The following is the second in a series of four blog posts on the evolving roles, skills and functions played by business intelligence and data professionals.
We learned in our first post in this series how the role of business intelligence (BI) professional is rapidly evolving, thanks to the recent explosion in data, new data types and self-serve analytics.
Emerging job titles such as data scientist, data architect, data wrangler and other data-heavy roles — with a much wider range of required skills than traditional BI reporting — now dominate the landscape. And rightfully so: it’s well documented that companies that embrace data and data analytics are able to find efficiencies and implement strategies they otherwise wouldn’t.
As the recent LinkedIn Emerging Jobs Report states, demand for these jobs coming from technology and non-technology firms alike, is high as companies in various verticals realize the value of data and analytics.
And the skill sets of traditional BI professionals, which have typically skewed towards SQL scripting skills and after-the-fact, static report creation, simply can’t handle the data demands of today.
How can you attract the best next-gen BI workers — and keep them?
Data science is hot among recruiters and companies, but it’s not necessarily a prerequisite right away: organizations should first ask themselves if they really need (or are even ready for) a top-tier data professional. After all, if you don’t have the right processes and company culture in place to begin with, it could be a wasted hire.
And since there still isn’t the critical mass of potential employees necessary to fill these job market demands, it’s a sellers’ market when it comes to attracting the best data workers.
But if you’ve identified a need to hire (and retain) a top-tier data professional in your organization, there are a few good rules of thumb to keep in mind:
- Research LinkedIn for similar job postings to build out the most accurate job description possible, including (if applicable) trending skills and backgrounds such as machine learning, the ability to work with large datasets, natural language processing and statistical modelling, along with softer skills such as creativity, communications and the ability to interact with executives and business leaders. Allow your data professionals to push the boundaries with forward-looking projects, be inventive and use their curiosity and creativity. If you don’t, there’s a good chance they’ll get bored and leave.
- Provide your data professionals access to the C-suite: it’s imperative that data scientists are given the freedom to consult with senior management at all stages of their projects. Not only do most good data professionals crave access to senior decision-makers, but their projects could also go off the rails if they don’t get this kind of valuable feedback.
- Allow them to work on more than just data: Most data professionals can (and want to) provide insights on the overall business. As the Kellogg School’s Eric Leininger says: “A great data scientist who has built up a tremendous reputation for uncovering great new truth can also be seen as a bit of an oracle whose opinions can be heard as facts.”
- Allow for cross-training: Allowing your data and business intelligence professionals to train for and learn about other analytics-based roles, such as digital marketing and operations management, can signal potential career progression along with imparting a greater understanding of the overall business.
- Help develop their business acumen: Most employees want to impress the C-suite, and data professionals are no different. Make sure to develop their business knowledge, so they can also talk the talk along with walking the walk.
Taking advantage of the changing role of BI
Adding data scientists, data architects and other similar roles to your organization can add tremendous value if implemented correctly. Next-gen BI professionals can empower everyone in your organization to make better decisions based on data-driven insights, including:
- Challenging staff to follow best practices
- Empowering fellow employees to understand and analyze their own data
- Identifying opportunities that otherwise go unnoticed
- Identifying and refining target audiences
- Identifying trends and setting goals, either for the entire company, for groups, or individuals
- Updating your data architecture to be more efficient and analytics-friendly
But it’s also important to realize that the perfect BI candidate probably isn’t out there (and if they are, they’re also in impossibly high demand). In part three of our series, we’ll look at that perfect candidate — and explore the importance of having a data team, either internal or external, at your disposal.