Data Monetization: An approach for unlocking data opportunities
The value of data is well understood across all industries, but today’s financial services firms are focusing more energy and resources on unlocking its potential than ever before. How they go about doing it differs widely with varying results. Based on their own experiences, Maria Hammargren, Prateek Kulshreshtha and Cian O Braonain provide a three-step approach organizations can use to monetize their information assets.
Strategies for Data Monetization
When it comes to increasing the value of data, there are a number of broad strategies that companies can use to monetize it. The preferred strategy or combination of strategies will shape the direction of the analysis to focus on either one or more outcomes.
The strategic opportunities for monetization can be classified into three broad categories: reducing cost, increasing revenue, and selling data.
Organizations will primarily realize cost reductions by increasing operational efficiencies. Using data to streamline technologies and infrastructure can decrease costs by improving efficiencies and eliminating duplication across systems. Data can underpin improvements in team productivity by optimizing processes, which can lead to lower workforce-related costs.
There are also opportunities to ease spending on data brought into a company. Firms can investigate different sources and explore whether in-house data or solutions are better suited to optimize data collection. Consolidating multiple Bloomberg feeds into a single service for all applications is one example.
Organizations with low-quality data often spend considerable money to fix and reconcile it across processes and divisions. Improving the quality of data and distribution can dramatically lower costs through better use of golden sources.
With a deeper understanding of data, organizations can gain and explore new business insights and revenue opportunities. Once data is analyzed, useful insights about customer behavior and purchasing trends can help drive more profitable services.
Matchmaking of services allows the organization to expand its potential client base by partnering with complementary organizations to offer a wider range of services not possible on their own. Data analysis can highlight potential partnerships that wouldnít otherwise be obvious.
What’s more, underutilized data needs to be investigated as it can be underutilized for a number of reasons: incorrect granularity, low frequency and reliability, or it is not combined with other data attributes to unlock its full potential.
Data is the most basic commodity to sell when looking to monetize your information assets. When data is exclusive, the potential for higher revenue exists as long as there is a market for it.
When data is too sensitive to directly sell, opportunities should still be considered because there could be value in anonymizing it. In addition to current data, historical data is also a viable candidate for potential revenue. For example, research from SocialFlow shows that each Facebook user is worth $14 due to annual advertising revenue highlighting the value of customer data1. Organizations can also explore the option of selling data services without selling the data itself.
Three Steps to Unlock the Value of Data
A disciplined, three-step approach will help organizations understand and obtain the most value from their data. Before diving in, an initial business model analysis will help set the stage and allow firms to better understand existing customer needs, current challenges and the strategies other similar industries have implemented.
A data analysis methodology can be used to ascertain the quality and potential of the data, which will help guide the enterprise toward one (or more) of the three data monetization strategies.
The third and last step involves estimating or quantifying monetary value by appraising the opportunity value for monetization, taking both costs and constraints into consideration.
Business model analysis
To maximize data monetization, organizations should first evaluate their current business model. A good understanding of the operating landscape will help steer an organization in the right direction as well as help it recognize the full context of its business potential and constraints.
The key areas firms should assess include:
Customer Behavior: Performing a behavior analysis helps to understand customers and their needs in more detail. The aim is to recognize what the customer is doing when interacting with the organization and why. It also identifies certain behaviors that can help predict similar patterns in the future or among other customer groups. For example, most major investment banks in Europe are refocusing on corporate clients who are looking for more flow business. A customer behavior analysis can help investment banks tailor and offer a larger spectrum of products and services to clients rather than a single product or service sold in isolation. The needs of base customers have not changed much, but it is important for organizations to understand how modern advances impact how their needs are met.
Company Structure and Governance: A company’s structure and governance is the link between people, skill sets and the desired output. This is a clear guide to the maturity and readiness of data. Companies should aim to have a disciplined data governance structure set up to help enforce a clear plan toward developing required skillsets and technologies, as well as secure budget and embed trust in the data quality framework. Having a strong data governance structure also facilitates giving the data back to the business to own and steward rather than relegating it as a technology asset.
Key Activities and Partners: Looking at key activities helps the organization understand what data is gathered, possessed and used throughout day-to-day activities. The firm’s key partners and suppliers will also be examined as well as any dependencies and SLAs in place. This provides visibility into the data types, usage and manipulation occurring, and offers a complete view of the internal current state of all data—not just the information deemed important by regulations or record keeping requirements.
Customer Relationships and Channels: In the digital age, customer relationships and channels form an important aspect of most business models. This includes how the company interacts with customers, levels of customer intimacy and self-service, as well as the overall benefits and value proposition for the customer. Naturally, all of these factors come back to the channels that the company sells through, such as a direct sales force, retail locations and ecommerce sites.
Companies need to stay current on how best to connect and interact with their customers. Some of today’s data sets and domains didn’t exist 10 to 15 years ago and organizations have to adapt to the evolving mechanisms of customer communication. The changing communication channels heavily influence the value that data sets can bring. Firms can leverage these new mechanisms to connect with clients to create more insightful and tailored products and services, which will ultimately drive new business.
Potential Boundaries: The potential boundaries an organization could be facing need to be taken into consideration. This includes every reason why a firm cannot use its data freely, sell data or explore new business streams or growth opportunities due to regulatory, reputational, ethical or other issues. Data strategies for monetization must be consistent with the firm’s appetite for reusing customer data.
Cost Structure and Revenue Streams: Organizations must assess their cost structure. Are the main costs data-related and if so, why? Are key resources required to keep expensive and inefficient processes alive due to data processing that is not optimized? Revenue streams need to be understood to know what customers are currently paying for and what value they would be willing to pay for.
