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Is Your Data Good Enough for Business Intelligence Decisions?

May 23, 2019



There’s no question that more and more enterprises are employing analytics tools to help in their strategic business intelligence decisions. But there’s a problem - not all source data is of a high quality.

Poor-quality data likely can’t be validated and labelled, and more importantly, organisations can’t derive any actionable, reliable insights from it.

So how can you be confident your source data is not only accurate, but able to inform your business intelligence decisions? It starts with high-quality software.


Finding the right software for business intelligence

There are numerous business intelligence services on the market, but many enterprises are finding value in IBM solutions. 

IBM’s TM1 couches the power of an enterprise database in the familiar environment of an Excel-style spreadsheet. This means adoption is quick and easy, while still offering you budgeting, forecasting and financial-planning tools with complete control.

Beyond the TM1, IBM Planning Analytics takes business intelligence to the next level. The Software-as-a-Service solution gives you the power of a self-service model, while delivering data governance and reporting you can trust. It’s a robust cloud solution that is both agile while offering foresight through predictive analytics powered by IBM’s Watson.




Data is only one part of the equation

But it takes more than just the data itself to make the right decisions. The data should help you make smarter decisions faster, while your business intelligence solution should make analysing the data easier. 

So how do you ensure top-notch data? Consider these elements of quality data:

  • Completeness: Missing data values aren’t uncommon in most organisations’ systems, but you can’t have a high-quality database where the business-critical information is missing.
  • Standard format: Is there a consistent structure across the data – e.g. dates in a standard format – so the information can be shared and understood?
  • Accuracy: The data must be free of typos and decimal-point errors, be up to date, and be accurate to the expected ‘real-world’ values.
  • Timeliness: Is the data ready whenever it’s needed? Any delays can have major repercussions for decision-making.
  • Consistent: Data that’s recorded across various systems should be identical. Inconsistent datasets – for example, a customer flagged as inactive in one system but active in another – degrades the quality of information.
  • Integrity: Is all the data connected and valid? If connections are broken, for example if there’s sales data but no customer attached to it, then that raises the risk of duplicating data because related records are unable to be linked.

Are you looking to harness the power of your source data to make actionable business decisions? Contact Octane to find out how we can help you leverage your data for true business intelligence.