Trucks at Work

Aiming the Big Data guns at one’s self

There’s never been much doubt that “Big Data” efforts in trucking are by their very nature largely internally-directed as fleets seek to figure out how to boost fuel savings, productivity, and asset utilization, along with improvements to other aspects of their operations by analyzing various streams of information.

For a time, though, many felt that Big Data efforts should be “outward” focused; that is, looking at ways to use information analysis to improve interaction with customers.

Yet now it seems many businesses are rapidly adopting trucking’s Big Data perspective, at least according to a new report compiled by Capgemini Consulting.

There’s a lot of overwrought language in the firm’s new study, entitled Going Big: Why Organizations Need to Focus on Operations Analytics. Yet the salient point is this: More than two-thirds (70%) of the companies it surveyed have put more emphasis on operational analytics initiatives over the last three years than on consumer-focused processes.

However, broad deployments of “Big data” efforts remain still remain limited at this point, with success an even greater rarity, with only 18% having both implemented analytics widely across operations and achieved desired objectives.

Is there a lesson in here for trucking? Anne-Laure Thieullent, who handles the European side of things for Capgemini's Insights & Data global practice, offered this thought on Big Data projects, which truckers should take to heart: “Factors limiting the success of Big Data projects are specifically siloed datasets, fragile governance models, inability to harness third-party data sources, and an absence of a strong mandate from leadership teams.”

Yet she also noted this interesting twist: U.S. companies are not only the most advanced with their “Big Data” analytics initiatives but also the most successful as 50% have successfully realized the desired benefits from operational analytics compared to only 23% of Chinese respondents.

Why are U.S. businesses more successful at Big Data initiatives? Capgemini believes a strong contributing factor is the focus by U.S. firms on setting up effective data and governance processes, as 47% of U.S.-based companies have made analytics an integral part of their decision-making process compared to just 28% percent in Europe.

“The prominence of U.S. organizations tallies with a recent resurgence in U.S. manufacturing and will drive U.S. manufacturing competitiveness in the coming years,” Thieullent noted.

She noted, too, that Capgemini has identified what it calls the “four stages” of operational analytics maturity:   

  • Game Changers – Just 18% of organizations are “game changers,” which are those firms that have integrated most of their analytics initiatives with their business processes and have realized the anticipated benefits from their analytics initiatives.
  • Optimizers – Some 21% have typically realized early benefits from their analytics initiatives in a limited number of areas within their operations but have not yet scaled up to more complex initiatives.
  • Strugglers – About 20% have integrated analytics in most of their business processes but struggle to realize the benefits.
  • Laggards – The lion’s share, 41%, are introducing analytics initiatives in their operations, they’ve mostly just implemented proof of concepts and lag behind in terms of deriving benefits.

So how does a company get into the top “Big Data” group? Capgemini boils it down to four key organizational attributes that will enable companies to leap ahead of their competition in this area:

  • Integrated data approach: Leaders in operations analytics are integrating datasets across their organizations to gain a holistic view of operations. Some 43% of Game Changers have completely integrated datasets compared to only 11% of Laggards.
  • Using a wide variety of data: Successful companies enhance the quality and scope of their operations data by using external and unstructured data; some 59% of Game Changers do this compared to 27% of Laggards. Similarly, 48% of Game Changers use external data to enhance insights compared to only 23% of Laggards.
  • Making analytics an essential component of their decision-making process: Within operations this is reported by 58% of Game Changers compared to 28% for Laggards.

Yet Jerome Buvat, head of Capgemini’s Digital Transformation Institute, warns that even the most successful “Game Changing” firms in the field of Big Data, be they in trucking or not, have only “scratched the surface” in terms of the benefits that can be gained via operational analytics. “More elements of the demand chain, from the factory floor to the products sold to customers, are becoming connected and are producing data,” he stressed.

“Cognitive computing is helping organizations to make sense of all of this data, while machine learning and Artificial Intelligence is enabling increasingly complex decision making and operational optimization,” Buvat added. “Yet few organizations are well set up to take advantage of these technology developments; those that aren't need to work out now how they catch up or face diminishing competitiveness.”

Something trucking fleets should think on as well.

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