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ANATOMY OF A DECISION

Essential  Strategies  for  Building    Data  Monetization  Business  Models  

Published: December 2015 Report Number: A0195

Analysts: James Haight, Research Analyst; Hyoun Park, Chief Research Officer

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What You Need To Know With the digitization of everything and the corresponding explosion of Big Data, enterprises have engaged in a veritable data arms race. Leading-edge firms capture every data point they can in an effort to understand everything from customer buying habits to optimizing back-end supply chain and manufacturing performance.

For good reason, much of the conversation has centered on an organization’s ability to collect, store, and analyze data. But there is another side to the coin that organizations are taking advantage of as well: monetizing data assets. Blue Hill Research has identified a variety of ways in which organizations are monetizing their data to produce value-added products and services. These opportunities are expanding with technology advances that make data easier to capture and analyze – and more importantly, easier for non-technical users to distribute anddigest.

GoodData, a cloud-based business intelligence software provider, has taken a farsighted stance in investing in the realm of data monetization. To provide market clarification and guidance, GoodData has engaged Blue Hill Research to study how analytics customers have successfully monetized their data. In this report, Blue Hill Research deconstructs the decision-making process of why these organizations chose GoodData, and analyzes the resulting business success associated with their data monetization efforts.

Through the analysis of peer organizations, this report allows business leaders to identify situations that are similar to their own circumstances and to pinpoint opportunities to maximize the value of their present and future data assets.

Understanding Data Monetization It is important to shift the conversation of enterprise data and analytics beyond the initial ability to simply collect, integrate, and analyze corporate and external data feeds. As part of our regular research, Blue Hill Research has covered the value proposition of extensively bringing effective analytics into an organization. There is a real and

AT  A  GLANCE  

Studied  Organizations  

• Mavenlink• MediGain• Mindflash

Business  Opportunity  Competitive  pressure  to  differentiate  and  unlock  internal  data  assets  into  packaged  and  value-­‐adding  insights  to  customer  base  

Solution  Selected  

GoodData’s  “Powered  By”  data  analytics  offering  

Business  Impact  

• Support  for  premium-­‐pricedproduct  offerings

• Solidify  unique  position  inmarketplace  to  drive  customeracquisition  growth  

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measurable benefit to shifting to data-driven decision-making that has financial impacts both on top-line performance as well as organizational efficiency.

However, as organizations mature in their use of data, they have the opportunity to expand the value of analytics beyond internal decision-making. In the race to track and quantify every business operation from production line to web traffic clickstreams, organizations have invested significantly in creating repositories such that data can become a corporate asset. As organizations move into the realm of data monetization, they are opening up new pathways to leverage value from this asset.

At the core of data monetization is packaging together insights to deliver value-added services to existing and net-new customers. The primary and most direct avenue for data monetization is selling a proprietary data source as a standalone product (such as selling hyper-accurate weather data as a subscription service to airports), but there are a variety of additional options companies can pursue to translate data to revenue. From a high-level perspective, data monetization revolves around tying data into broader service offerings and gaining a competitive advantage in the process.

Blue Hill Research has seen data monetized in a number of ways, including garnering a price premium through introducing new functionality into a product offering, bringing net-new services to an existing customer base, or entering new customer segments.

In general, there are three key paths to value. The first is through direct generation of revenue, where data is actually sold as a standalone service, insight, or recommendation. Second, data can be used to support customer retention, which is increasingly important in subscription-based and service-based economies where relationships and proactive service are increasingly important to maintain and extend customer lifetime value. A third way of monetizing data is in improving geographically dispersed relationships and improving the personalization associated with a good or service. By doing so, a vendor can maintain a one-to-many relationship while providing the “many” with the personalization that companies seek.

A number of cloud and analytics innovations have made monetizing data assets easier and more accessible. A new class of solutions has emerged to allow insights from data analysis to be found, disseminated, and digested by non-technical users. In today’s world, one does not need to be a trained data analyst to utilize data in driving their decision-making processes. These innovations have lowered hurdles that have traditionally precluded front-line business employees from taking advantage of data analytics. As a result, organizations can disseminate insights in novel ways that broaden the opportunity to productize data and increase productivity.

