Companies see data as one of their most powerful assets and enterprise data continues to be created at massive scales. Yet many companies don’t how to collect, manage, use, monetize and value their data. This means there is potentially a huge, unused set of data in organizations’ database or datacenter. This is what Gartner defines as dark data: information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes such as analytics, business relationships and direct monetizing. (1)
As the cost of technology continues to fall, and both individuals and companies have more opportunities to access and process data, the way companies collect, manage, use and monetize their data is important for competitive strategy. The value of data can only be unlocked if the data is collected and used in ways that drive innovation and enable the creation of new revenue streams and business models.
To put an appropriate “price tag” on data, companies need to consider what approaches there are to take when it comes to data valuation. Failing to accurately quantify the enterprise value of data may result in undervaluing the future value of the whole company, and the importance of proactive and appropriate data management.
Three layers of data value and key points to consider
Currently, there is no single formula for placing a precise price tag on data, and several different frameworks exist. Genpact, a global professional services firm focused on delivering digital transformation for clients, recommends taking a structured approach to putting a value on enterprise data (2). The approach is built on three layers of data value:
- An organization can aggregate their information and make it available as a data-as-service model.
- Examples include aggregating payment track records and selling credit history information to institutions looking to support financial decisions. Retailers who have extensive data on their customers can also sell the information to manufacturers or other consumer good companies.
- An organization can combine a set of data with other information to get new value. An important aspect to consider here is how to derive the value of data before it goes dark, and which parts of big data might create a useful and valuable combination.
- extracting value by collecting data on the trends and patterns and applying machine learning to predict the likelihood of future events.
These three layers of data value—intrinsic, derivative and algorithmic—are important when it comes to unlocking the value of unused data. To do that, other key points to consider are the following:
1. The data operating model
Managing data efficiently is one of the most prominent challenges for any organization, and the larger the organization, the more data it usually operates with. For that reason, knowing the answers and the reality-expectation gap to the following questions is extremely important:
– How does the organization collect data? – What type of data does the organization collect?
This is the first step on the way to unlock the value of unused data since data that is not being collected cannot be used for creating value.
In addition, trends such as digitalization and customization have increased the demand for not only a better data quality but also new consumer-minded regulations on privacy, such as GDPR in the EU. Together these are forcing organizations to enact better controls over how data is created, transformed, stored and consumed across the organization. Therefore, a successful and user-friendly data operating model helps when converting the business strategy to a well-functioning operational design. A good operational design will enable deriving more value from data, since collecting data can result in more data as algorithms produce data about their own performance.
2. The data ecosystem
A good data ecosystem is needed to understand and govern the data. Data ecosystem consists of various technologies that help the organization to capture data and to store, process, analyze and visualize it to produce useful insights. This can help in analyzing and better monetizing the data.
Data ecosystem infrastructure
The rise in volume, velocity, and variety of data has led to the development of new technology that can process big data. Some examples are Hadoop, NoSQL, and Massively Parallel Processing, which can segment the data and allow for faster and concurrent data processing.
Data ecosystem analytics
Analytics platforms are especially important from the market analysis perspective. They allow tracking users and user cohorts to identify trends in user engagement, retention and conversion, and visualize how users explore the organization’s product, service or website. Analytics can also be applied internally: companies such as VMware, Saberr, and Humanyze use people analytics to make predictions on
affiliation and to improve the understanding of team dynamics. This can transform HR and every other business function.
3. Data platformization
A key role of data platforms is to pull data from multiple sources, and clean and harmonize it so that it can be used at the right time in the right format. This can be done by combining big data and AI functionality to enable real-time analytics which allows the organizations to react to significant changes without delay and improve the organization’s performance and increase accuracy and the speed of issue recognition which prevents unnecessary operational costs. Real-time analytics also aims to increase customer satisfaction by providing faster and more accurate simultaneous services to customers.
4. Data monetization
With a good data operating model, data ecosystem, and data platform, the application and monetization of data become easier. The ability of data to fuel data-driven technology such as AI, IoT and blockchain will help organizations drive disruptive changes that will directly benefit the organization and its customers by enabling unique services and products to customers either through (hyper) customization or by providing completely new services that weren’t possible before. One of the most novel applications of big data and dark data could be in the healthcare industry, where organizations could unlock non-clinical dark data to create hyper-personalized interactions that would generate tremendous positive value for patients and employers and themselves while saving in operational costs.
To unlock the value of their data, the companies must first know how their data is being collected, managed used and monetized.
Since data is one of the intangible assets with most unlocked potential, one of the biggest challenges companies face is data valuation and management that allows them not only to determine the value of their data today but what the data is going to mean to the company in the future. This is why all organizations should move to a more proactive approach in managing something that could prove to be one of their most valuable assets.