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How to Fill Your Analytics Talent Gap

By Vlad Gorlov
September 23, 2015

This article first appeared on DataInformed. See the original post here.

In the last half decade, a talent gap has caused problems for executives looking to utilize data in a meaningful way. Companies are in dire need of people with the skills to collate data, interpret the results and translate them into actionable strategic insights that have a direct impact on the company’s business objectives. Yet, fewer and fewer professionals have the multi-disciplinary skills needed to fill such a role.

So, how does an executive go about filling this gap amid a dwindling talent pool?

The first step is to define the goals and scope of what the role would be expected to accomplish, but at a technical level. The ideal candidate would be able to seamlessly bridge the gap between data analysis and business strategy. Consequently, the role’s manifestation – be it through technology or multiple experts – would be able to recognize the importance of the data in a real-world setting, moving back and forth between education and real-world experiences toward the company’s goals.

The process of forming the direction for the role can be narrowed even further, to a couple of questions: What sort of data does your company have currently? What data is required for progress in the future? If you can determine the baseline and where you want to be, the options for filling the gap and achieving your goals become much clearer.

The best answer for long-term success is building the infrastructure that allows your organization to succeed without a multi-disciplinary analyst but also entices such an analyst to join down the road.

Here are four areas to emphasize when creating the foundation to grow your data analysis department:


Without innovative, user-friendly data analysis software, this project becomes all but impossible. If your timeline is pressed, there are stopgaps that can help you get by for a while. Certain technological packages provide analyses, visuals, and custom manipulation capabilities that will suffice in the absence of a multi-disciplinary professional. However, it is crucial to note that when analyses come prepackaged, they cut corners in the background that you may not notice. This is a risky proposition but, in a pinch, it is possible to succeed.

A better option would be to invest time and money in software that builds the system from the ground up and establishes a fluid organization for your current data professionals. As with determining the scope of a data analyst role, as mentioned above, define the goals for the software: Increased margins? Deeper analysis of competitive data? Improved profitability for deals completed by your sales team? If you are able to focus in on certain company objectives, your search for the software to help realize them will be much smoother.

With these points checked off on your list, the final piece of the puzzle is familiarity. Because your organization lacks a single expert to fill the position, you must disperse responsibilities among several employees. As a result, the technology must be familiar enough for people with a variety of skill sets to be able to use.

Position Integration

 In the early 2000s, data scientists and analysts were valued and utilized in a manner that directly influenced business practices. Once the recession hit, their importance was devalued and many were deemed replaceable. Since then, there has been an ever-growing investment in data, with nearly 72 percent of businesses increasing spending on analytics in 2012 alone. Accordingly, there is a large volume of data without anyone skilled enough to collate and interpret it.

In order for the group of analysts using your new software to have an impact, their department must be involved with higher-level business operations. Establish a firm position for the data analysis department in your company to confirm that they are vital to multiple branches of your organization, particularly the finance, sales, and pricing science or marketing departments. If these experts can integrate themselves into the workflow of the different drivers in your organization, their insights can be utilized more effectively toward improving the bottom line.

Skill Development Programs

Countless companies have initiated mentorship and development programs for in-house talent, only to abandon the projects when results are not immediately apparent or when schedules are too tight. Much like the implementation of the analytics software, programs like these require significant time and financial investments.

With the proper investment – in particular with regards to time and energy – development programs can be successful in creating a fully functioning analytics department. Because you have dispersed the roles to experts in certain areas of analysis – whether they specialize in business strategies or data analytics – they now will have the option of acquiring new skills on the other side of the fence, thus filling the talent gap within your own organization.

Perhaps most importantly, these newly developed experts will have the backing of an organization that built a solid foundation for their success. They will have the technology to gather and interpret the desired data points and they will have the standing within the company to know that their decisions are impacting business performance.

Protect Yourself with Infrastructure

To guard against external forces, such as the scarcity of talent, the above infrastructure does wonders in placing your organization at the forefront of analytical success and creating an environment that entices future multi-disciplinary professionals.

With the infrastructure in place, your organization will be prepared to capitalize on its current data to set it apart from the industry and cement it as the standard for where the industry is going.

  • Analytics , data , DataInformed , executives , skill development , talent gap , technology

    Vlad Gorlov

    Vlad Gorlov is Director of Pricing Science at Vendavo. In this role, he is responsible for pricing science implementation projects, pre-sales efforts, as well as product and methodology development. Prior to joining Vendavo, Vlad was Modeling Practice Leader at Nomis Solutions, where he oversaw analytical work on pricing optimization engagements across North America and globally. Prior to Nomis, Vlad held a number of positions in pricing, marketing optimization, and customer analytics. Vlad holds an MA in Economics from Yale University and an MS in mathematics, and has over 15 years of experience in pricing, mathematical modeling, and advanced analytics across numerous industries.