The Need For Speed in Business Analytics

The Need For Speed in Business Analytics
The Need For Speed in Business Analytics

Your analytics initiatives are only as useful as the data that fuels them.  A recent HBR article: Simplify Your Analytics Strategy summed it up nicely.  If you are serious about competing on analytics, you need to have a strong competence in data.

Fast Data = Fast Insight = Fast Outcomes

I recently spent time at an analytics event talking to senior architects and IT leaders on this subject.  Here are some of the interesting quotes I heard as I discussed the need for speed on these initiatives:

  • “IT cannot deliver what (the speed that) business needs today”
  • “The faster you can integrate (data), the more innovation you can bring to your organization.”
  • “I look at speed as a huge factor in organizations, as they way they will differentiate themselves.  Those that get that speed matters (will differentiate themselves).”
  • “You want business agility, but you need to go faster.”
  • On succeeding with the business; “We won their trust by delivering in 3 months rather than one year.”
  • “New (analytics) technologies come frequently. We need to integrate them faster.”

The purpose of analytics is to deliver actionable insights, faster than the competition.  By actionable insights we specifically mean an insight that can be used to make a business decision.

The reason for speed is that many organizations are looking to achieve competitive differentiation based on the analytics insights they can deliver.  The math is pretty simple; insights have the most value when they are unique in your industry.  As soon as another competitor has the same insight, the value of that insight decreases dramatically.   As one senior manager from a global system integrator put it:

  • “We must make use of data before it loses value.  In realtime.”

It all sounds pretty straightforward, but there multiple forces working to actually slow down the delivery of clean, complete, and timely data for your analytics initiatives.  Some of the major culprits are:

  • Data is locked up on application silos. It was never designed to share with any other use.
  • The volume of new data. Data volume is estimated to be growing at 40% per year.
  • The complexity of the data. Gartner estimates that 50% of the data at large organizations is coming from outside of the company. This means many new and unknown data formats.
  • The lack of standardization for how data is managed in most organizations. Data is usually a subset of a larger project with not provision for sharing the data with other applications and uses other than the project at hand.
  • Islands of Technology. New, emerging analytics technologies (cloud, big data, NoSQL, and others) are leading organizations to create islands where data is managed differently for these initiatives than it is for the rest of the organization.

What best practices do we recommend for organizations looking to manage their data for competitive advantage – particularly for analytics projects?

  • Standardize on data management tools. Stop the one-off projects.
  • Look for data management tools that will work together across as many analytics use cases, data types, and analytics technologies as possible.
  • Any data on-boarded must be discoverable and usably by any new project or user (who has access rights).
  • Require reusability of logic, and skills
  • Require automation in order to scale your resources
  • Data management processes must be repeatable in order to operationalize them
  • Look for machine learning and intelligence to drive productivity

For more information:

eBook: What It Takes to Deliver Advanced Analytics