Article originally posted on Data Science Central. Visit Data Science Central

Perhaps to no one’s surprise, the growth in companies implementing Big Data and Analytics projects continues to climb – as evidenced by the continued growth in data lakes.  As most companies begin to implement their Big Data and Analytics strategy, they struggle to show value for their efforts.  This can come from several areas:

• According to this Accenture report, one of the most common reasons Big Data and Analytics projects under-perform is that teams are measuring the wrong things. Some of the questions you need to address are “how many things do we really need to measure?”, and “is there a causal relationship between the metric and the business?”  This is precisely where most BDA teams miss the mark so it’s important to get this right. See my blog “Unintended Consequences of the Wrong Measures” for more insights into identifying the right measures.
• Another example comes from this Deloitte surveythat claims “better decision making” as the top benefit of implementing a BDA project (see Figure 1).

Figure 1:  Source: “The Analytics Advantage. We’re just getting started”

Let’s use sports as an example.  Say the metric of measurement is “Days on IR (injured reserve)”.  This metric may need further refinement as different positions impacted by IR have varying impacts on the performance of the team (starting QB vs. 3^rdstring QB on IR for example).  Stephen Smith presented a novel approachat the 2017 MIT Sloan Sports Analytics Conference.  Stephen uses a method that includes NBA player salary to show the value of injury prevention programs.  As it turns out, the more money a team has on the floor, the greater their chances of making the playoffs:

Figure 2:  Quantifying the Performance impact of avoiding injuries

Another example is the college football kickoff rule change implemented by the Ivy League in 2011.  They studied their data and found that most concussions occurred during kickoffs.  They then moved the kickoff from the 35 yard line up to the 40 and brought the touchback out to the 25 from the 20 yard line.  This resulted in more touchbacks and fewer concussions (see Figure 3).

Figure 3: Impact on Concussions From Change in Kickoff Point

See the blog “Leveraging Agent-based Models (ABM) and Digital Twins to Prevent Injuries” for more examples how leading sports organizations are leveraging Big Data and Analytics to prevent costly injuries.

As a leader in IoT, Big Data, and Analytics, Hitachi is well equipped to help derive full value from all of your data.  From Reference Architectures for Cloudera,MapR,Hortonworks, and MongDBengineered to help you quickly build your data lake to architectures designed to allow you to query both structured and unstructured data with a single query.

If you would like to learn more about what Hitachi offers in data management and data intelligence solutions, be sure to come see us at NEXTin San Diego, CA September 25-27.  When you make it there, you’ll want to see the “Accelerate Your Application Ecosystem With VSP ” and “How SFS Group, Aldo and Cognizant Transformed With IT Modernization” breakout sessions.  And be sure to catch my Spotlight session “Improve Your Return on Data With Modern Data Pipelines for ML and AI.”