Analytics job one: Closing the data-to-decisions gap

Are you marketing to the right customer? Is your product priced correctly? Have you found a more preemptive way to detect fraud? Everywhere you turn, data and analytics sit at the heart of answering such common but still vexing problems.

But it’s the word data that seems to garner most of the attention, in part because of anecdotes like these: Experts predict that there will be a 4,300 percent increase in annual data generation by 2020, representing close to 40 zettabytes of data, which IDC found to be the equivalent of 57 times greater than all the grains of sand on all the earth’s beaches [1, 2].

When my firm conducts decision science workshops, in the lead-up to each event we survey participants and ask a simple open-ended question: “What’s your top challenge with respect to data and analytics?”

Two types of responses emerge: concerns over data and concerns over analysis and decision-making, but with the former outweighing the latter by more than 2 to 1.

Your background, be it from IT, marketing or a new analytics team, will affect your priorities. But across these disciplines and across most companies where we’ve worked, I see so many who believe that data must be completely buttoned up before it can be used to make informed decisions. These days that mindset can lead to costly delays and misallocated investments.

We get it. Data has exploded, and companies must still dedicate a lot of resources to managing it. But data can no longer trump decisions. You need to treat them as equals. Especially if you play a role in coordinating your company’s data and analytics efforts, consider doing the following:

1. Balance data creation with insights consumption.

We now know that data is proliferating at a rate that is impossible to keep up with. Production of data is outpacing our ability to use it, although not for a lack of trying. The avalanche of data has led many top-level execs and the people working under them to view analytics tools as a way to keep up. The thinking is that by creating more metrics, more dashboards, more visualizations and more reports, you can yield more insights.

However, these “insights” may not all say the same thing or they can overload you with too much information, including much that is irrelevant. For example, you can analyze click rates in 15 different ways, but that does not necessarily help you understand why people click and why they don’t. This chaos makes it difficult for business leaders to make smart, data-driven decisions vs. ones that are based more on gut feel. You could look to America’s costly drug war over the past 40 years as another piece of evidence that suggests how vast resources aren’t always arrayed in a manner that addresses the heart of a problem.

Every business is home to thousands of decision supply chains that include identifying a problem, gathering data, generating and evangelizing insights, and then taking decisions on those insights. As an analytics leader, it’s your job to formalize this supply chain and inject transparency with a focus on measurable business outcomes. So before creating the next flavor of dashboard, obsess over who and how the resulting insights will be used. This helps keep analytics overload at bay and prevents you from being at the mercy of hundreds of separate decision-makers.

Table 1: Top data and analytics challenges (N=150 Mu Sigma clients and prospects).

2. Focus your team on asking great questions more than finding fast answers.

Your team is expected to quickly provide accurate answers when presented with questions. How many leads resulted from this campaign? What specific actions did visitors take on the site before becoming customers? How will the introduction of a new product affect our service quality? When faced with a barrage of questions in a high-pressure situation, it is human nature to blurt out a response. No one likes to seem ill-informed or ignorant.

Rather than focusing on answers (which may end up wrong anyway), the better approach is to slow down and before answering, make sure the questions themselves are right. This is particularly important when dealing with data because one can always manipulate and interpret data to yield an answer that supports a particular position. Mu Sigma’s founder calls this “choking the data.”

The key to really unlocking the value in your data is to ask the right questions. This may involve stepping back and taking a more expansive view of the problem rather than keeping it narrowly bounded. For example, exploring if you might have a broader customer experience problem rather than a pricing problem with a particular SKU. You may have to challenge basic assumptions and return to a first principles point of view, so that you can be confident in your answers.

3. Teach the organization how to fish; don’t just give ’em the fish.

You may be under constant pressure to respond to hordes of problem requests. As a result, your teams dedicate so much time to call-and-response that they do not find the time needed to develop their analytical skills and put their creativity to use in a more proactive manner. One remedy that I’ve witnessed is to begin by allocating two-thirds of a team’s efforts to reacting to inbound problems and the rest to more proactive capability building – but over time, flipping that ratio.

Here are some core tenets of quality problem-solving, each of which could require a fisherman’s guide unto its own. For one, always begin with measurable outcomes in mind and chart the behavioral changes required to drive those outcomes. Next, make sure you approach problems in a consistent, meta-data driven manner. Third, map and understand the interactions between business problems. Finally, integrate different forms of analytics – descriptive, inquisitive, predictive and prescriptive – into each business problem, to avoid blinkered views.

4. Empower the organization to analyze data and contribute to the greater knowledge pool.

You can’t be everywhere at once. Similarly, there is no one governing model for analytics that helps one group streamline all the information necessary to make the right decisions. In fact, governance of analytics can be a confusing, intricate mess, and it is now tangled up with information governance and sometimes data governance as well. Across the board, most governance focuses on lowering risk through decision rights, policies and standards, and this makes sense given the multifold threats inherent in data.

So what’s the solution? Look no further than the “Citizen Analyst,” a term coined by Gartner and IBM, which has been used to define someone who may not have a formal background in data science, but uses the wealth of available informational resources and tools to conduct analyses on their own.

You can use these citizens to your advantage by empowering them with resources they need to do their jobs better. They may be able to create new Tableau cockpits, hire their own data scientists or procure their own modeling tools. As an analytics leader, you can’t beat them, so you’d best join them by offering tools or services, including flexible capacity, and helping them secure funding or providing training and advice for their own problem-solving work.

5. Don’t allow your focus on technology to undermine the culture you’ve fought to create.

Those who stay mired in the tangible worlds of data, software and infrastructure can easily lose touch with important intangibles like company culture. As an analytics leader you must balance your focus on technology with a focus on culture. The journey from data to decision science requires introspection. Your team will perform at its best and receive the same from others when it adopts a learning mindset that allows employees to capitalize on change rather than trying to manage or react to it.

It’s also important to be open to experimentation and encourage team members to fail fast and often (but cheaply).In addition, be sure to incorporate a tight feedback loop into your analytics work, and focus on the art of problem-solving rather than a never-ending list of projects. You’ll find this helps inspire a mix of creativity and efficiency.

Table 2: Five Parallel Work Streams.

These are just a few ideas that can help you bridge the gap between efforts to seize control of the data deluge in your organization, and spending more time on better decisions and business outcomes. Table 2 provides a short summary of the parallel threads that you’ll need to sustain. Do you have other ideas or interesting tactics to share? I’d love to hear them.

References

1. CSC: http://www.csc.com/insights/flxwd/78931-big_data_universe_beginning_to_explode
2. IDC: https://www.emc.com/collateral/analyst-reports/idc-digital-universe-united-states.pdf