Blue Hill Research has identified core characteristics of organizations that effectively monetize their data assets. Chief among these characteristics is the data’s relevance to operational decision-making of prospective customer bases. Many organizations do not believe that they are in a position to monetize data assets because they think

Blue  Hill  Analytic  Adoption  Research  

To  further  understand  the  value  propositions  of  bringing  effective  analytics  into  an  organization,  Blue  Hill  recommends  the  following  documents:  

• Chasing  Sales  ROI:  ThreeHigh-­‐Impact  Use  Cases  for  SalesAnalytics

• The  Business  Analyst’s  NextFrontier:  Advanced  Analytics

• Fundamental  Shifts  inInformation  Management

• New  Revenue  Opportunities  atthe  Intersection  of  IoT  andAnalytics

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that they are not producing proprietary data that can be packaged and sold as a subscription service. But Blue Hill Research has seen that any organization with access to customer usage and customer interaction data has the potential opportunity to collect and distribute proprietary data that may be meaningful to another market. Particularly, if an organization services a large number of customers with similar objectives, there is a tremendous opportunity for organizations to analyze behavior patterns across their customer base. In doing so, they can understand best practices and benchmark statistics that are indicative of their most successful customers. This data has tremendous value to customers looking to improve their own operations.

Blue Hill Research observes broadly the core characteristics of what makes data assets a good fit for monetization initiatives.

Figure  1:  Blue  Hill’s  Key  Traits  for  Successful  Data  Monetization  

Source:   Blue   Hill   Research,   December   2015  

Business decision-makers should cross-reference the data that they have available within their organization in conjunction with data that they could easily obtain via a change in measurement or processes against these characteristics. Doing so will identify potential areas of low-hanging fruit where data could be packaged and delivered as a value-added service.

Moving from Theory to Practice Blue Hill Research has observed a broad spectrum of organizations that have successfully used the distribution of analytical insight to monetize their data. To augment aggregate context and best practices of these observations,

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we present observations obtained from deep qualitative interviews with three organizations that have a history of success in this endeavor: Mavenlink, MediGain, and Mindflash. Mavenlink provides a project-oriented delivery system to support professional services industries such as marketing, IT services, architecture and engineering, and business and management consulting. MediGain is a billing and revenue cycle management software vendor focusing on healthcare providers such as physician practices or hospitals. Mindflash is an online learning management provider that supports online courses or training modules. This includes use cases such as building out and distributing HR training material, and training customers or partners.

Organizations’ paths to monetizing data can take a number of forms. In some cases, organizations have capitalized on their data through manual, static and labor-intensive processes such as consulting arrangements or one-off custom reports. In other instances, organizations look to start this process from scratch with an eye towards scalable and repeatable platforms and processes. Mavenlink, MediGain, and Mindflash represent each of these journeys. To better understand the corresponding drivers and outcomes of this journey, Blue Hill Research expounds upon the opportunity identification, solution selection, and investment impact associated with choosing GoodData to pursue data monetization.

Three Models for Unleashing the Value of Data Mavenlink quickly found that in providing project management capabilities, clients were not simply looking for canned project reports, but sought custom reports and business intelligence as a service to support their specific business needs. Because project managers cannot hide from any aspect of the project and need to keep a firm hand on budget, resources, timing, and risk, they need reports across all aspects of project planning, management, accounting, and management.

From internal and venture firm meetings, Mavenlink determined that the tipping point for buyers to maximize utilization of the project came from providing strong analytical capabilities that would support both operational intelligence and executive visibility across the project portfolio while keeping specific features separate. As an example, project managers must manage specific costs associated with a project, while directors and executives may be more focused on the margins associated with each project that drive profitability. Based on this need, a key consideration for Mavenlink was providing this level of granular experience while using a solution that was easy to provision was with the end goal of maximizing usage, adoption, and revenue.

MediGain allows healthcare provider offices to outsource their back-office financial services and billing operations. In the last five years, there has been an explosion in the complexity of these processes as many new service providers entered the healthcare field, such as electronic health record (EHR) and practice management vendors. MediGain recognized a significant pain point for their customer base, and knew that there was a need to ease these obstacles for their clients.

MediGain saw an opportunity to provide a client dashboard that would provide much-needed visibility and clarity into back-office operations, as well as standards and guidelines for their clients. If MediGain could aggregate, distill, and disseminate insights about best practices (such as optimal treatment mix or revenue per

 

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patient type), their clients would benefit. In providing this additional layer of insight, MediGain realized that it could make the transition up the value chain from “process outsourcer” to “provider of strategic intelligence.”

