Start to Monetize Your Data with Data Marketplaces and Data Value Scoring
With examples of online marketplaces all around us, smart organizations are following suit by providing data marketplaces to data consumers across their enterprises.
Treating data as a product and making it available through a marketplace is a rapidly trending and proven approach to better using and managing your data. A well-structured, well-governed data marketplace offers your business users a consumer-like experience for discovering, understanding and selecting the data products that best fit their particular business needs. In one central marketplace, your data stewards and owners can curate, score and manage the organization’s data assets. At the same time, your data analysts, data scientists and other business users shop for, analyze and share knowledge about those assets, then request and gain governed access to them.
In this post, I’ll examine data marketplaces and the related concepts of infonomics, data valuation, data monetization and data value scoring. You’ll see the benefits your organization can derive from its own data and the central role that your data intelligence software plays in the effort.
Infonomics: Treating data as an actual asset
It’s an axiom that data is valuable, but few companies ever make the effort to measure how valuable it is. They rarely value its cost basis or its contribution to revenue as they would any other asset, like a building or a printing press. They’re in a poor position to manage their data like an asset because they can’t manage what they don’t measure.
If you’re not managing your data as an asset, then you cannot fully monetize or generate value from it, either. There’s a vicious cycle of not measuring, not managing and not monetizing your data. And the result is underperformance as a business.
Infonomics is a way of flipping the script so that you start measuring the value of your data. That leads to making the investment needed to manage data as you would any other asset. The result is an improvement in quality and availability of data such that you can monetize it.
With time, the effort should be self-funding. Your goal should be enterprise data management and an analytics function that pays for itself, like a self-funding data warehouse, data lake or data mesh.
What is data monetization?
Mind you, this is not just about selling data. Think of data monetization as the process of generating measurable value streams from available data assets.
First, you should approach it as a process and an ongoing initiative for your organization.
Second, it involves measuring what you’re generating. That means regularly connecting the dots between what you’re doing with your data and the economic value that comes from it.
Third, data monetization is a function of value streams that come in many different forms.
And finally, those value streams come from your organization’s available data assets. You can also look at data beyond your own four walls and find ways to generate value from it. Examples include open data, syndicated data, web content, harvested web content and social media.
Many organizations monetize their data indirectly. Their internal data monetization efforts include improving process performance, reducing risk, improving partner relationships and developing new products and markets.
Other organizations focus on monetizing data externally. Their direct monetization efforts include bartering and trading data, using it to enhance existing products or services, licensing it directly and selling market insights.
Naturally, industry and government regulations preclude the sale – and even the exposure – of customer data because of personally identifiable information. Instead, you can sell additional related products and services to your customers, or you can use your data as collateral to secure loans. In fact, even though your data can’t be an asset on your balance sheet, it’s possible to move your data and digital assets into a separate holding company. That company is then valued and included on the parent company’s balance sheet.
The role of the data marketplace
No matter how you want to monetize your data, you will need a marketplace. The pursuit of monetization is leading to the rise in data marketplace adoption, according to the West Monroe 2023 Data Monetization and Marketplace Study, which surveyed more than 500 companies. And the payoff is considerable.
The study found that businesses with both internal and external data marketplaces are twice as likely to outperform their peers. That applies whether those businesses use external marketplace vendors or set up a marketplace on their own. More significantly, companies with both internal and external data marketplaces see their investment returned threefold. In other words, the companies are performing better and their data marketplaces are generating measurable benefits. That’s why measuring data is on par with both managing and monetizing it; all three are important.
So, should you start out with an internal data marketplace, external data marketplace, or both? The answer varies. Companies in highly regulated industries tend to start with internal data marketplaces focused on the benefits associated with getting governed data more easily into the hands of data consumers. In less-regulated industries, internal data marketplaces continue to be beneficial, but external data marketplaces are also more common because companies have the latitude to sell or license their data and data derivatives.
Data marketplace trends
Survey results indicate a 25-percent increase in companies commercializing data products and a 70-percent increase in those forming a line of business for it by 2025. That points to a trend in managing data products as a mainstream business function.
As suggested earlier, the surveyed companies are using data products and marketplaces for a variety of purposes:
- Exchanging data with other organizations
- Licensing data to consumers and third parties (e.g., other data brokers)
- Improving the stickiness of relationships
- Improving decision making
- Improving operational effectiveness by mapping changes to data
- Promoting their own products better
- Introducing new revenue streams
- Exchanging data for favorable commercial terms like discounts or non-cash commercial benefits
Recommendations for data products and data marketplaces
Still, this quickly evolving landscape is unfamiliar to many companies. Here are a few guidelines as you explore these new opportunities:
- Use internal data marketplaces to achieve greater levels of data democratization and data governance.
- Don’t wait to implement or participate in external data marketplaces.
- Given the strategic importance of data marketplaces, chief data officers (CDOs) need to take an active role in establishing them. Don’t leave this to your IT organization alone.
- External data marketplaces should bring in more benefits than just cash. Look for ways to exchange data for other kinds of favorable commercial terms and relationship-related benefits.
- Follow a disciplined, product-oriented approach to data monetization, as you would with any other product introduction.
- Anticipate objections and challenges to sharing data internally and externally. Put in place a change management approach as an adjunct to your efforts.
- Measure the projected and actual return on investment and annual rate of return on your data products.
Data value scoring
As described above, if data is to be an actual asset, you must begin to measure its value. Data value scoring (or data valuation) is a way of establishing your data as an asset now and measuring the return on it later.
Some companies set a value on their data before they monetize it; others combine the efforts. Either way, data value scoring is useful for reasons as varied as encouraging data governance, justifying a data management budget, stimulating data usage and building a data-driven culture. In the context of the data marketplace, it helps potential consumers assess the value of data they want to acquire – an important part of data democratization.
Scoring data value takes into account a variety of information about the data. Data quality, previous data user assessments, the extent of associated data governance provided – these and other metrics can be used to assign a data value score that will point data consumers toward high-value data. Data intelligence software, such as erwin Data Intelligence by Quest, offers organizations sources of information about the data, as well as the automation to deliver data value scoring that is standardized, efficient and reliable.
From model to marketplace – Tackling data product delivery
Data marketplaces and data value scoring are imperatives for organizations pursuing data monetization efforts. But, when further fueled by data modeling, cataloging and governance, tackling a data-as-a-product approach is more pragmatic and achievable. Imagine this scenario:
- The business envisions and creates the data products they want to take to market.
- They request from the data architects the data they will need for those products.
- The architects put together the appropriate data model.
- The model is connected to the data catalog, enabling you to push it into the catalog. You can then use either the model or the catalog to generate the schema of the database, including the tables, columns and extract-transfer-load (ETL) for the datasets.
- The business then introduces data governance controls, automatically scores the data and further curates the product by answering questions such as these:
- What is the intent of this dataset?
- Which data is behind it?
- Where did that data come from?
- How should this data set be used for this data product?
- What kind of insights come from this product?
- Once curated, the data product is placed into the data marketplace for all data consumers to easily find, gain governed access to and begin to use within the business.
Conclusion and next steps
To learn more about data marketplaces and how they can support your data valuation efforts, watch the on-demand webcast, “Data marketplaces and the value of data scoring,” with Susan Laine, director of solution strategy for erwin by Quest, and myself. We also discuss data valuation in more depth in this on-demand webcast, “From infonomics to data juice, data value is still king.”
You can also learn more about erwin Data Marketplace here.
Driving data democratization
Watch the webcast “Driving Data Democratization with erwin Data Marketplace” to learn how you can offer your data users a central location to shop for, share and compare enterprise data.