Chapter 9
Best Practice #8
Leverage Analytics for Data Monetization

“California’s consumers should have a share in the wealth that is created from their data.”

California Governor Gavin Newsom

Today most companies have access to large volumes of data related to their operations, compliance activities, business entities like suppliers, customers, and competitors, and much more. The enormous amount of data that is collected on business operations offers possibilities for monetization. Data monetization is the process of leveraging data to generate monetary value from raw data or data-based solutions. According to Gartner, data monetization is using data for quantifiable economic benefit. This can include indirect methods such as improving business performance, leveraging beneficial terms or conditions from business partners, information bartering, productizing information, “informationalizing” products, or selling data outright [Gartner, 2020].

Is data monetization a new concept? Although still nascent in some industries, data monetization is already prevalent in many industry sectors, especially those sectors that are in the B2C (Business to Consumer) segment. Today, companies such as Facebook, Amazon, and Google derive a significant portion of their revenue from the user data they have captured. These companies harvest massive amounts of data about their users and then provide this data for a fee to the advertisers. This is a classic or traditional case of data monetization from consumers. Is this ethical and legal? It might not be ethical for some people, but as of today, it is legal. However, California’s Governor Gavin Newsom proposes “a new data dividend” that could allow California’s consumers to get paid for their digital data from companies such as Facebook and Google. The data dividend proposal follows the California state legislature’s passage of the data privacy bill, granting consumers specific rights related to their personal data [CNBC, 2019]. It is not just the consumer or user data that can be monetized. For example, today monetizing car data is a common data monetization topic as cars generate various types of data such as how they are used, where they are and who is behind the wheel. As a result, several players associated in the automotive industry try to turn car data to create data related products and services. Mckinsey finds that the global revenue pool from car data monetization could be as high as US$ 750 billion by 2030 [Mckinsey, 2016].

SPS Commerce, a retail analytics firm, provides cloud-based supply chain management software in a digital network that includes more than 90,000 retail, distribution, grocery, and e-commerce companies. SPS collects retail PoS data from retail chains like Walmart, Loblaw, Amazon, and Costco. SPS then monetizes this data by converting the data into insight reports on product sales and supply chain operations and selling the insight reports to CPG companies like P&G, Nestle, Pepsi, Kraft Foods, and Coca-Cola.

Why is this a best practice?

As discussed earlier, the three key purposes of data in business are to support the business in operations, compliance, and decision making. Why should a business enterprise monetize its data? What are the drivers to use data other than its core purpose which is using data for insights, compliance, and operations? At the highest or strategy level, data monetization in business helps in increasing revenue, reducing cost, and mitigating risk. But at the tactical level, there are three main reasons for pursuing data monetization in business.

Firstly, data monetization refocuses the business enterprise to accomplish its primary goal - maximizing returns for the investors. Even in today’s world of CSR (Corporate Social Responsibility) and TBL (Triple Bottom Line), maximizing profit remains one of the core objectives of running a business. Data management fundamentally is a tactical endeavor, and data monetization can link the strategic goal of maximizing profit for the shareholders to the tactics. Given that data is a business asset, companies should look at every possibility of harnessing this intangible business asset for better financial results. Secondly, data monetization helps to optimize asset performance as efficient asset utilization is one of the core functions of the business. Even though we see a new data-centric economy, data is still an under-utilized business asset in most companies. Historically, companies have managed tangible assets such as land, plants, equipment, and inventory. In today’s digital world, data is a new asset that companies must manage well. However, most companies are leaving money on the table, with only one in 12 companies monetizing data to its fullest extent [Gandhi et al., 2018]. Thirdly, focusing on data monetization improves business efficiency. Managing data is an expensive process. Unfortunately, many companies are collecting data without a clear objective. Forrester says 73% of the data collected in business is never used for any strategic purposes [Gualtieri, 2016]. If the data collected by the business is not monetizable, data can even become a liability, as seen in the cases of Equifax, CapitalOne, and Target. So basically, focusing on data monetization will position the business to become efficient, thereby reducing its SG&A (Selling, General, and Administrative) expenses and minimizing the risk in business operations.

Realizing the best practice

Today monetizing data effectively— can be a source of competitive advantage in the digital economy. While companies such as Facebook, Amazon, and Google leverage user data for a fee from advertisers, all enterprises cannot turn their data into a monetizable asset in this manner. According to data monetization expert, Doug Laney, “Managing information as an asset involves applying traditional asset management principles and practices to information. This can involve adapting physical, financial, human capital, or other asset management methods. And measuring information as an asset is about gauging an information asset’s quality characteristics, business relevance, impact on KPIs, along with applying traditional accounting valuation methods.” [Laney, 2017].

Fundamentally data monetization is solving a business problem using data for improved business performance. The monetary value could come from creating new revenue streams, or it could be in the reduction in the cost of operations, savings in the time spent in running the business, or in mitigating risk in business operations. In this regard, there are three primary paths to data monetization in business enterprises:

  1. Data architecture
  2. Embedded analytics
  3. Data products

Data architecture

In today’s digital economy, business enterprises strive to maximize the value of data for improved business performance. Data architecture is a key enabler for an enterprise to become data-driven. It is the practice of designing, building, and optimizing data-driven systems by incorporating the company’s vision, strategies, business rules, standards, and capabilities to manage the data. While many progressive and proactive organizations have the data architecture capability within the CDO (Chief Data Officer) function, there are still some organizations that are yet to take that plunge. This section looks at the importance and needs for a good data architecture in a business enterprise for effective data monetization.