Mindflash has the ability to track usage data for all of their clients as they deploy their online-based learning and training curricula. As such, Mindflash recognized that these aggregate data points could provide value back to their individual customers. For instance, understanding the optimal content length for maximum participation or typical rates of correct/incorrect answers for test questions allows Mindflash customers to better capitalize on their investment. Broadly, Mindflash was looking for an opportunity to move upmarket and further differentiate itself through expanded functionality and tiered pricing. They knew that packaging these aggregate insights and delivering them in a consumable manner would be a catalyst to fueling the proposed company-wide evolution.

Solution Selection Criteria for Data Monetization The driver for monetizing data is clear – every company would be glad to make more money while doing the same amount of work. What is less clear is how to choose the avenue through which best to achieve this data monetization. Organizations face the option to build, buy, or partner to bring necessary functionality to its customer-facing intention. However, the majority of organizations that Blue Hill Research observes – and specifically in the case of the three highlighted organizations – believe that building and/or buying functionality presents an unnecessary and unjustified distraction from the firm’s core competencies. Because of this internal accounting of resources and effort, companies seeking to monetize data quickly to seize market opportunities typically partner with dedicated solution provider as the fastest path to move from concept to concrete functionality.

In the case of MediGain, the firm had recognized this opportunity early on, and tried to solve the identified pain point by hiring more personnel. They were using a man-hour intensive process of aggregating records from disparate practice management and records systems to provide a unified report on a monthly basis. As a firm, they had a staff of 20 employees (including a number of business intelligence developers) consolidating data from over 400 practice management solutions. The end results were time-intensive and static reports. To scale this service offering, MediGain understood that a dedicated analytics provider was the most compelling path to value.

In the process of choosing the right partner, GoodData was identified specifically because of its ‘Powered By’ offering. GoodData introduced the Powered By product line to target organizations looking to embed analytics offerings into their own products. Fundamentally, data monetization is about aggregation, analysis, and distribution of digestible insights. Core selection criteria for both MediGain and Mindflash revolved around the ability to deliver these insights in a frictionless and streamlined view for their own customers. Blue Hill Research observed the following underlying factors that contributed to the selection of GoodData:

   

 

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Figure  2:  Key  Selection  Criteria  for  GoodData  as  Data  Monetization  Solution  

 

Source:   Blue   Hill   Research,   December   2015  

GoodData stood out because of its ability to bypass the two largest obstacles in data monetization efforts: aggregating and analyzing the data, as well as delivering the analysis within an embedded experience. Because of GoodData’s roots as a cloud-based business intelligence offering, the solution has a firm grounding in directly connecting to disparate data sources and uniting data into a single accessible environment for analysis without additional downloads or subsidiary software. Moreover, GoodData presented an opportunity to automate data analysis and distribution workflows, which allowed for further time savings. Because GoodData specializes in these use case types, and offers a dedicated product line, the organizations were able to realize cost savings while incurring minimal implementation costs from professional services. As an example, Mavenlink already supports 26 specific interactive reports as well as a custom report builder across over 300 metrics to support professional services.

Each organization seeks to broaden the scope of their analytical insights for their customers. For example, MediGain is considering allowing their customers to have self-service access to reporting to drill in and ask questions. Because GoodData core software is as a business intelligence provider with self-service capability, the organizations perceived self-service capabilities as a logical extension of GoodData’s offerings.

Structuring the Value of Investment The decision to apply GoodData as a solution to effectively monetize data assets resulted in persistent and significant gains. Table 1 summarizes the value additions that Blue Hill Research observed.

 

 

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Table  1:  Monetization  of  Data  Assets  

Use Case Outcome Manifestation of Value

Mavenlink Providing 26 interactive reports across budget, profitability, resource utilization, time tracking, and project detail

Support a custom reporting engine for super users seeking to build reports from 300+ project metrics

Improved forecasting of project outcomes and greater control over project profitability

Reduced TCO from resource utilization

Elimination of manual data analysis and transformation

MediGain Provide automated updates of financial performance to customers

Deliver comparative performance to industry best practices/benchmarks to customers

Developed unique competitive advantage in marketplace

Scalability through displacing significant manual processes

Advanced in the value chain as a provider of strategic insight

MindFlash Provide best practices/benchmark analysis as part of premium service offering

Ability to support higher priced premium offering

Appeal to broader spectrum of customers and expanded service offerings

Source:   Blue   Hill   Research,   December   2015  

While these organizations have not directly sold analytic capabilities as a standalone product or service, the revenue impact of their efforts was meaningful. As an example, Mavenlink believes analytics represents a strategic advantage in serving project-based professional services organizations seeking better accounting, forecasting, and decision-making across scattered and dispersed project components.

