, and your own gut instinct. But the benefits are potentially limitless.
Originally published in September 2014 at the svds.com blog and LinkedIn.
I recently spoke about “Unlocking Business Opportunity from Big Data” to a group of former CEOs and senior business executives. Glen Matsumoto, Partner and head of the NY office at the private equity firm EQT, invited me to speak with EQT’s group of Industrial Advisors who serve as Board members for a variety of companies in the industrial and infrastructure industries.
I told this group of executives about our philosophies at Silicon Valley Data Science, including becoming a data-driven businesses, how to approach an emerging technology like Big Data by setting the right high-level agenda via a data strategy, understanding the building blocks of an Experimental Enterprise, and how they can be marshaled to create results. Finally, I shared some industry examples, highlighting successful initiatives at other companies that have created lasting business results from big data.
It was fascinating to engage in dialogue with this group during the Q&A portion, and to hear their questions about data science and big data—which largely reflected Board-level concerns around strategic growth.
The first question was: “Should we be concerned about big data and privacy?”
This has been a hotly-debated topic within the data community for years, but I was intrigued that this question was also top-of-mind for this group of senior business leaders. As I shared with them, we believe that businesses have to fundamentally center the conversation on trust. I encouraged the group to focus on creating trusted relationships with their employees and customers, building upon a foundation of clear and transparent communication about how data is being used by their businesses.
Big data technologies and the data science algorithms applied to that data do have the power to create incredibly detailed understanding of customers and businesses. By working to create a trusted relationship with customers, companies can change the conversation from one about fear and distrust of technology, to one about the benefits (or not) that a customer can expect from the use of their data.
Because most companies evolve their use of data over time, privacy policies and corporate communication must be clear about how data can be used, how the customer may (or may not) benefit, and the available mechanisms to opt-in or opt-out of data collection or analytics. Companies must also focus on more visibility for customers—no one is going to read through a 30-page privacy policy, so summaries of key concepts are a must.
Separately, we recommend that all of companies be prepared for a data breach. Investments in security and protection of data are clearly important and should be a high priority for any CIO. However, with large corporate data breaches highlighted in the news almost every week, privacy failures have come to feel inevitable. Having a clear strategy and plan in place for how to deal with a data breach is an important facet of building—and retaining—a trusted relationship with customers and business partners.
The second principal question was: “How do we ensure that we get business value out of big data investments?”
I shared our view on how to prioritize business investment in big data by creating an effective data strategy that focuses on how to use data to enable your business, as opposed to figuring out what to do to data as the end point. I advised the group to make sure that business objectives were well understood, and to then understand what technologies could be used to support those objectives.
We highly recommend that technology organizations understand the generic patterns (i.e., technical workloads) of how technology can be applied to address specific business objectives and use cases. With this understanding, companies can take an agile and iterative approach to deriving business value from new technology investment—to ensure that the question is not whether there is benefit in investment in big data, but rather how quickly benefit can accrue to the business.
(My co-founder and CTO, John Akred, and our VP, Advisory Services, Scott Kurth, recently gave a seminar on our approach to data strategy. If you’re interested in learning more, you can sign up to be notified the next time they offer the seminar.)
There were several other things discussed, but the last question I will highlight was: “In what areas of strategic planning should we make sure to consider when looking at big data?”
We discussed five major areas in which data science and big data should be considered when doing strategic planning:
If you haven’t yet talked with your own Board of Directors about what you’re currently doing with data, now is the time to answer the questions that may be on their minds.
Start a conversation about trust, but also discuss how to communicate your privacy policy clearly, and what you’ll do if there’s a data breach. Make your data strategy explicit (and if you don’t have one yet, we’d love to help!). Explore the areas of strategic planning in which you may not yet be using your data to its fullest potential.
What other questions about big data have you gotten from your board?
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