Why should the business care for a data architecture? How is data architecture tied to data monetization? Data architecture looks at data monetization for improved business efficiency by offering solid strategies for companies to effectively manage their data across the entire DLC. Specifically, data architecture offers three key benefits to the business.

They say, “start with the end in mind”; one cannot build a sky-scrapper without an architecture. In today’s data-centric business world, the digital journey for the business starts with a solid data architecture - the foundation for data monetization for a sustainable competitive advantage. So, what is contained in the Data Architecture? According to TOGAF (The Open Group Architecture Framework), the Data Architecture should at least comprise of two key elements [TOGAF, 2020]:

Embedded analytics

The second data monetization capability is on realizing Embedded Analytics (EA). EA primarily looks at data monetization based on the time saved in consuming the insights in business operations. In most business enterprises, BI applications and transactional applications are entirely separate systems. This forces users to switch between the two IT systems; the user accesses the BI system to get insights and then uses the transactional system(s) to act. Multiple applications are used to derive insights and act. This results in a significant amount of time consumed to access insights and act. Nucleus Research estimates that switching from the transactional system to the BI system to get business insights wastes up to two hours per employee each week [Moxie, 2016].

It is not just the employee time wasted; it is also the lost business opportunity due to the cost of delay (CoD). CoD is a metric that measures the impact of time on the outcomes. In short, CoD is the leakage in value over time. CoD is closely tied to the opportunity costs, which is the loss of potential gain from other alternatives when one alternative is chosen.

This is where embedded analytics comes into play. EA, which is the integration of insights within the transactional applications, so users can work closely with the insights in the transactional applications they use every day. Technically, EA is the insertion into the UI (User Interface) of the OLTP (Online Transactional Processing) system or the SoR (System of Record) the insights from the SoI (System of Insight) systems. Apart from the UI design, embedded analytics relies on API (Application Programming Interface) and Identity Management (IDM) functionalities for seamless consumption of insights in the transactional or SoR systems. API is a software intermediary that allows two systems to talk to each other. IDM describes the management of individual identities, their authentication, authorization, roles, and privileges.

Below is a simple example of EA, which is geo-triggered or location-based push notifications which are triggered when a user physically moves near a Sephora store –a personal care and beauty store [MacFarlane, 2019]. These notifications, which are often done through either geofencing or beacons, offer personalized, timely, and location-based push campaigns based on the insight that the user is physically close to the store.

Data products

The third data monetization capability is to develop data products. A data product is the application of data for improving business performance; it is usually an output of the data science activity. There are three types of data products.

Building a data experiencing data product is relatively easy compared to building data enhancing or data exchanging product as the control and influence is usually inside the organization, and the impact can be seen quickly. While data experiencing data product will not create new revenue streams for the company, it will potentially improve efficiencies, expose value leaks, and reduce operational risks for the company.

According to Gartner, during the fourth version of the CDO, CDO 4.0, businesses should focus on data products, and on managing profit and loss using data products instead of just being responsible for driving data analytics projects and programs. Data products scale data and analytics capabilities, hiding the complexity in data management and other organizational constraints and offer the opportunity to deliver transformational value to the enterprise.

Building data products are based on two key elements: strong value proposition and high-quality data. The strong value proposition comes from looking at the entire value chain holistically and identifying value leakages. Value leakages typically happen when there is a hand-over or transition from one value stream in the VSM, and the transition often results in the misalignment of processes, peoples’ skills, KPIs, and data. VSM was discussed in chapter 6.

The second element in building data products is associated with high-quality data. As discussed before, high-quality data means the data that is used in the data product is subject to the 12 key data quality dimensions discussed in best practice #4. The high-level steps in building a data experiencing internal product for a business enterprise is shown in Figure 9.3.

Typically, when building data products, there will be compliance issues involved in data monetization such as privacy and security. The compliance issues pertaining to the data can be addressed using data manipulating techniques such as encryption, anonymization, scrambling, tokenizing, and masking which will potentially remove the sensitivities in the data.

Conclusion

Today, almost all companies want to leverage data for building sustainable competitive advantage. Data will not give a company that competitive advantage. The company needs to position the data for competitive advantage. But how? Companies must treat data as a valuable business resource by focussing on three key elements – value, rarity, and non-substitutable, which was discussed in chapter 1. If these three core elements are not built in the data strategy, the data will not offer that competitive advantage and the capability to monetize data.

Fundamentally data monetization is not just making money from data; it is a new way of thinking for enhanced business productivity. It is about creating a sustainable competitive advantage for the business. However, data monetization is at an early stage of adoption in most companies. Enterprises are beginning to see that the benefits of data monetization are many—from creating new revenue streams, development of new services, quicker time-to-market, reducing the cost of business operations, and minimizing risk. However poor data quality and data compliance issues represent the two biggest obstacles to monetizing data. Companies that find ways to address these two challenges, will be able to better monetize their data and provide more value to their stakeholders.

References