For MediGain, the packaged insight they provide to their customers is now part of their standard service offerings. This level of insight provided to clients is unique in the marketplace, and is a pivotal driver in winning deals to acquire new customers. Further, the scalability that GoodData introduced allows the firm to deliver superior insights to its previous services while spending less time seeking analytic realizations. This has allowed them to pursue and service net-new accounts that further contribute to their financial performance.

Mindflash, as a company, made the transition to a multi-tiered and differentiated service-offering approach. The additional packaged insight that GoodData delivers to their clients provides sufficient value to support and justify a higher-priced premium offering. This has allowed Mindflash to better serve customer segments, expand the scope of services that they offer, and drive additional revenue.

 

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Blue Hill Recommendations for Data Monetization As a starting point, businesses working on monetizing business data must consider macro trends as technological innovations and business mindsets continue to evolve. For instance, the emerging Internet of Things presents an opportunity for organizations to quantify business operations and customer interactions in novel ways. For instance, a shipping and logistics company using sensors to track their assets might now find themselves collecting valuable data that they could package and deliver to their customer base, or they might find that a curated data set of logistics information might be a valuable subscription to sell to an interested third party. To unlock this potential value, Blue Hill Research advises that organizations should take inventory to categorize what data assets might be of value to either their existing customer base or potential new customers.

As more organizations recognize the value of data as a tool for enhanced decision-making, there will be increasing demand from customers for more data from their service providers. Those that can deliver insight in a digestible medium will be well-positioned to leverage their internal data assets to produce more customer value and ultimately value for the firm as well.

In light of these business trends, Blue Hill Research advises the following recommendations on a departmental level for organizations to effectively monetize their portfolio of accumulated insights.

Executive management: Executives are in a position to identify opportunities to provide new services or to improve customer retention and lifetime value. Significant resources have been invested in infrastructure to capture, aggregate, integrate, and analyze the data points that business operations produce. However, these analytic resources are underutilized and undermonetized because comparatively few resources have been spent to transform this data into externally facing products. Shifting the mindset from using data for internal decision-making to creatively applying data to produce new market facing products is an important first step. Consider your existing investment in data and analytics to be a business asset that should be utilized. In that light, calculate or estimate the current Return on Data Assets (RODA) for your data and analytics investments and how strategic business decisions can improve that metric.

For line-of-business professionals: In productizing and monetizing data, product and marketing teams must consider the following aspects of data and analytic usage. First, line-of-business managers should consider the value of benchmarking customers on an anonymized basis. Any vendor supporting a specific departmental or vertical function with cloud-based analytics has the opportunity to aggregate and benchmark its clients, which can be valuable both as a contextual service for existing clients and as a standalone data service for external stakeholders.

Second, consider a tiered offering model for providing analytic capabilities to employees, partners, and customers. This does not mean holding back functionality for stakeholders, but to provide dashboards, benchmarking, indexing and metadata management, and self-service as separate functions. This separation both allows companies to avoid overloading potential end users with too much functionality at once and allows the business to segment and configure specific offerings to specific individuals. For instance, strategic analytics consumers

 

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may prefer benchmarking and indexing capabilities to track trends while sales managers or channel partners may prefer self-service and dashboarding capabilities to view progress on a daily basis.

Product manager, marketing, sales, and service professionals should also consider the value of measuring customer engagement associated with a product and centralizing these data and analytics within a broader corporate context to support a 360-degree view of the customer. Standalone product usage analytics simply represents a silo of information that needs to be integrated with everything from sales and marketing opportunities to product and application development cycles to service and bug-fixing efforts. As solution adoption, technology utilization, customer loyalty, and customer lifetime value replace legacy metrics and customer traits such as vendor lock-in, asset-based sales, and long-term sales, it becomes increasingly important to poll and track customer sentiment and product utilization on a frequent basis simply to maintain business.

