Big Data Integration in a Transnational Business World

Stephen DeAngelis

June 25, 2015

The landscape in which today’s transnational businesses must operate is complex. Challenges can be confronted at every level — national, regional, local, and internal. There are no “from the box” solutions that are going to be right for every company since each company’s situation is unique. As supply chains get extended around the globe, their complexity increases. To help deal with this complexity, companies need improve visibility into their supply chains. Supply chain analyst Lora Cecere (@lcecere) recommends that, at a minimum, this visibility should extend from a company’s suppliers’ suppliers to a company’s customers’ customers. Few companies today have this kind of supply chain visibility. There are lots of reasons for this, but one of the most significant reasons is that data integration and analysis is difficult, especially when it involves different systems, different languages, and sometimes a complete lack of electronic data. And, of course, the companies that provide the physical links between suppliers and customers (i.e., brokers, transporters, warehousers, etc.) must also be added into the mix.

Take, for example, the situation faced by the Coca-Cola Company. Professor Sam Ransbotham (@Ransbotham) of Boston College, explains, “At The Coca-Cola Company, pulling together useful data sets is a particular challenge. Coke’s distribution model involves a network of hundreds of independently operated bottlers around the world that use the Coke concentrates to make and bottle Coke drinks (as well as other non-Coke affiliated beverages). Those bottlers send data to Coke, and Coke’s job is to put those data streams into a common system and use it to look back on how things have gone and project how things might be. Complicated — to say the least!”[1] To underscore exactly how complicated, Ransbotham provides a few statistics:

“Coke is the world’s largest beverage company, with more than 500 brands and 3,500 products sold worldwide. In 2013, the company had $46.9 billion in net operating revenues, and a net income of $8.6 billion. It has about 250 bottling partners with 900 bottling plants, and employs over 700,000 system associates worldwide. In addition to its flagship Coca-Cola products, the company’s brands include Minute Maid juice, Fanta and Sprite soft drinks, and Dasani water.”

To find out what kind of challenges Coca-Cola must deal with, Ransbotham interviewed Coke executives Remco Brouwer and Mathew Chacko. Chacko told him, “Currently our bottlers run their own system, and so they send us data in all sorts of different formats. We have to be flexible in being able to inject data. But when we transform that data, we need to transform into those standardized taxonomies or hierarchies. We also have the reverse problem because we need to transform information back into the bottler’s view — we have to give them back their information in formats they can read. We aspire to provide data as a service, both to our customers and to our bottlers.” Brouwer added:

“There are over 250 bottlers around the world, and we are in the center of this nucleus. These 250 bottlers are all sending us data. We’re trying to solve some of the day-to-day things like moving more and more into the direction of a one-number system. At the same time, it’s the idea of the shared knowledge because in the end, overall, we want the same thing, and that is to understand the consumers better and be as relevant as possible in the market so that people will buy our products and be delighted by them. In the end it’s about — it’s a bit of an old term, but it is about making better decisions faster. Better decisions by having your data right and your right visualizations, and faster by not spending too much time to get there. That means starting at the bottom and trying to drive clarity in the numbers themselves. As we move more into the direction of big data and cross-measure analysis of everything that big data technologies enable, we’re at the beginning of the path here, there are many answers out there to questions we did not know we had.”

One way of dealing with all of this complexity is to employ a cognitive computing system, like the ENTERRA® Enterprise Cognitive System™, that can help integrate data as well as perform the kind of analytics that leads to better and faster decisions. These advanced analytics are sometimes referred to as “Analytics 3.0,” which are characterized by self-learning technologies capable of generating insights across structured and unstructured data. Systems that employ Analytics 3.0 technologies are able to elevate the decision making of the total workforce as well as institutionalize knowledge. An Accenture study explains it this way, “Cognitive computing technology builds on [machine learning] by incorporating components of artificial intelligence to convey insights in seamless, natural ways to help humans or machines accomplish what they could not on their own. At its most advanced, cognitive computing will be the truly intelligent data supply chain — one that masks complexity by harnessing the power of data to help business users ask and answer strategic questions in a data-driven way.”[2] The “data supply chain” is what Accenture calls the kind of information flow required by companies like Coca-Cola. Cognitive computing systems facilitate the data supply chain. In fact, the Accenture study calls cognitive computing “the ultimate long-term solution” for dealing with data complexity.

At Enterra, our ultimate goal is to help companies become digital enterprises with the help of our Enterra System of Insight and Actions™, which involves our Enterprise Cognitive System™ and the use of cutting edge mathematical solutions. The Enterra Enterprise System of Insight can interact with systems of record to enhance value chain operations and foster a better digital path to purchase for consumers in areas such as: new product development; strategic sourcing; manufacturing; supply resilience; transportation & logistics; omni-channel sales; and consumer sensing. These are all complicated areas in which transnational corporations need to excel. Once a company starts using a cognitive computing system, it will undoubtedly find uses for the system that weren’t even originally imagined. The system becomes a collaborative business partner that never sleeps, never takes a break, and is always looking to improve its performance.

Coca-Cola, of course, is not alone in facing complicated and complex data integration challenges. The Accenture study insists that “every business is a digital business” and most analysts agree that the most successful companies in the decades ahead will be those that successfully transform into digital enterprises. With the advent of the Internet of Things, the amount of data that is going to be generated, stored, and analyzed will explode and companies are going to need all the help they can get to stay afloat in the oceans of data in which they must swim.

Footnotes

[1] “Coca-Cola’s Unique Challenge: Turning 250 Datasets Into One,” MIT Sloan Management Review, 27 May 2015.
[2] “From Digitally Disrupted to Digital Disrupter,” Accenture Technology Vision 2014.