Executive Summary | Monetizing Data and Analytics
Data monetization opportunities are increasingly within reach of businesses everywhere. This report illustrates how enterprises are addressing data monetization opportunities—both indirect and direct—through the democratization, operationalization, productization, and sale of data.
- By James G. Kobielus
- March 16, 2023
Data monetization opportunities are increasingly within
reach
of
businesses
everywhere.
Recent innovations
in
converged
data
analytics
platforms can accelerate an enterprise’s ability to productize their data and other data-derived assets,
such as
trained machine learning models. In addition, cloud marketplaces
can
be
key
channels
for
monetizing
these assets as well as any AI/ML-powered products, services,
and
applications
with
which
they
are bundled
or
into
which
they’ve
been
embedded.
Monetization opportunities thrive when enterprises make data, data-driven analytics, and insights more easily accessible, consumable, shareable, and programmable. For years, TDWI Research has written about the growing democratization of self-service business intelligence, data science, and other analytics tools. This Best Practices Report examines whether enterprise democratization of data and analytics is making a contribution to increased revenues, cost reductions, process efficiencies, returns on investment, and other quantifiable aspects of financial performance.
This report uses findings from a survey of 185 data management and analytics professionals. It illustrates how enterprises are addressing data monetization opportunities—both indirect and direct—through the democratization, operationalization, productization, and sale of data. Key findings include:
Data democratization expands access to tools, platforms, and skills needed for monetization. Democratization is often the starting point and focus of most enterprises’ data monetization journeys because it can empower employees and others working within (and in partnership with) the enterprise to do more. In that regard, respondents reported that two-thirds of their organizations have been either very successful or somewhat successful at data democratization and roughly two-fifths of business users employ self-service tools for data discovery or exploration. Where democratization is concerned, the most important enabling enterprise investments cited included building data literacy, self-service analytics and data preparation, providing data analytics automation and productivity augmentation tools, and improving the skills of business analysts to perform more advanced analytics.
Data operationalization is widely adopted and drives indirect monetization. Data operationalization is the process of using data an enterprise owns or has acquired to influence business outcomes that boost revenues and/or reduce costs. It’s often a key jumping-off point for subsequent enterprise journeys into direct data monetization. To the extent that an enterprise has successfully democratized access to data and analytics, it can drive data operationalization more deeply into every facet of its operations. It can more pervasively pursue data-driven optimization of every decision, process, and interaction to achieve the best results, both quantitative and qualitative. Almost two-thirds (60%) of respondents cite data operationalization as a priority impact of data and analytics in their enterprise.
Data productization is a fundamental step toward direct monetization, but it’s not yet widely adopted by enterprises. We define data productization as an enterprise using its data and data-derived assets to build revenue-generating products and services that it sells to external customers. For purposes of this report, a “sale” refers to any collection of a fee for the distribution of a product or service to a customer. This includes a one-time sale, leasing or licensing a product or service, or providing an updated product or service on a regular basis (such as through a monthly subscription). Data productization is not yet a major revenue-generating activity for most firms, but there is ample expectation that the bottom-line contribution from these activities will grow in coming years.
Enterprises are almost evenly split between productizing data and analytics as a revenue- generating practice now (or planning to) and having no plans to do so. More than one-fifth (22%) report that their organizations are currently productizing data and analytics, with the principal products being data-derived analytics applications such as dashboards and scorecards.
Nevertheless, organizations are beginning to organize for greater adoption of data productization, with more than two-thirds (68%) of respondents reporting that their enterprises have designated specific individuals, roles, or functional groups for productizing data and data-derived assets they own, or that they plan to. However, there is currently no clear single role responsible for data productization in the business world; the responsibility is most often assigned to data managers and data stewards.
Data sale is a logical next step beyond productization, but it depends on enterprises having access to trusted cloud marketplaces and exchanges. Data sale refers to the selling and licensing of business data, analytics applications, and other data-derived assets. Enterprises are clearly on the path to reselling raw data sets, cleansed and curated data, pretrained machine learning models, and other such assets directly to consumers, many of whom are also businesses. Around one in five respondents reported, for example, that they are selling derived, transformed, cleansed, augmented, and/ or curated data sets, while almost as many are reselling data-derived algorithms and/or models, such as machine learning, deep learning, and natural language processing models. Close to three-quarters of respondents reported that their enterprises already rely on cloud data marketplaces, data exchanges, or other channels to sell data sets, pretrained machine-learning models, and other digital assets—or plan to in the coming year.
Of course, there’s a clear limit to how far enterprises can and should go with data monetization. An enterprise should never attempt to monetize any data through any approach unless it owns the data or has clear rights to use it in the intended fashion. Given the wide range of data privacy laws and regulations around the world, businesses must be exceptionally careful when attempting to monetize data that includes personally identifiable information of customers, employees, and others. Additionally, they should always maintain robust governance safeguards for ensuring that only sanctioned, high-quality, curated, and compliant data assets are monetized.
It’s still the early days for data productization and monetization. Nevertheless, the journey is enticing and many enterprises have embarked on it. The widespread adoption of robust DataOps and MLOps pipelines are empowering more data scientists, business analysts, and others to productize data and analytics more effectively. These tools and platforms—plus the cloud-based marketplaces through which data and data-powered products and services are offered—are the key to comprehensive data and analytics monetization.
SAP and Snowflake sponsored the research and writing of this report.