The data analytic buzz is everywhere these days. The pressure for companies to increasingly look inward for better insights and forecasting has a lot of companies jumping before knowing where they’ll land.
Companies frustrated by their analytic efforts usually find their struggles fall within one or more areas.
Misaligned Business and Analytics Goals
The most important thing is having overall strategic goals the business is collectively contributing toward. But when departments collect and consume data autonomously, they can easily lose sight of the overall picture. The change, of course, needs to come from leadership.
Internal departments shouldn’t be expected to independently develop metrics and collect data tailored to the big-picture strategy without instruction and guidance from executives.
Sharp Insights but Slow (or No) Action
When analytics findings are strong but follow-through is poor, it’s usually because data hasn’t been properly integrated into each department’s workflow. After all, it’s one thing to know what needs fixing and it’s quite another to actually execute.
When teams are trained, and processes developed to not only consume data and form insights, but smoothly transition into implementing those insights, that is when data starts to actually work for the company. And because even data-driven decisions aren’t foolproof, those too should be measured for effectiveness.
Unreliable Data
As powerful as data can be to develop insights and make better decisions, the numbers need to be reliable for the findings to be worthwhile. Even if business goals and processes for interacting with data are aligned, decision-making can be difficult.
Worse, as hard as it can be to get all employees to integrate data into their workflows, it’ll be even harder if employees perceive data as untrustworthy. Data can become more reliable by consolidating data collected from multiple channels (and for multiple reasons) into one overall stack. This avoids redundancies in data and enables departments to work off the same information.
Unclear (or Non-Existent) Vision
A different problem but similar outcome to analytic efforts being misaligned with business goals happens when a company doesn’t have an overall vision. With no vision of what to focus on and achieve, a pile of data is at best unclear and at worst completely useless.
Companies need to have a big-picture strategy of what to focus on before data collection methods are talked about. With clear goals and focus areas set, the types of data to collect – and how to collect them – will become clearer.
Not Enough Bandwidth, Expertise to Analyze Data
Maybe data quality is high and the information is shedding light on how to inch closer to long-term goals. Yet, findings are slow, and decisions delayed. This probably means staff data analysts are drowning in report requests. This impedance hurts, because everything’s being done right, except for having a system to process the data into clear takeaways.
One way this can be solved is by leveraging data visualization tools that automatically dig through multiple rows of data to deliver insights via charts, graphs and maps. This frees up the data team to focus on data quality and data process improvements instead of burying their heads in the sand all day to answer each department’s various requests.
How to Transform Business Analytic Efforts
Transforming analytic processes and efforts across a company isn’t an easy or straightforward task. Company culture needs to shift to believing in data and how it can clarify the next move. This can only happen though when focused business goals are developed and analytic strategies created to fuel those efforts.
And even then, some kind of deep learning software or a dedicated team of data analysts are needed to sort through the information. If companies are going to be data-driven, they need to let the data drive them. That’ll never happen when their efforts are stuck in neutral.
