We’ve been hearing buzz about artificial intelligence and what that means for data management. Don’t get me wrong. We are excited about the possibilities that machine learning and AI tools can bring to data mining and insights. But, we’re still aware that AI is still an evolving technology and, without good data intelligence, companies can get tangled up in the complexity of deriving value from AI.
Some recent stats to consider:
- 84% of customers are concerned about the quality of data being used to feed algorithms.
- 86% of enterprises claim they are not getting the most out of their data.
- 74% say their data landscape is so complex that it limits agility. Reference: CIO Knowledge
Plus, data is now being funneled in from multiple, unstructured sources through different integration systems. The result is an increasingly complex landscape for data. This complexity also comes with new tools to help manage all the new data types.
Before companies can even begin to invest in tools like artificial intelligence, it’s important they first understand a few things…
- What kind of data do you need to see?
- Which users or teams will be analyzing the data?
- How will that data need to be presented?
- What questions do you need to be answered from your data?
We talk about data management but it’s really more about “information management.” What’s the difference? In order for a project to be successful and scalable, the implementation requires both data management and oversight reporting based on data consumed from transactional tools across the entire project lifecycle.
Every member of your team should be able to work together to achieve the results you want, supported by software that offers built-in tools for governance, metadata management, and predictive analysis. This approach enables you to be sure that the results of your efforts can be explained and trusted by all stakeholders.