How to Bend, Not Break, Under Today’s Analytic Demands

How to Bend, Not Break, Under Today’s Analytic Demands

As Dun & Bradstreet’s analytics experts collaborate with data scientists and analysts in companies worldwide, we’re hearing about crucial needs for significantly compressed timelines around digital transformation, for extreme pivots on business models and delivery channels, and for abrupt shifts in target segmentation. Those needs are compelling organizations to employ a new type of analytical prowess known as analytic flexibility.

What Is Analytic Flexibility?

Analytic flexibility is most simply defined as the ability to easily change how you are approaching analysis or insight creation. You may hear it cloaked in other terms, like “we need to be more analytically savvy” or “we need to use AI and ML more effectively,” but it all boils down to the idea that you want (or have) the capacity to stretch and do more when needed.

Businesses that take this approach have found to ways to reduce time spent identifying, preparing, combining, and evaluating data; instead, they are spending more time on creating the insights that help them innovate, expand, and solve problems as trends emerge and morph over time. Or in other words, just like the agile methodology of their product peers, analytically flexible insights teams can test, learn, and pivot in a dynamic feedback loop.

How Analytic Flexibility Can Help You

To determine if you could flex a bit more, we’ve compiled a list of five hurdles that analytic flexibility can help you overcome.

Hurdle 1: You need to do more or pivot faster

Those who are analytically flexible arm their teams with tools built to help create an end-to-end process -- from data ingestion, alignment, and modeling, to testing, operationalization, and revalidation. In turn, output increases because teams can focus on high-value pursuits that drive results. It is this adaptive nature that lets teams pivot in a flash to deal with sudden changes impacting their strategy, company, or market. It also means that they aren’t stuck going in one direction, following one strategy, or completing a project just because they started it.

Hurdle 2: You don’t have a large enough team

Would you believe us if we said that was okay? Analytic flexibility doesn’t rely on any number of team members. It relies on the competencies of efficiency and adaptability. To encourage this, leaders look to automate the repeatable things so that their smartest minds don’t get bogged down with administrative work.

Hurdle 3: You have too much data and too few insights

You know that you need the ability to look outside in, as well as inside out. Getting those views can require using a myriad of internal and external data sources. But which do you use when? Those that are analytically flexible embrace all of the data, experimenting across millions of data points to pick and choose the perfect combination of data elements, features, and sets that will result in useful insights. How do they do it? It's a combination of providing ready access to large data sets AND giving their team an easy way to bring disparate data sets together. It sounds so basic, yet time and time again we hear of data scientists spending hours of their time identifying, preparing, and assimilating data. The big picture shouldn’t be a big headache.

Hurdle 4: You’re not creating breakthroughs

To be analytically flexible, data science and analyst teams need room to experiment — a place to try out new ideas, a place to succeed and fail. If only allowed to work in a live production environment, you risk stunting innovation or disrupting key business processes and operations. Either of these could bring serious consequences to your company or you.

Hurdle 5: You don’t know where or how to start

One of the definitions of flexibility is “the willingness to change or compromise.” Those who are analytically flexible don’t try to do it all themselves. They collaborate and use external help in the areas where they fall short. This might mean getting experts to help develop or evaluate a new model or analysis. For some workgroups, this may mean using a prebuilt environment instead of standing up their own. For others, it could mean temporary staff augmentation. In all cases, analytic flexibility allowed them to push their analytics strategy forward despite shortcomings.

Change Will Come Again

Commercial organizations across the globe are still reeling from the crises of the past year. But the rusty adage is true: the only constant is change. Practicing analytic flexibility is one of the most important things companies can do to stay vigilant and understand their options in multiple scenarios, whether good or bad.

D&B Analytics Studio helps organizations like yours ensure they have the support pillars they need to become more analytically flexible – comprehensive B2B and third-party data, robust analysis tools for combining data sets and modeling, and a secure platform where you can play, test and learn safely. For more details on D&B Analytics Studio, or to discuss your particular needs with our experts, contact us today.