Senior executives trained in accounting continue to struggle to understand how to determine the value of their data. The article \u201cWhy Your Company Doesn\u2019t Measure The Value Of Its Data Assets<\/a>\u201d written by Doug Laney (by the way, why does the Forbes web site absolutely bury the reader in ads?) contains a telling comment from a senior accounting firm partner:<\/p>\n \u201c\u2026 balance sheets and income statements which form the backbone of today\u2019s accounting system now fail to capture significant sources of value in our economy. He said that the measurements we use don\u2019t reflect all the ways that companies create value in the New Economy, and this lack of transparency results in undue market volatility and mere \u201cguesstimates\u201d by investors in valuing companies. Even the chairman of the AICPA stated that the accounting model is out of date and based on the assumption of profitability depending upon physical assets\u2014an accounting model for the Industrial Age, not the Information Age.\u201d<\/em><\/p>\n This paragraph reflects how a traditional accounting mindset, in the age of digital assets, is focused on the wrong valuation method \u2013 trying to represent value using an artificially-defined balance sheet that doesn’t capture how today\u2019s companies are using data to create new sources of customer, product, and operational value.<\/p>\n Nothing says \u201cWe really don\u2019t know how to quantify value in the digital age\u201d better than Figure 1 where a significant percentage of the most valuable firms\u2019 \u201cvalue\u201d is credited to nebulous intangible (non-physical) assets.\u00a0 And the discrepancy in the creation of value between traditional physical assets and intangible digital assets is growing exponentially.<\/p>\n Figure<\/strong> 1<\/strong>: \u00a0Increasing Percentage of Most Valuable Firms defined by Intangible Assets<\/strong><\/p>\n To properly reflect the value of their digital assets, executives must embrace an economics mindset where the value of an assets is determined from the use of that asset. This is critical given the unique economic characteristics of digital assets \u2013 they never wear out, never deplete, can be used across an unlimited number of use cases at zero marginal cost, and they can appreciate, not depreciate, in value they more that they are used (if properly engineered).<\/p>\n I introduced the Data Monetization Roadmap<\/strong> in \u201cIntroducing the \u201c4 Stages of Data Monetization<\/a>\u201d as a guide to help organizations in their data monetization journey. The roadmap emphasizes that the driver of data monetization is in the use or application of the data to create value. That is, the value of data isn\u2019t in possession but in the application of the data to create new sources of customer, product, and operational value. \u00a0<\/p>\n As organizations negotiate the Data Monetization Roadmap, they will encounter two critical inflection points:<\/p>\n This pivot point is where the organization makes the transition from just capturing, storing, securing, and governing data to actually monetizing it. How do you get organizations to make that first pivot towards Data Monetization?\u00a0 How can one help the business stakeholders to connect to and envision where and how data and analytics can generate value (see Figure 2)?<\/p>\n Figure<\/strong> 2<\/strong>: Data Monetization Roadmap Inflection Point #1<\/strong><\/p>\n Navigating Inflection Point #1 requires close collaboration with business stakeholders to identify, validate, value, and prioritize the business and operational use cases where data and analytics can create new sources of value. \u00a0The Big Data Strategy Document<\/strong> in Figure 3 provides a framework for that collaborative engagement process.<\/p>\n Figure<\/strong> 3<\/strong>:<\/strong> Unleashing the Business Value of Technology<\/strong><\/a><\/p>\n The Big Data Strategy Document decomposes an organization\u2019s key business initiative into its supporting use cases, desired business outcomes, critical success factors against which progress and success will be measured, and key tasks or actions. The Big Data Strategy Document sets the stage for an envisioning exercise to help the business stakeholders brainstorm the areas of the business where data and analytics can drive meaningful and relevant business value. Yep, there is a lot of work that needs to be done before one ever puts science to the data.<\/p>\n So, now we\u2019ve given the business stakeholders a taste of success in monetizing their data.\u00a0 Interest is building and others across the organization are asking for help in monetizing their data.\u00a0 Now it gets really fun!<\/p>\n The second inflection point occurs just as organizations are scaling their data and analytics success across the organization.\u00a0 More and more business units are coming to the data and analytics team for assistance with their top priority use cases. But remember:<\/p>\n \u201cOrganizations don\u2019t fail due to a lack of use cases; they fail because they have too many.\u201d<\/em><\/p>\n The volume of use case requests starts to overwhelm the limited data and analytics resources.\u00a0\u00a0 And when the business units can\u2019t get support in a timely enough manner, the business units get frustrated and seek outside solutions. And as these organizations go elsewhere for their data and analytic needs, some fatal developments occur:<\/p>\n The result: instead of creating data and analytics assets that can be easily shared, reused, and continuously refined, the organization has created data and analytics debt that drives up maintenance and support costs which quickly overwhelms the economic benefits of the data and analytic assets.\u00a0 Welcome to Inflection Point #2 (see Figure 4).<\/p>\n Figure<\/strong> 4<\/strong>:<\/strong> Data Monetization Roadmap Inflection Point #2<\/strong><\/p>\n What can organizations do to avoid the collapse of the economic value of data and analytics that can occur at inflection point #2?<\/p>\n Figure<\/strong> 5<\/strong>: Data Lake 3.0:\u00a0 The Collaborative Value Creation Platform<\/strong><\/p>\n Data Lake 3.0 employs intelligent catalogs to help the business units find the data they need for their use cases.\u00a0 The data lake also employs intelligent data pipelines to accelerate the ingestion of new data sources, and a multi-tiered data lake environment to support rapid data ingestion, transformation, exploration, development, and production.\u00a0 And eventually, these modern data lakes will transform into contextual knowledge centers that not only help the business units find the data, but also provide recommendations on other data sources (and analytic models) that might be useful for their given use case.<\/p>\n Figure<\/strong> 6<\/strong>: Data Monetization Governance Council<\/strong><\/p>\n The key to scaling the organization\u2019s data monetization capabilities is to thwart data silos, shadow IT spend, and orphaned analytics that create a drag on the economic value of data and analytics.\u00a0 When the business and operational costs to find, reuse, and refine the data and analytic becomes greater than the cost to build your own from scratch, then that\u2019s a failure of the Data Monetization Governance Council.<\/p>\n The Data Monetization Roadmap provides both a benchmark and a guide to help organizations with their data monetization journey.\u00a0 To successfully navigate the roadmap, organizations must be prepared to traverse two critical inflection points:<\/p>\n Carefully navigate these two inflection points enables organizations to fully exploit the game-changing economic characteristics of data and analytics assets \u2013 assets that never deplete, never wear out, can be used across an unlimited number of use cases at zero marginal cost, and can continuously-learn, adapt, and refine, resulting in assets that actually appreciate in value the more that they are used.<\/p>\n Yes, you could say that the Data Monetization Roadmap is the game plan for fully exploiting the Schmarzo Economic Digital Asset Valuation Theorem<\/a>. \u00a0But that\u2019s just me and that Nobel Prize in Economics talking\u2026<\/p>\n \u00a0 Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<\/a><\/p>\n
Mastering the Data Monetization Roadmap<\/strong><\/h1>\n
\n
Inflection Point #1: Proving and Expanding Value<\/strong><\/h1>\n
<\/a><\/p>\n
<\/a><\/p>\n
Inflection Point #2: Scaling Data Monetization Potential<\/strong><\/h1>\n
\n
<\/a><\/p>\n
\n
<\/a><\/strong><\/p>\n
\n
<\/a><\/strong><\/p>\n
Mastering the Data Monetization Roadmap Summary<\/strong><\/h1>\n
\n
<\/a><\/p>\n<\/div>\n