When is the right time for real-time analytics?

November 10, 2015

Real-time reporting and analytics is no longer a bonus feature for BI platforms; it’s a necessity. But when is the right time for real-time?
So you’ve implemented a business intelligence strategy and are seeing the benefits of operational BI.  This is stage one on the journey to becoming a fully data-driven organization. As the organization grows in analytics maturity and begins to expand its ambitions to ever-broader data sets, the volume of structured and unstructured data available for analytical use increases, often exponentially.

Now is when it’s time to ask yourself, when is it the right time to incorporate real-time analytics to your BI strategy?

Because truly, an organization cannot be fully data driven without data that is up-to-date, often times by the minute, to ensure the most accurate representation of the company at the moment decisions need to be made. At TARGIT, we consider this shift an important stage on that BI journey.

Real-time analytics make it possible to not only use data reactively—to look back on past performance and inform corrective action—but proactively to make real-time operational adjustments. The use of real-time analytics also cultivates the adoption of BI throughout the company.

Real-time data models create significant business intelligence opportunities in departments that might not regularly otherwise utilize BI, such as the Sales department who now have instant insight into their customers’ needs—often before they know these needs themselves—and can anticipate orders before they’re even placed.

A word of caution on data quality

Access to information right as they need it to perform their work means all business areas can become data-driven and work smarter. As this type of data becomes so vital for daily routines though, the quality becomes increasingly important. As with any data governance strategy, data must tested and validated before it is folded into the overall BI practice.

With real-time data, this is hard to do because there is no time to clean the data before it makes its way to the users. That’s why it’s crucial to write queries (views for the data model) that can handle data issues such as missing values, missing records, calculations that are intelligent enough to couple with outliers, and other data entry errors.

Having a well-structured data warehouse is still the recommended approach for the majority of data consumers of BI solutions. This is where we pull out financial reports, historical sales overviews, and inventory trends. But for areas such as open sales, purchases, production orders, or project management, accounting, and daily sales activity, having a real-time model can be critical. 

Building an entire solution as a real-time model would not be wise from a performance or data quality point of view. Instead, introduce smaller areas of data that can benefit from being real-time and go from there.

This is a new era of BI; one that should be embraced but handled with care. With the right strategy, real-time analytics introduces more Action Loops to more people throughout the organiztion, fostering an environment of faster, better data-driven decisions.

Ready to incorporate real-time analytics into your TARGIT practice? I just wrote a whitepaper that shows you how to do it step by step.

Download the whitepaper today.

Real Time Analytics