The digital revolution has not only made data more abundant; it has made it much more accessible. Previously managers would require a dedicated analyst (or IT staff) on payroll just to get a clear idea of how their company is performing in numbers. This was due to the information being difficult to extract, or requiring proprietary skills to understand and make it presentable enough for ‘data laymen’ to be able to grasp. Hence the data analyst was needed in order to help the company make sense of its data.

However, business intelligence software has changed all of this: Suddenly, data is not the sole possession of organizational gatekeepers, or those with the authority to order reports from them. Suddenly any business executive can connect to her data, build a dashboard and see the stats that are driving the business in real-time.

So what does this mean for the data analysts? Has their job been made redundant? Or to put it in more provocative terms — is it time to fire your data analyst? Spoiler alert: the answer is no. Self-service business intelligence software lets business executives do a lot on their own: explore data, identify trends and monitor KPIs. But at the same time, the data analyst’s role has become more crucial than ever: your company needs him or her to prepare the data for analysis, as well as for more advanced statistical analysis and data modeling. For these tasks, you’re still going to need a professional on board.

DIY Data Discovery for the Masses

First, let’s look at what BI software does let do yourself, even without any background in data analysis, statistics or computer science. These are the functions that should essentially be considered ‘plug-and-play’ in any reasonable BI software tool (on a sidenote, if you have a BI system in your company and still need IT to accomplish these tasks, well – it might be time to look for a new one):

  • Getting an overview of your business: BI software lets you see what’s going on in your organization, both from a bird’s-eye view and in detail. It gives you a single place to see all your data and use it to uncover trends and overall developments. Creating this type of report would have previously required someone who could mold the data into a useable form, but this is no longer the case.
  • Tracking KPIs: Non-technical users can monitor metrics against predefined goals in real-time, while previously the analyst or IT professional would have to perform these calculations manually.
  • Identifying strengths and weaknesses. Having one centralized repository and view of the data allows executives to understand which departments or areas within the company are performing well, and which less so, and take action accordingly in real-time.
  • Data discovery: unlike the static report, BI dashboards let you drill into the data and see it up-close and personal, as well as search for connections between different business processes. Again, this could not be accomplished previously without the help of someone who knew how to “speak” with the data and its various repositories – but as the software now does this on its own, this functionality has become part of the business user’s realm.

The fact that non-techies have the ability to perform all these tasks by themselves is no small feat, and would have seemed almost unimaginable a few years ago. However, there’s a reason we’re not seeing troves of data analysts in the unemployment lines (in fact, quite the opposite is true…

Why You Still Need Your Data Analyst

Despite the advancements in business intelligence technology, there is no replacement for a skilled data analyst when it comes to the more heavy-duty aspects of data analysis and to truly harness the full power of the data your organization possesses. These are tasks that cannot be completely automated, such as:

  • Data preparation. Before data can be analyzed, it has to first be cleansed, integrated and normalized — especially when data is coming from multiple and highly disparate sources. While BI software should allow business users to perform simple joins without intricate knowledge of SQL, more complex source data will often require more work before it’s ready to go. The dedicated analyst, who knows the ins and outs of the data and its underlying structure, is the perfect man (or woman) for the job.
  • Managing complex data models: When dealing with Big Data, especially data that is generated not just inside your business but also from external sources, you cannot simply feed it into the system and hope for the best. You need someone to separate the wheat from the chaff and find ways to connect these vast datasets in a useful and efficient way, and here the human factor will once again come into play.
  • Advanced predictive analytics: Beyond understanding what your business has been doing so far, analyzing data can give you a hint at the possibilities the future holds. This requires knowledge of statistics, as well as an intricate familiarity with the business itself and the various factors that affect it. Modern software might be smart, but it is not nearly smart or creative enough to accomplish this without human intervention.
  • Effectively communicating results: When dealing with complex data and statistical analyses, you need to know not only how to reach the insights, but also how to communicate them in such way that they will be easily digestible and understandable to everyone in the company. The data analyst is needed to turn these complex concepts into clear presentations with actionable takeaways.

So don’t fire your data analyst just yet. In fact, you might consider giving him or her a raise – BI software will spare them the grunt work, and give them the opportunity to handle much more advanced (and interesting) tasks.