Becoming a data-driven org:
The data skills your company needs in 2017

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New year, new you technology. To kick off 2017 right, we’re here to enlighten you on all the technology trends, disruptors and themes this next trip around the sun will bring. (Check out what happened in 2016 here.) Each week this January, we’ll share a new blog post from our experts about what to expect in 2017 and how you and your business can prepare. Happy New Year! 

Which data skills do you think are most important this year? Let us know on Twitter with #TechIn2017. 

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Cause and effect—it’s one of the overarching themes in data. And one of the main reasons your organization wants data. They want to determine the true cause of an effect to make better-informed, more strategic business decisions. Don’t we all? 

However, when we talk about causality, I’d invite you to think bigger. While causality is defined as identifying what’s driving an outcome, if we think about how things really occur in an organization–in your organization–the determination of true causality is vastly more difficult than it is usually assumed to be. Those causalities need to be thought of as iterative, highly technical and ongoing processes. In business especially, it’s extremely rare to be able to plainly say that A causes B. More often, there are dozens of IF/THEN contingencies between A and B—and it’s those factors and variables that are best fleshed out by skilled data scientists. It’s why your business needs a data team—to help make sense of these causes and effects that directly tie to your business’ bottom line and determine what data your company actually needs. So which data skills and roles should you have within your company today? 

Data skills: What your business needs

Data skillset one: Ability to analyze data
One stumbling block for companies trying to become data-centered is that ubiquity of data requires two distinct skillsets, in order to truly leverage your data assets. The first is the ability to analyze data. It’s important to note that data analysis is NOT data visualization. Data visualization is the last step in the process of analysis. Your analysts need to be able to acquire, manipulate, blend, query and store data before they even get to the visualization of it. These prerequisites are routinely underestimated and undersold by data visualization software companies who are looking to make everyone an analyst overnight.

Data skillset two: Adding meaning to those analytics and communicating it 
The second skillset that is absolutely necessary is the ability to consume whatever data products, reports, or dashboards your analysts and data scientists create for your business users. Contrary to popular belief, the ability to consume analytics is not a given, even for those who are mathematically inclined. Understanding the meaning in your data is contingent upon your people being able to interpret analyses and visualizations accurately. Visualization illiteracy is a real thing, and it’s a product of poor communication skills (on the part of the viz creator), unfamiliarity with data visualizations and a lack of time and bandwidth for those who are consuming the data. There is only so much time in a workday. Your employees cannot and will not spend extra time deconstructing a poor data visualization and/or analysis.

Why you should have a standalone data team or data department  

Once your people acquire the requisite skills and mindset to analyze data and make decisions based on those analyses, start to think about maximizing your organization’s ability to be data-centered. From a very practical perspective, there are specific actions an org can take to make data part of its day-to-day operations. Perhaps most important is to think of data as a domain of its own. Just like you’d consider finance or marketing or HR as a specific team made up of people with specific skillsets, you should consider data in the same manner. The complexity, omnipresence and importance of data requires a team of skilled, experienced data geeks who provide a foundation for any organization that strives to be data-centric. So, what does this team of data geeks look like? 

4 data positions your business should have (or be hiring for in 2017)  

Typically, a data team should consist of four roles: 

  • Data Engineers - They acquire, move and stage data for the business to analyze.
  • Analytics Engineers - Their responsibilities range from building ad hoc data pipelines to writing complex queries to building compelling data visualizations. 
  • Data Scientists - They should inform high-level strategic decision making
  • Data Solutions Delivery - Individual(s) who are able to tie all of the technical work into business context. 

It’s especially important to note that this last role should be responsible for leading and promoting a data-centered culture across the organization, which includes training and mentorship for the business. They should also be the primary data steward for all the business critical data so that your organization has a level of confidence that their data is accurate, relevant and timely. 

Regardless of the particular role, everyone on your data team should be skilled in interviewing, detective work and other attributes that help gather seamless business requirements for reports, datasets, dashboards and other artifacts. They should all have the ability to speak two languages (business and technology). While you can’t hire a unicorn employee, your data employees should communicate well and be able to give top-notch presentations that others outside of the data department can understand. And most importantly, their leader–whether it be a VP of Data, Chief Data Officer or something similar–should have a seat at your C-level meetings. Elevating your data team to a leadership level and having someone there to represent data for the org is crucial to becoming a data-driven business. 

Make 2017 the year your business becomes data-driven 

Becoming a data-centered organization is important for a whole host of reasons, but primarily it allows your people to make decisions that are informed by empirical judgment that is focused on the truth, and not on subjective conjecture. As mentioned above, becoming data-driven and data-centric is not an easy task. It involves a heavy commitment from both a strategic as well as practical perspective. However, as with most any investment in organizational infrastructure, the gains will far surpass the costs—and you’ll probably have the data to back it up. 

Learn more about the other tech trends your organization should be paying attention to in 2017 here.

Contributor

Bill Saltmarsh

has a passion for bridging the gap between data curiosity and data fluency. His interests and experiences range from writing queries that wrangle and transform data to creating functional and aesthetically pleasing visualizations. As a member of the topwallpaper data team, data analytics is the medium by which he provides others with greater meaning in their work.