The world is generating digital data at an unprecedented—and frightening—pace. Global supply chains are extraordinarily data rich and extremely complex.
Now that the age of Big Data is really here, data volumes are off the charts and companies are struggling to achieve digital benefits. Sure, digital analytics empower supply chain managers to capture and analyze more data, and at greater depths, than ever before. But with low-cost cloud storage solutions encouraging them to save all the data they can find, complexity, cost and risk are not being managed, and their ability to engineer the connected and insightful digital supply chain ecosystem that is required to realize full value remains elusive at best.
Nearly every company I speak with tells me they are being held back because of their data or, more precisely, their inability to manage the rising oceans of data. The impact is real, and the data, processes and systems they have today are not getting simpler. The reality is that many supply chain managers haven’t yet had time to develop proven methods for integrating their data and deriving better insights within their four walls, yet they are already finding themselves drowning in that ocean of data and complexity as they try to connect and do business with others in their ecosystem.
According to International Data Corporation (IDC), as of 2014, we were creating data at the rate of 1.7 megabytes of new information, every minute, for every person on earth. In the same report, IDC says the cumulative total of the world’s digital data reached 4.4 zettabytes by the end of 2013 and, by 2020, we will be creating 44 zettabytes of new data every year. I did not make up that word: One zettabyte equals a trillion gigabytes.
Data Volume and Complexity Obscures Supply Chain Insights
Supply chain managers reading those numbers have a right to be scared. Our new report, “Digital Supply Chain: It’s All About that Data,” finds that rising data complexity may well present an existential challenge to companies and their supply chains. The sheer volume of data produced by supply chains and their newly formed digital ecosystems are an embarrassment of data riches whose growing complexity actually inhibits supply chain executives’ ability to access the right business insights at the right time to empower better decision-making. Proliferating data has the potential to harm businesses by adding a counterproductive level of complexity that leads to chaos.
Cost and Compliance Risk Also Rise as Data Proliferates
Supply chain managers’ data proliferation challenges don’t end with obscured business insights and rising complexity. Though storage solutions seem inexpensive at first, costs mount up fast when you’re capturing all the data produced by digital supply chains. A survey of nearly 1,500 Europe, Middle East and Africa (EMEA) companies reported that a midsize company with 500 terabytes of data is likely spending roughly $1.5 million per year in storage and management costs to support nonessential data.
Perhaps worse is the related compliance risk. Companies storing everything for later analysis may put themselves at risk of accumulating sensitive data that is out of compliance with new regulations. The EMEA survey found that, on average, 54 percent of the data collected by respondent companies was dark data, the contents of which was unknown—and companies could only identify 14 percent of their data, on average, as business critical.
But there is a solution. It requires three key steps and simply betting on better technology is not an option.
Step 1. Enterprise Data Strategy
Make enterprise data management strategy a core foundational element of everything you’re trying to do in digital supply chains. This should not be hard, at least not conceptually. Take your organization’s long-term business goals and develop related key hypotheses—i.e., the questions that matter most to achieving those goals. Then, identify the data you need to capture to answer those questions, today and tomorrow. Optimization of supply chain operations happens only when data is analyzed in the context of key hypotheses.
Step 2. Integrate that Data
Of course, enterprise data management strategy demands that you obtain a unified enterprise view of your data. Companies with multiple incompatible data analysis tools in different business units must find a way to integrate that disparate data into a unified enterprise view. One approach is to choose one data analytics platform in which to aggregate data from the others. A more recent approach is to use advanced machine learning tools emerging now that automate data processing, alignment and visualization, enabling you to automate the process of aggregating and classifying data from all your disparate systems.
Step 3: Focus, Simplification, Standardization
Third, use your enterprise data management strategy to instruct the development of classification taxonomies that focus and simplify the rest of the data management and analytical process. This serves to standardize data taxonomies across the organization, and enable more focused data acquisition and analysis. The highest value business insights are being achieved by organizations that thoughtfully classify and analyze their data in the context of key questions that matter most about their business.
Ask the Right Questions to Gain Insight—and Avoid Extinction
Like it or not, modern digital companies must be prepared to continuously evolve their supply chains, business models, and operational processes concurrently with their customers and markets, in real time. With data volumes and complexity increasing exponentially, asking the right questions—questions that provide real value, questions that help define your business goals—is critical. Once those questions and related hypotheses are clearly defined, companies then need to develop data strategies and implement classification taxonomies that enable focused data acquisition and analysis.
Asking the right questions can put your organization on the path to strategy, to simplification and ultimately valuable insight—as opposed to the dangerous path down which chaotic data proliferation is pointing many companies: from threat, to disruption and then extinction.
Dave Padmos is Ernst & Young’s (EY’s) global technology industry leader, advisory, helping clients turn strategy into reality by executing enterprise-wide performance transformation initiatives. He brings over 25 years of consulting leadership, client service expertise/knowledge, industry insight, and IT knowledge to clients in the technology, media and public sectors.
The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organization or its member firms.