Data Management Rules for Analytics

With analytics taking a central role in most companies’ daily operations, managing the massive data streams organizations create is more important than ever. Effective business intelligence is the product of data that is scrubbed, properly stored, and easy to find. When your organization uses raw data without proper management procedures, your results suffer.

The first step towards creating better data for analytics starts with managing data the right way. Establishing clear protocols and following them can help streamline the analytics process, offer better insights, and simplify the process of handling data. You can start by implementing these five rules to manage your data more efficiently.

As the amount of data produced by organizations daily grows exponentially, sorting through terabytes of information can become problematic and reduce the efficiency of analytics. Such large data sets require significantly longer times to scrub and properly organize. For companies that deal with multiple streams that exhibit heavy bandwidth, having a clear line of sight towards business and analytics goals can help reduce inflows and prioritize relevant data.

It’s important to establish clear objectives for data and create parameters that filter out data points that are irrelevant or unclear. This facilitates pre-screening datasets and makes scrubbing and sorting easier by reducing white noise. Additionally, you can focus even more on measuring specific KPIs to further filter out the right data from the stream.

Another problem analytics suites face is reconciling disparate data from multiple streams. Organizations have internal, third-party, customer, and other data that must be considered as part of a larger whole instead of viewed in isolation. Leaving data as-is can be damaging to insights, as different sources may use unique formats or different styles.

Before allowing multiple streams to connect to your data analytics software, your first step should be establishing a process to collect data more centrally and unify it. This centralization makes it easier to input data seamlessly into analytics tools but also simplifies the methodology for users to find and manipulate data. Consider how to set up your data streams best to reduce the number of sources to eventually produce more unified sets.

The endless stream of data raises questions about quality and quantity.