Monetizing Data: Identifying and Capturing ROI on Analytics crystallizes input from 17 Canadian analytics practitioners, executives, suppliers and other experts to deliver a practical and compelling guide to capturing returns on data and analytics initiatives. With the report, the V2V community has established a framework that practitioners, executives and entire organizations can use to optimize the use of data in their operations.
The report is structured in three main parts. The first, “Identifying the sources of data value,” explores four scenarios for monetizing data: use of data to inform immediate/direct/tactical actions, use of data to improve processes, control costs and/or drive efficiency and productivity, use of data to inform strategic/corporate decisions, and direct data monetization – models for selling data directly, or analytics products/services based on data. As the graphic below illustrates, the first three approaches operate on different timelines and offer different magnitudes of potential benefit. Direct data monetization stands alone on the chart: while there has been a great deal of thought dedicated to monetizing ‘exhaust data,’ the working groups found that most direct data monetization is done by firms specializing in data or embedded-data products or services; these business operations vary widely in both payback period and revenues/returns to shareholders.
The second section of the Monetizing Data report, “data monetization best practices,” provides both a building-block and a process cycle view of how analytics professionals and their organizations can extract tangible value from data. The third section, “metrics used to evaluate data monetization,” identifies four types of metrics (financial marketing, industry-specific and technical) that can be used to quantify the return on analytics., begins by examining the challenges associated with formulating the ‘right question’ and securing the “right data” to support a credible response. Discussion on this topic helped illustrate why this issue was identified as a key step in analytics practice: both the question and the data are influenced by several factors, and each in turn has an impact on the other.
Report launch: June 26 Meetup
As is the case with all V2V research, the Monetizing Data report was launched at a meetup held at Information Builders’ Canadian headquarters in downtown Toronto, and provided attendees with a chance to delve into different perspectives on the core topic, as well as extensive opportunities for one-on-one or small group discussion and networking.
The meetup kicked off with a session on “The management perspective: business expectations for data monetization.” Robert Eckersley of Information Builders acted as host for a discussion with Kartik Mathur, Senior Portfolio Manager at Bank of Nova Scotia, and May Chang, Interim EVP at Joseph Brant Hospital.
Eckersley began by asking his guests how ROI on analytics projects is measured and communicated in their environments – in dollar terms, process improvements or other benefit categories. As might be expected, Chang and Mathur had different approaches to this issue: Chang, speaking from a healthcare perspective, noted that a dollars-only discussion will ‘turn off’ physicians, while innovations focused on improvements in quality of care will drive interest and buy in (and ultimately, contribute to more efficient, cost-effective operations); Mathur said that a financial institution will look at both direct monetization (e.g., fraud reduction) and indirect monetization (for example, better or faster decision making) as evidence of return on data analytics investments.
At the conclusion of this discussion, the meetup moved on to address “The practitioner perspective: identifying, driving and delivering data monetization.” This session, moderated by InsightaaS Principal Analyst Michael O’Neil, featured four leading experts: Sarah Sun, who has moved from the financial sector to become Chief Data Strategist for pioneering resource sector supplier Goldspot; AI entrepreneur Francis Jeanson, who founded I AM OPEN after several years in analytics management with the Ontario Brain Institute; Achille Ettorre, who works internationally with the International Institute for Analytics and in Canada with Queen’s University’s Smith School of Business, and who is widely known from his time as Senior Director of Finance at Loblaws, and Data Evangelist Ganesh Iyer of Information Builders.
The panel was asked to address three questions: where do you look for ROI on analytics, what do you consider to be best practices in monetizing data, and what timeframes, metrics and expectations should be used to capture and communicate the benefits of data monetization? These are complex topics, but the group was able to connect answers across the three areas to describe practices and processes that are appropriate in a variety of business contexts.
Sun began by talking about the progression in approaches to data monetization and management expectations of analytics impact and returns, spanning all four points shown in the Figure above. She used examples from both her financial services background and from her current role at Goldspot; in one scenario, she described a process that started with tactical input (customer satisfaction analysis), and then moved to “harder to sell” process improvements – data-driven approaches to reducing costs and capturing efficiencies, which had the effect of changing processes and jobs, and of creating a degree of organizational resistance. The third step in Sun’s example was connecting data to corporate strategy – identifying the ability to use data to identify where gold is – which in turn led to direct data monetization in the form of using this core capability as the basis of a service offering.
Ettorre raised a number of best practice observations in his commentary, and also offered an example, of merging substantial operations from two companies into a single entity. He noted that in this scenario, amalgamating data is a challenge: governance processes need to be established to get beyond differences in data and cultures, to identify and focus on best value sources, to appropriately highlight risk and to quantify the payback potential associated with different action areas– all while ensuring that the analytics team can deliver some quick wins to keep the board engaged with the process. Ettorre urged meetup attendees to start this process by getting “a feel for the organization, the team,” noting that success requires “more than buy-in from the top.” Once this groundwork is in place, analytics management can proceed with an approach emphasizing ongoing review and alignment of people (investing in training, changing staff where necessary), processes that extend across business units, and which are simple enough to elicit consistent behavior, and the necessary technology – which, according to Ettorre, should bracket analytics initiatives, with IT engaged early but the technology itself positioned as the last element in the strategy.
Iyer had perhaps the most difficult role on the panel, asked to explain how the specific insights raised by his panel colleagues mapped to his experience with the wide range of leading-edge firms that Information Builders works with. He reinforced the observations offered by Sun and Ettorre, noting that practitioners “have to start with the immediate, tactical actions – but have to move to a strategic discussion as well,” and added that, as per comments made by Jeanson (and Chang in the earlier discussion), public sector organizations often have their own definitions of monetization. Pressed to identify characteristics that separate organizations that are successful in data monetization from those that struggle, Iyer noted that in many cases, less-successful firms have not invested in building foundational internal capabilities (using, as an example, a prospective customer that wanted to implement an AI solution “when data was all over the organization and Excel was their analytics package”). To build a viable approach to data monetization, Iyer believes, organizations “have to have a data management platform,” and have to be realistic iun setting timeframe expectations. “Everybody wants it done tomorrow,” Iyer said, “but timeframe depends on organizational maturity – do you have the data integration and access and do you have the skills” needed to develop and capitalize on data-driven insights?
About V2V and its research launch meetups
The Vision to Value (V2V) community is working on an ambitious research agenda, with twelve discrete topics organized under three main subject headers. Work on the first category, “Analytics in a business context,” is nearly complete, with three of the four topic reports – on developing the analytics business case, problem solving (the right data for the right question) and monetizing data – already complete, and a fourth, on introducing analytics to the organization, scheduled for release in September.
V2V best practices are established through working groups that include analytics practitioners, suppliers, academics, and others who rely on fact-based input to business decisions. The documents are written by InsightaaS analysts and vetted by the V2V research participants, and then launched at meetups that combine executive and practitioner-level panel discussions with extensive peer networking. With an overall attendee approval rating of 90% – including ‘excellent’ ratings from more than 60% of attendees – V2V meetups have become established as the best forum for analytics professionals in the GTA.
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