Data governance is on the rise. A lot of discussions involve data lakes and the emerging concept of data mesh. There are various definitions of data governance. Cohen defines data governance as “the process by which a company manages the quantity, consistency, usability, security, and availability of data”. Newman and Logan define data governance as “the collection of decision rights, processes, standards, policies, and technologies required to manage, maintain and exploit information as an enterprise resource”.
Often the focus is on the data supply chain. Yet there is a big difference if you say “data supply chain” or “data value chain”.
Modern Data Governance must include both sides - data value chain and data supply chain.
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The data supply chain approach sees data as an asset while the data value chain approach treats data via product concept. The supply chain focuses on enabling data flow and security. The value chain focuses on customer needs, who uses the data, and what kind of value it creates. In short, the data supply chain has a governance focus while the data value chain has a business focus.
But before entering data value chains, let's have a look at the data supply chain because in the data economy we need both.
Data Supply chain
The data supply chain reminds me of parcels traveling in ships and trucks. Those parcels are going from product owners and manufacturers to customers. Logistics companies often handle this logistics chain. Important is to deliver the parcel at the given address and in the given timetable.
Now take that above parcel example and put it in the data economy context. There are organizations responsible for creating the raw data, companies refining and packaging the data, combining two or more sets of data into one data product, delivering the data product to the customers.
How to define the data supply chain? Katie Lazell-Fairman provides us a compact definition:
The lifecycle process of data; selection, procurement, transfer, quality assurance, warehousing/storage, data management, transformation, monitoring, and distribution — feeding data pipelines for use in data products.
There’s a lot of similarities between parcels and data products even if the first is about the analog world and the latter is purely digital put aside the sensor hardware devices and so on.
Data Value Chain
What about the data value chain then? To really simplify things we can say that the data supply chain is about logistics. If you manage the data supply chain, you are managing the data flow, making sure it happens in given parameters. Those parameters are set by the data value chain, which is the monetization layer of the data flow. It defines if the data packages are delivered faster or slower to the customer. Much like if you buy from Amazon, you have the option to pay for faster delivery. The same applies to data products. Data product pricing might be set so that you pay more to get more frequent updates or more information.
Now consider a data product that offers status measurements of a given sensor network. Let's say that each sensor contains information about a medical device in a hospital. With a basic plan, you get location and status updates of the devices with a 10-minute update cycle - with a premium you get a 1-minute update cycle. In some use cases, you don’t need the location of the devices every minute, and then selecting a basic plan would make sense with lower costs.
Based on the given reasoning I have defined the data value chain as follows:
The Data Value Chain is about transforming raw and processed data via pipelines enabled by data supply chain into refined customer needs focused data products with business models for an internal, partner or public 3rd party usage.
Data Governance and Productizement symbiosis
Data monetization is the process by which businesses create revenue from their data. In the modern data governance we have both supply and value chain aspects with equal importance.
Data supply chain feeds data pipelines for use in data products, which are the manifestations of data value chain.
Traditional data governance and data monetisation with help of data products are not opposite phenomena. Neither should you consider data monetisation or productizement as a replacement for data governance. Both of them are needed. Which of them has bigger focus and importance changes in time as the company proceeds in entering the economy.
Companies do not just magically jump into data economy. In the early phase data strategy is defined and C-level commitment is arranged as well as the needed funding. At this stage technology and supply chains have small importance. The monetization and business take 80% of the attention. Result is that we have a plan, reason, commitment and funding to enter data economy.
In the second phase it becomes important to build the foundation for the data monetizastion. That is when traditional data governance is having most significant role. At this phase necessary skills are acquired and processed created. At this stage company explores the data products internally. Focus is in the data supply chains. As a result we are confident that we can enter the data economy.
Finally the company enters the data economy. This happens most likely in phases. First, expand internal data products to partner level. Second, design and release public data products sold to 3rd party consumers. Data monetization and value chain kicks in, in all levels! Data monetization business starts to blossom and business developers - not tech - lead the activities.
Key takeaways
We've discussed both data supply and value chain in the episode. Keep following principles in mind and you'll succeed in the data economy
Modern Data Governance must include both sides - data value chain and data supply chain.
Data supply chain feeds data pipelines for use in data products, which are the manifestations of data value chain.