Ideas and Strategies from other Organizations and Industries: Lastly, it is important to look at how organizations, both within the same industry and in different industries, are monetizing data. Lessons learned from other companies will help firms steer clear of known problems and risks as well as help them realize the potential of their own information assets. A solution for monetization does not have to be hugely innovative to be profitable. Parity with other industry participants can be effective, provided the firm invests in gaining a clear understanding of the full benefits of the competitors’ solution.
Once data is analyzed, useful insights about customer behavior and purchasing trends can help drive more profitable services.
Conducting a business model analysis helps clarify the context behind how and what existing data is generated and how it is used by the organization. It also sets the stage for a deeper analysis, looking closely at the overlaps and intersections of data sets to find areas of impact where the customer needs exceed the services offered. Output from the first step (business model analysis) provides an understanding of:
- Customer needs
- Industry-wide developments
- Assessment of how customer channels are evolving
- Competitor landscape
The data methodology contains six steps:
Inventory of Data Sets
The first step is to understand the existing data models. Creating a similar data set for the customer’s needs and competitor’s offerings reveals the gaps between the services the organization is offering versus customer need, competitor offerings and industry-wide developments. The following provides further explanation:
- This assessment of the services offered versus client needs highlights what customers are asking for and not being provided, as well as trends in the market that are not being followed. The organization could have existing data that will help the company extend its services to meet additional customer needs.
- Understanding the gaps between the firmís services and its competitors will help highlight threats, including disintermediation by advancements or industry changes.
- With a greater understanding of customer needs, firms can explore buying public data to address any additional needs and requirements.
Use Cases for Data Monetization
After completing the inventory of data sets, organizations can create potential use cases for the identified data models. Identifying and analyzing where the data sets overlap will highlight how firms can apply these elements in multiple areas or circumstances. Finding revenue-generating ideas from the changes invoked during regulatory compliance is an example of this process. Assessing the firmís data sets overlaid with the regulatory data set allows for other pain points within the business to be addressed concurrently. Organizations can get ideas by looking at how similar data sets from other industries have been used for monetization.
Feasibility of Data Monetization
Because of constraints around the usage of data for deriving monetary value, firms should conduct a feasibility study to see which scenarios fall outside of legal and regulatory boundaries and hence can be ruled out in an early stage. Such concerns should be documented upfront, as any possible opportunity is useless unless it meets legal, regulatory, organizational vision/framework and ethical/reputational criteria.
Once firms know which data sets are available and can be used without restrictions, the data needs to be transformed to a state where analytics can be run and insights generated. At this point, the organization will need to consider if more ownership, consistency or accuracy is needed.
Data Analytics and Building Data sets for Monetization
Analytics can now be performed on the prepared data to derive insights. Organizations may need to revise data sets and analytic models to provide deeper insights or to ensure that results are available in a timelier manner. This should be seen as an iterative process on the use cases.
Once monetary insights are available, it’s important to industrialize the process by formalizing data ingestion through production source systems. For example, a big data platform can be utilized to take live data at an enterprise scale and transform it into customized views that are suited for analytics. These views are then fed into a business intelligence (BI) tool capable of running analytics algorithms. The whole data science process needs to be operationalized in an agile way (think DevOps for Data Science) so that it becomes a continually evolving process that meets changing business needs.
Estimate the value of the data
The final part of the approach relates to assigning a monetary value to the data. Measuring and improving data quality takes time and effort; similarly, making data available from legacy systems comes with a cost. Understanding the regulatory and legal impact and any steps needed to comply with data protection regulations in certain jurisdictions also should be considered when calculating the total value that can be extracted from the data.
To understand the potential monetary value for capitalization, consider the equation below. Even if high-level estimates are taken, all variables in the equation must be represented in monetary terms.
This equation is only a starting point. With the insights gained in the previous stages, firms should customize the equation to their particular analysis. The opportunity value can be derived by using standard market-sizing techniques, and from the previous analysis, the constraints and costs to be subtracted from the opportunity value will be well understood.
Data has become a critical asset for organizations in their efforts to survive and thrive in today’s fast-changing landscape, with many turning to it to unlock opportunities for a competitive advantage.
The key to success is to holistically look at the company’s business model and operating environment before beginning to understand the details of their data. Analytics can only be meaningful if efforts have been made to understand the context of the environment—both from an organization and customer perspective.
Often, potential opportunities may seem too conceptual. By leveraging this disciplined three-step approach, firms can separate concept from reality and successfully capitalize on their information assets.
Maria Hammargren is a senior business consultant with Sapient Consulting based in London. She has worked in the financial services industry for more than nine years. Since joining Sapient in 2011, Maria has worked with multiple clients on a wide variety of projects, most recently centering on data and regulatory change. Currently focusing on data management and data analysis, she is working with industry participants on regulatory impact and advising banks on implementing best practices for data management.
Prateek Kulshreshtha is a senior business consultant with Sapient Consulting and has worked in key global financial centers, including London, Chicago and Singapore. He has worked closely with sell-side and buy-side firms in areas such as workflow redesign and regulatory audits. In the last few years, Prateek has focused on data issues in large global banks and has witnessed the tremendous impact of key data elements throughout the enterprise. He helps firms with disparate systems and processes unlock the value of their data resources.
Cian O Braonain is the Sapient Consulting European lead for data and transparency, specializing in regulatory compliance and the use of data management to improve transparency to regulators. With Sapient since 2000, he has successfully led a variety of complex regulatory initiatives across the globe. Cian’s recent focus has been on the adoption of data management best practices to reduce the burden of compliance while providing the infrastructure for revenue upside opportunities.