The challenge of integrating new data and collection tech with historic data

To additional strengthen our dedication to offering industry-leading protection of information know-how, VentureBeat is worked up to welcome Andrew Brust and Tony Baer as common contributors. Watch for his or her articles within the Knowledge Pipeline.

Any time new applied sciences or techniques change into too invaluable or ubiquitous to not combine for companies throughout {industry}, there are holdouts that cling to the outdated methods or prioritize the acquainted over the revolutionary. These organizations have a tendency to not final lengthy.

However even amongst adopters, there are these firms that try to fail to merge the outdated with the brand new, whereas others make it occur. We’re seeing this on full show within the areas of sports activities, the place organizations are challenged to combine legacy information with new assortment applied sciences and information units. What units the success tales aside?

When confronted with waves of latest information on account of developments in automation and information assortment strategies, a sports activities group ought to first acknowledge that it’s an excellent drawback to have. With know-how like lidar, for instance (a laser-based movement-tracking system), that’s targeted on bettering the accuracy, depth of data and seamlessness of information assortment, efficiency evaluators now have entry to an unlimited, untapped trove of information that can be utilized to raised inform their choices. The query then turns into: how does a membership handle that inflow of latest information?

First, preach endurance. Think about that organizations and their information groups have been utilizing the identical strategies and approaches, making the identical assumptions and associations, for years. Previous habits die laborious. And since superior analytics will be utilized to all the pieces from recreation technique to the optimum kinds of soda served on the stadium concession stands, a corporation adopting these applied sciences for the primary time will want across-the-board buy-in. That takes time.

The largest problem, nevertheless, is integrating a corporation’s historic information with trendy data. Assortment applied sciences and strategies aren’t all which have modified on this space. Right now’s information appears very totally different than that of the previous, and in some circumstances, the kinds of measurements don’t align with earlier information units. How do a corporation’s information groups clear up this drawback? Begin right here:

  • Run translation workout routines. Put aside a transitional interval throughout which an in depth evaluation of all information and strategies – each trendy and historic – is performed.
  • Amass a statistically important quantity of information. Keep away from any statistical noise or false positives a too-small pattern dimension might yield. You’ll wish to get this proper the primary time.
  • Pay attention to biases. Sure predilections might happen within the calibration of the system. Figuring out and correcting them are necessary to keep away from constructing bias into your baselines and future calculations.
  • Account for variations in information assortment strategies. Totally different sports activities venues use quite a lot of monitoring know-how, a few of which have inherent limitations that affect the information collected.
  • Know that some translations will be probabilistic in nature. Measure to a continuing: in different phrases, participant X runs at a pace of Y, so the brand new measurement output needs to be equal to Y.
  • Integrating outdated and new information will be laborious. Ensuring that outdated information units aren’t misplaced whereas embracing the insights new information unlocks will be expensive and time-consuming. But it surely’s necessary to recollect after the train that a corporation might be higher positioned to make personnel choices. 

The important thing for sports activities organizations integrating outdated and new applied sciences, methodologies and data is to take a deep, thorough dive into the information. Uncooked historic information don’t assist most golf equipment. Knowledge must be simply understood by new person profiles right down to make it viable, which takes invaluable time and which can leech all its usefulness within the course of.

A schism could exist between information units monitoring comparable or equivalent actions utilizing totally different applied sciences or approaches. When measuring the power of a kick on the pitch, for example, information collected from wearables hooked up to a participant’s boot could not simply combine with information collected that measured that very same kick utilizing laser-based lidar. 

And since wearable applied sciences are limiting in the place and the way typically these measurements will be tracked, there could also be gaps within the suggestions from the tech on account of lacking information factors. Knowledge smoothing can’t sew this data collectively.

Upgrading to new applied sciences is, after all, typically price it. Take lidar, which is extra correct whereas being extra moveable and unobtrusive from the participant’s standpoint than previous tech. The problem of information integration is the one noteworthy draw back to adopting lidar for a membership’s participant analysis division. And with the fitting plan, even that problem will be solved.

Raf Keustermans is the CEO of Sportlight Expertise.


Welcome to the VentureBeat group!

DataDecisionMakers is the place specialists, together with the technical individuals doing information work, can share data-related insights and innovation.

If you wish to examine cutting-edge concepts and up-to-date data, greatest practices, and the way forward for information and information tech, be a part of us at DataDecisionMakers.

You may even think about contributing an article of your personal!

Learn Extra From DataDecisionMakers

Leave a Reply

Your email address will not be published. Required fields are marked *