Data Value Chain – How to capture value in the long-term

When Michael Porter, renowned guru and thought leader of business administration, established the concept of the value chain, it was 1985. The value chain, Porter famously states, refers to the activities and processes, which an organization undertakes in order to create a marketable product. During these activities and processes, companies combine various available resources. More specifically, Porter listed money, labour, materials, equipment, buildings, land, administration and management. In 1985, when Porter first specified the concept, most value chains involved the flow of physical goods to and within manufacturing sites and the subsequent assembly of products. But the good old days have passed. (Big) Data might have become the most valuable resource for business operations.

Big data helps companies to allocate traditional resources – labour, land, capital – efficiently and is therefore the power engine of the modern corporation. To unfold its full potential, it has to be tweaked and refined at various stages. Essentially, there is a data value chain (DVC), which refers to all kind of activities and resources, which are applied to support the applicability and value creation of data in a commercial context.

Consultants and industry experts have long been urging companies to get in shape for the “age of big data”, to holistically assess how big data can help to improve operations, product development and marketing. Put another way, every aspect of commercial activity is under scrutiny. Effective management of the data value chain becomes a crucial competitive advantage. Nevertheless, there are still some uncertainties surrounding big data and what it takes to tap its full commercial potential. The concept of the data value chain might help to clarify what it needs for corporations to succeed in the long term.

Data source mapping – understanding data as a valuable resource

In order to leverage data for commercial activities, corporations need to develop a granular sense of what data sources are even available (and relevant) to them. With increasing digitalization, there are valuable data sources everywhere. Incoming customer request e-mails are just as valuable as regional sales data for different product categories. Beyond internal data, there are countless external data sources, which prove just as valuable. Acquiring a thorough understanding about the availability of data streams is the first task in order to arrive at a “data source map”. Just like oil companies need to know where precisely to drill, developing a “data source map” for the business at stake is a crucial and valuable exercise.

Managing variety – data processing and standardization

Given the variety of data types, sources and formats, standardization and processing of data in an integrated fashion is a key component within the data value chain. In order to enable any kind of insights, discover new market opportunities, foster the development of products or lift operational inefficiencies, data formats need to be streamlined and interoperable. In fact, it is this database-oriented work, which forms the backbone of any kind of big data strategy. Without it, big data is not big data, but a mere collection of random data sets.

Analysis and decision-making

Building up on the previous activities within the data value chain, analysis and forecasting techniques are applied to generate new insights and actionable conclusions. Applying inter alia sophisticated algorithms and machine learning (to name just a few buzzwords), data scientists do their magic to eventually lift the potential of big data for the corporation. Deriving actionable insights in a timely manner and thus enabling data-driven real-time decision-making is the key objective and data science bears responsibility for the development of the engines behind the scenes.

Which activities provide the largest value add?

Big data has little to no value if there is not a clear use-case for the business. What precisely should be optimized? What dynamics should be assessed in order to increase profits? Essentially, from a business perspective there is always two possible use cases: increase revenue or decrease costs. From this use-case-driven perspective, the largest value-adding activity in the data value chain stems from data analysis and visualization in order to drive decision-making. Without it, all other previous activities are useless.


However, a solely use-case driven perspective is also a very short-term-oriented approach. For just one use case, costs for pre-processing & integration of data sources might seem outrageous and could potentially offset any kind of benefits derived from the initiatives. One could therefore try to lower up-front costs for data processing and integration, taking shortcuts and tailoring it to one specific use-case. While this might seem tempting in the short-term, it actually prevents big data from unfolding the commercial opportunities that it inherits.

Establishing a state-of-the-art data processing platform should actually not be treated as costs, but as an investment, as it enables all future use-cases to run with significantly lower costs for this value-adding step. While – from the short-term cost-perspective – the value add /cost ratio is off balance, it balances out in the long-term as it provides data scientists with a solid platform to work with, which only has to be modified and complemented instead of being created from scratch again. Applying the long-term perspective also underlines the significant returns the investment into pre-processing and integration of data sources can provide. The investment allows any business to unlock the potential on those steps in the data value chain, which are most valuable for them: data refinement and analysis for decision-making purposes.


While the manifesto holds true that data without a use-case is useless, it is just as true that any big data strategy is useless in the long-term, if it does not build upon thorough and holistic efforts to pre-process and integrate data from various sources. While we don’t know what the future brings, we know that big data will play a big role for every company out there. While some might not see the relevance yet (or rule out the benefits because of short-term costs), establishing an integrated data platform is a good investment in the future and will be an essential step for any corporation to get in shape for the “age of big data”.


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