Valuable lessons: Your Data Is Worth More Than You Think by MIT Sloan Management Review.

Over the last just two years, I started getting more passionate about data. When I started watching a video from the World Economic Form regarding Fourth industrial revolution, I started noticing how digital transformation is skyrocketing. I deliberately started my keen interest on the Internet of Things, Artificial Intelligence and 3D printing too. To be honest in 3D printing, I don’t know how data works. To learn a fair view of those topics, I seen the term “Data”. Data matters here to learn. That is what makes to me to pursue the Data Scientist course.

So this is an article about data gives you views about data valuation with three basic reasons. I pasted the source link down below. I sincerely encourage you all to visit further.

About the Authors

With over 20 years in advanced analytical applications and architecture, John Akred is a recognized expert in the areas of applied business analytics, machine learning, predictive analytics, and operational data mining. He has successfully delivered applications using a wide range of architectural approaches — including stream processing, in-database analytics, complex event processing, real-time optimization, event-driven architectures, and more — at scale. He tweets @BigDataAnalysis. With a background in machine learning, Anjali Samani is adept at managing and delivering commercial data science projects across a range of industries. She has nearly a decade of experience as a quantitative analyst in investment management, focusing on financial modeling and risk analysis, and is passionate about enabling organizations to identify innovative solutions through effective use of data. Anjali tweets @AnjaliSamani.

Even when company leaders recognize that their data has value, they have difficulty measuring that value accurately — and it can cost them.

Data has become a key input for driving growth, enabling businesses to differentiate themselves and maintain a competitive edge. Given the growing importance of data to companies, should managers measure its value? Is it even possible for a company to effectively measure the value of its data? An increasing number of institutions, academics, and business leaders have begun tackling these questions, leaving managers with many alternatives for assessing the value of data. None are yet generally accepted, nor completely satisfactory, but they can help organizations realize more value from their data.

Why Is Data Valuation Important?

There are three basic reasons organizations want a good way to understand the value of their data. A good sense of value can help guide good decisions around direct monetization, internal investments, and mergers and acquisitions.

Direct Data Monetization

Many organizations are keen to monetize data directly by selling it to third parties or marketing data products. Inability to understand data’s value can result in mispriced products. Understanding the impact of exposing data to third parties on the value of a company’s data for indirect monetization can help guide the decision on whether to pursue explicit monetization. Today, despite an increasing recognition of potential benefit, most organizations are very conservative about what data they expose outside the enterprise. Good valuation approaches could help leaders understand if selling their data would really affect their competitive position or ability to realize their own benefit from it.

Internal Investment

Understanding the value of both current and potential data can help prioritize and direct your investments in data and systems. In our experience, most organizations struggle to articulate the relationship between their IT investments and business value generally. For data systems, the problem is particularly acute. Surveys report that only about 30% to 50% of data warehousing projects are successful at delivering value. Understanding how data drives business value can help you understand where you should be minimizing costs, and where you should be investing to realize potential ROI.

An ability to articulate data’s contribution to an organization’s overall value can transform the relationship between technology and business management. Chief experience officers (CXOs) charged with managing data report that their ability to articulate business value from data investments with rigor supported by the CFO results in more resources available to drive more positive outcomes for their organizations.

Mergers & Acquisitions

Inaccurate valuing of data assets can be costly to shareholders during mergers and acquisitions (M&A). Steve Todd, an EMC fellow, argues that data valuations can be used both to negotiate better terms for initial public offerings, M&As, and bankruptcy, and to improve transparency and communication with shareholders. Did Microsoft Corp.’s purchase price of LinkedIn Corp. include the value of LinkedIn’s data about professionals and companies? Did they survey potential uses of data in the combined company? The assumption that data’s value is captured only by sales and revenue figures may understate the overall value of a transaction to the benefit of the buyer — and to the detriment of the seller.

Current generally accepted accounting practices (GAAP) do not permit data to be capitalized on the balance sheet. This leads to considerable disparity between book value and market value of these companies, and a possible mispricing of valuation premiums. While internationally agreed-upon standards may emerge in the next five years, the Association of Chartered Certified Accountants (ACCA), the global professional accounting organization, is encouraging accounting companies to come forward with approaches. Wilson and Stenson provide an excellent review of accounting approaches that recognize and value intangible assets in general, and information assets in particular.

Existing Approaches Are Useful, But Limited

Methods for valuing data are varied. Most descend from existing asset valuation or information theory. Some attempt to attribute the value of business outcomes directly to data-driven capabilities. Like statistical models, all have limitations, but some are useful.

Dell EMC Global Services Chief Technology Officer Schmarzo developed the so-called “prudent value” approach, which values data sets based on the extent to which they could be used to advance key business initiatives that support an organization’s overall business strategy. This approach has two main advantages:

  • It provides ballpark valuation (or a range of values) for the data set derived from the financial value of the business initiative.
  • More important, it frames the data valuation process around the business decisions that need to be made to drive the targeted business initiative. It quantifies the ways in which different data sets might be utilized and the impact this could have on the success of the targeted business initiative.

Mapping data to valuable outcomes can fulfill many purposes of data valuation. It supports rigorous ROI arguments based on concrete business outcomes for IT investment decisions. It can also guide pricing direct monetization efforts by relating the business value of the decisions third parties use with respect to data to guide the price they might pay for access.

Some of the most comprehensive work on the subject of data valuation comes from Gartner Inc.’s Douglas Laney. Laney, vice president and distinguished analyst, Chief Data Officer Research, proposes “infonomics” as an economic discipline, arguing that information should be treated as an actual corporate asset — measured, managed, and deployed as if it were a traditional asset. Laney describes six different information valuation methods, three foundational and three financial.

The foundational methods are primarily aimed at businesses that wish to prioritize or create an aggregate of data quality characteristics to get a sense of what its relative or intrinsic value is. These methods force businesses to take stock of their data, how they are leveraging it (or not!), and ultimately articulate its value and evaluate what is and isn’t useful. Laney’s financial measures draw on methods to value intangible assets.

The biggest limitation of Laney’s approach is that it does not tie the value of information to its role in supporting business decisions. His approach is more likely to be useful for valuing data in M&A transactions.

Where to Start

While there is still room for significant improvement in how to value data, current methods can still be useful to enterprises. Organizations should begin efforts to:

  • Create management consensus on how to build business cases for IT investments in data, infrastructure, and capabilities.
  • Use data valuation to prioritize data investments.
  • Begin cataloging and estimating value from existing and potential data-driven capabilities to inform valuation on the public markets or in M&A transactions.

Organizations that become more capable of getting value from data will certainly realize benefits and competitive advantage. Developing the ability to understand data’s value, and contribution to outcomes, is an important part of delivering that value.




With respect.



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