The importance of data science is only going to grow in the coming years. As we see the results of our data-empowered work take form in how we shape our businesses, our products and our own goals, we are beholden to take a reflective gaze at the relationship between our daily tasks and the organizations that they serve. For this reason I’m suggesting three actions that any data scientist can take to maximize benefit to themselves and to the companies that they serve.

1. Participating in the Leadership process

Principles of leadership are a dime a dozen. There are countless inspirational books, words of wisdom, and pithy witticisms that tell you how to be a good leader, what are the characteristics of good leaders, how to lead, when to lead, etc. One of my favorite quotes is this: “Good judgment comes from experience, and experience comes from bad judgment.” For me, that’s because good leadership relies on informed decisions that ultimately come from rigorous and meticulous work of trial and error, especially when it comes to data.

In the brave new world of business analytics fueled by big data, there has been significant discussion about the evolving roles of C-suite executives, including the CEO, CTO, and CIO. That discussion is now expanding to include the CMO (Chief Marketing Officer) plus the new roles of CDO (Chief Data Officer) and CDS (Chief Data Scientist). I do not have an MBA and I usually don’t undertake risky behavior, such as telling a CEO how to run her or his business.

However, it is entirely appropriate for the CMO, CDO, and CDS to step up to the challenges of leading and directing the analytics, big data, and data science efforts of their organization, respectively. It is also appropriate (and should be in the job description) for these execs to stand firm against corporate cultures and naysayers that resist big data analytics projects with these types of remarks: a) “Let’s wait and see how it develops elsewhere”; b) “We have always done big data”; or c) “What’s the ROI? Show me the numbers.”

We can counter this kind of thinking with this line from Admiral Grace Hopper:

“The most dangerous phrase in the language is, `We’ve always done it this way.’”

 At the end of the day, it is not the responsibility of a data scientist to make major business choices on behalf of a company, but it is critical that all data scientists are able to thoughtfully, methodically, and if need be, passionately point out the most important aspects of what they uncover so that those who do have major leadership roles can make the most important decisions with the most data-informed insights.

2. Measuring Customer Experience

Continuously connecting the quantities that we analyze to the experiences that they measure (or directly influence) is critical to viewing our work through a strategic and ultimately more beneficial lens. Customer Experience (CX) is a top priority focal point in data-driven digital business transformation. Customer-centricity is not new, of course. However, in the modern digital business context, the conversation around customer-centricity focuses on steps to measure CX, to optimize CX, and to apply design thinking around CX across the full customer journey.

This focus on experience management (measurement, optimization, and design thinking) has broader application in UX (User Experience) and DX (Digital Experience). In addition, we see experience journey management being discussed and applied in other domains, such as Healthcare (Patient Experience) and Human Resources and Human Capital Management (EX: Employee Experience).

We focus the discussion here on the customer, though of course the concepts can equally well be applied to any digital users or stakeholders (including B2B clients). So, what is CX? Matthew Wride’s definition of EX can be paraphrased within the context of CX in this way:

“The Customer Experience is the sum of the various perceptions that customers have about their interactions with your organization.” 

CX is something you measure, digitally. But the CX journey is not measured and managed in isolation at independent disconnected touch points along the customer’s journey (e.g., with the website, the app, the service delivery person, the call center representative, the sales clerk, or the billing office), but across the full integrated summation of those data points. After all, a “journey” is not a point, but a continuous trajectory with many points. The points are just representative (ideally, informative) milestones (or markers) along a continuous path. The customer’s experience is a reality that is greater than a collection of data points.

The customer doesn’t care about our organizational structure or data silos. So, CX measurement is critically (and holistically) important within and across all of the various digital and non-digital channels through which we engage with our customers in their journey with us. If we get this wrong, the consequences of poor CX could be disastrous, including revenue loss, customer churn, or negative sentiment broadcasted publicly on social media. So, the more points you have to help you understand a customer’s experience, the more insights that you will gain from it, and consequently the more likely you will be able to optimize the CX.

3. Embracing the Possibility of Failure

As my last point, I’d like to remind data scientists to look past their daily pressures and remember not to fear the chance of failure – or as the Michael Jordan liked to say, “You miss 100% of the shots you don’t take.” We are of course always gunning for greater efficiency and optimization, but sometimes you cannot get there by taking only slow, conservative steps. You may not get there at all if you don’t take a shot at it.

Brilliant ideas in the world of data science, or anywhere else in life, are useless without a goal and taking a shot at that goal. Your goal can take many forms: discover something new (in science); make a better decision (in government); take a more effective course of action (in business); provide a better experience (for your customers); deliver a better diagnosis (in medicine), etc. In any case, if you don’t try it (even if you fail), you haven’t gained anything from your big data analytics investments. I prefer to define ROI as “return on innovation”.

In other words, have you taken your shots at new ideas, new products, new customers, new markets, new understandings, or new models? Have you scored on some of those shots? Can you point to innovations that have come from your efforts? Of course, we care about how much is spent on failed efforts. However, it is not the percentage of successes that matter, but the existence of successes. Measure them, report them, improve upon them, keep taking shots, improve your analytics “game”, tell the story of your lessons learned (i.e., those beneficial failures), and deliver the ROI that really matters.

It’s not always easy navigating unchartered waters. But big data analytics are now providing ample opportunities for corporate leaders to do exactly that. Our increasing capacity to serve leaders, customers and ourselves through data science will play a defining role in ensuring that those opportunities are never squandered.

 

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