As more organizations employ predictive analytics, there is a growing amount of data they must properly manage and store. Failure to do this right can lead to a host of issues that significantly detract from the technology’s benefits and, in some cases, harm the organization.

Luckily, a recent piece in SearchStorage outlines some key mistakes companies make when storing predictive analytics data. Read on for more on these potential traps and what you can do to make sure your organization doesn’t fall into them.

  1. Failing to Avail of Cloud Resources: A key consideration for predictive analytics success is having connected data sets so that companies can have a holistic view of their information. If the data infrastructure and strategy don’t fully utilize Cloud technologies, most organizations will struggle to unite disparate data sets needed for the real-time analysis that is crucial to deriving predictive analytics insights.
  2. Prematurely Implementing Data Management Tools: Of course, data management tools are essential for properly storing predictive analytics data. But if organizations deploy these technologies without first establishing a data storage strategy, they significantly increase their chances of having this information become misaligned, duplicated, or decayed.
  3. Underestimating Performance Requirements: As John Edwards wrote in the SearchStorage article, “Traditional unstructured storage systems were often designed under the assumption that only a small percentage of file data would be active at any given time.” This is not the case with predictive analytics today, particularly since many enterprise storage systems are also burdened by machine learning and deep learning technologies. To avoid predictive analytics storage issues, companies must select an architecture designed to deliver the high performance and scale necessary for the amount of unstructured data in use today.
  4. Treating Predictive Analytics Data as Source Data: The source systems from which the data used to power predictive analytics is drawn are typically designed for operational purposes. This means that organizations must evaluate the data storage mechanism and ensure it fits the analytic approach at hand—automatically storing this information in the same manner as the source system data could lead to issues down the road.
  5. Failure to Properly Store Data: In our heightened security environment, it’s essential that companies have a plan for storing predictive analytics data prior to conducting any analysis. Important considerations include encrypting the information and limiting access to a select group of people.

As more data is generated from the Internet of Things (IoT), we can only expect predictive analytics to become more embedded in enterprise information management strategies. As such, it’s important for companies to be mindful of the considerations outlined above to ensure that improper data storage doesn’t hinder their predictive analytics deployments.