Many businesses today know the importance of big data. They have identified KPIs, set up a data warehouse, and collect more information than they know what to do with. And therein lies the problem.

While data collection is important, it is only useful if your company knows how to apply it. Yet big data application is something many businesses continue to struggle with.

The solution? A data strategy.

A good data strategy looks at all parts of the big data process — from collection to storage to security to application and beyond. It denotes what data is collected, who collects it, where and how it is stored, how analysis is performed, and more. Essentially, it breaks down every part of the data process.

When you are looking at your strategy to improve data application, you will want to pay particular attention to these components:

1. Duplicate Data and Processing Overlaps

Duplicate data can be the bane of an analyst’s existence. You will want to make sure that your system is set up in a way that quickly identifies duplicate data that could end up skewing the results.

Processing overlaps can happen when multiple users are accessing and analyzing the same data and starting fresh every time. When data projects are looked at as one-off, from-scratch endeavors, this can happen very easily. But not only can lead to inaccuracies, it can also waste staff time.

You might consider having pre-made data reports available or treating data reports like other business support activities, with one department responsible for creating them.

2. Automated Data Analysis

Another way to make data more actionable is by automating the analysis. Certain software can do this part of the process for you, turning specified data into visual elements, like charts, bar graphs, pie graphs, etc.

Studies show that the human mind processes visual information much more quickly than written information. If, for example, you have your program set to automatically turn sales figures into a pie chart, a sales manager would just be able to sit down and look at the chart — much quicker than scanning through rows of numbers.

3. Set Actionable Targets

Results come easiest when you know what you are looking for.

The best data analysis is going to happen when you know what you are collecting and why you are collecting it. Is it important to track how many times the same customer calls in? Potentially — if you want to know how much time a service rep is spending on existing clients vs. new prospects. But if it is not important and not going to be analyzed, then why are you tracking it?

Consider setting strategic goals that you can use data to meet. Keep the list on the shorter side — normally five-to-ten goals is recommended, but that could vary depending on your business. You also might want to set larger company-wide targets and smaller departmental or even project-related markers.

4. Build Analysis into Your Workflow

Unfortunately, data is not going to analyze itself (even with automation). To make the most of your data collection, you need to make it a priority.

If you are setting a data collection workflow, assign tasks for analysis and build it into your schedule. For instance, if you are creating a budgeting workflow, leave room in the process for analysis. Fortunately, if you are using automation software, it can take care of most of the data entry, leaving more time for review.

5. Focus on More Than What Has Already Been Done

While data analysis is great for reviewing the past, it can also be used in the forecasting process. Predictive analytics can help plan for the future, which can allow you to take actionable steps and apply the data in ways that get you there.

True Sky’s corporate performance management software automates financial data collection and analysis so you can focus more on the big data applications. Automatically create dashboards and data visualizations, identify duplicate data, and more.

Contact us today to find out how we can help. Call 1 855 878 3759 or visit www.truesky.com.