For IT professionals: The true monetization of data can only occur with the appropriate infrastructure to ensure data integrity and quality when delivered to end customers. However, the integration, embedded experience, and guided visualizations that are necessary to deliver consumable (and thus monetizable) data assets are often not found in traditional internal data analytics endeavors. As such, line-of-business professionals must work with IT to enable a consumer-grade user experience with enterprise-class data quality and standards. This does not mean simply creating a customer-facing dashboard, which may provide marginal short-term value. Rather, persistent delivery of quality value to the customer base requires a well-considered data supply chain that flows from secured and curated sources and is delivered through a guided and intuitive experience. Analytic solutions need to support an individualized and secure data environments that allow each user or each customer to experience a customized and contextualized experience. This context cannot be compromised by the fear of poor security or data governance. Rather, IT needs to focus on choosing analytic platforms that are both massively scalable and specifically governable and malleable to support individual preferences.

This combination of scale and context presents a challenge to the enterprise as the skills to accomplish this objective are often siloed between the customer-facing line-of-business and product managers on one side that demand unique experiences and IT professionals building analytic infrastructure to a single standard. As such, IT needs to seek solutions that can manage change and governance at scale through the ability to manage specific accounts, features, administrative settings, data access settings, and self-service preferences. This allows IT and business managers to collaborate and develop a scalable structure to handle new productized data opportunities as an organization collects and analyzes more data over time rather than cripple either the line-of-business need to innovate or the IT need to secure, govern, and guarantee high levels of service.

Conclusions and Key Takeaways As organizations continue to increase the amount of data that they collect, and build on their ability to process that data, they are laying the groundwork for the next phase of garnering value from their data. Savvy organizations are looking at the collections of data that they have, and are identifying ways to leverage these assets beyond just improving their own decision-making processes. These organizations are directly monetizing their data through repackaging their insights and delivering them to their customer base.

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As shown by Mavenlink, MediGain, and Mindflash, monetizing data assets does not solely consist of selling data subscriptions as a net-new and standalone product offering. Rather, companies should also consider the opportunity to deliver curated insights to customers directly within existing product portfolios and leverage this added value to either support increased premium pricing or to establish an undeniable competitive advantage in the marketplace.

In pursuing opportunities to monetize data, choosing the right avenue is critical in setting up the initiative for success. In our research, Blue Hill Research finds GoodData to be well-suited for the particular challenges and nuances associated with taking internal data and delivering it as a value-producing asset to external customers. We offer these observations so that business leaders can identify commonalities between peer organizations and identify the opportunities for their own organization to leverage existing corporate data assets for higher use.

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ABOUT THE AUTHOR

James Haight Research Analyst

James Haight is a research analyst at Blue Hill Research, focusing on analytics and emerging enterprise technologies. His primary research includes exploring the business case development and solution assessment for data warehousing, data integration, advanced analytics, and business intelligence applications. He also hosts Blue Hill’s Emerging Tech Roundup Podcast, which features interviews with industry leaders and CEOs on the forefront of a variety of emerging technologies. Prior to Blue Hill Research, James worked in Radford Consulting’s Executive and Board of Director Compensation practice, specializing in the high tech and life sciences industries. Currently, he serves on the strategic advisory board of the Bentley Microfinance Group, a 501(c)(3) non-profit organization dedicated to community development through funding and consulting entrepreneurs in the Greater Boston area.

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ABOUT THE AUTHOR

Hyoun Park Chief Research Officer

Hyoun Park is the Chief Research Officer of Blue Hill Research, where he oversees day-to-day research operations, delivery, and methodology focused on vendor and technology selection. In addition, Park covers analytics and enterprise mobility technologies as a noted advisor, social influencer, and practitioner. Park has been named as a top 10 Big Data, analytics, and mobility influencer including quotes in USA Today, the Los Angeles Times, and a wide variety of industry media sources. Over the past 20 years, Park has been on the cutting edge of web, social, cloud, and mobile technologies in both startup and enterprise roles. Park holds a Masters of Business Administration from Boston University, and graduated with a Bachelor of Arts in Women’s and Gender Studies from Amherst College.

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For further information or questions, please contact us:

Phone: +1 (617) 624-3400 Fax: +1 (617) 367-4210

Twitter: @BlueHillBoston LinkedIn: linkedin.com/company/blue-hill-research Contact Research: research@bluehillresearch.com

Blue Hill Research offers independent research and advisory services for the enterprise technology market. Our domain expertise helps end

users procure the right technologies to optimize business outcomes, technology vendors design product and marketing strategy to achieve

greater client value, and private investors to conduct due diligence and make better informed investments.

Unless otherwise noted, the contents of this publication are copyrighted by Blue Hill Research and may not be hosted, archived, transmitted,

or reproduced in any form or by any means without prior permission from Blue Hill Research.