The companies who are the most successful today are investing heavily in data maturity and machine learning to make smart business decisions. However, most companies still lack clean data, clear governance, and the tools to effectively interpret their data and enable machine learning. We’re quickly running out of time where work arounds and band-aids for messy data will buy organizations more time. Purposeful clean data decisions are imperative for ensuring business value in the future.

91 percent of organizations in a survey last year said they had not yet reached a "transformational" level of maturity in data and analytics.

Some of this is due to a disconnect between business and IT. IT may not have the tools or understanding to collect data beyond their own domains, and business may not fully recognize that before you invest in IT and the cloud, you first need to establish data intent and get your data house in order.

While some companies may be aware that they need to examine and clean their data, many are stuck in a sit and spin cycle because they are unsure of how to do it or haven’t yet invested in the resources to do it properly.

Some may think that moving to the cloud isn’t an option for them yet because their data is still immature. However, there’s more benefit that companies may realize when moving to the cloud. Cloud migration can easily be the right motivation to make your data clean as the move forces companies to reexamine their data processes, infrastructure, and governance to mature their data effectively.

Here’s an example I like to use highlighting the difference between deciding to clean up your data for the sake of cleaning it up, and cleaning it up to move to the cloud: If I told you to go to your house and clean up your attic, garage, and office, you might tidy up a bit, but without additional guidance, there’s a good chance that you would do a poor job. Now what if I told you I need you to clean up your 2500 square-foot home and move into a 600 square-foot apartment? You would have to make bigger, more precise changes than you would have made without those specific instructions.

Moving to the cloud, coupled with using the cloud for machine learning, is forcing businesses to truly own their data and understand its dependencies. It also requires business and IT to work together more collaboratively to determine what they want to accomplish with both. They might want to better understand customers’ needs and/or drive down costs.

One of our insurance tech clients, used the cloud and machine learning to gain important customer insights that allowed them to offer better customized packages and grow their market share faster. Without the move to the cloud, they wouldn’t have invested in the heavy lifting it took to thoroughly review, and eventually mature their data.

Cloud-centric disruptors are:

  • Treating data as an asset
  • Measuring data quality and applying governance
  • Moving quickly and driving value via use cases
  • Using business processes that match modern technology
  • Standardizing on open source tools

And by doing so they are often improving customers’ experiences through real-time personalization and reducing fraud, waste, and abuse.

Machine learning and other innovations can be done without the cloud, but it’s so much more effective with it. We know that facing the review and clean up your data is overwhelming because chances are your data is messy and you’re not proud of it. But it’s time to stop avoiding it, to charge through the fear, and move forward to set your organization up for future success.