What is your data worth? - Short & Todd (2017)

Comment: The previous articles are concerned with the economic value of data for statistical purposes. This article discusses internal business value. The gist of the approach suggested by Short & Todd is to use a triggering event to determine company need for valuation; with requires the need to have systems in place to permit valuation when demanded. Pre-positioning requires the allocation of resources to continuously improve data systems. Here, data is defined as intangible. 

Short, J.E. and S. Todd (2017): What is your data worth?, MIT Sloan Management Review, Vol. 58, No.3. , https://sloanreview.mit.edu/article/whats-your-data-worth/ Reprinted here: https://oag.ca.gov/sites/all/files/agweb/pdfs/privacy/short-whats-your-data-worth.pdf [James Short, Ph.D.: Lead Scientist, San Diego Supercomputer Center (SDSC); Steve Todd: Fellow & VP Strategy and Innovation at Dell EMC/Dell Technologies]

    Part (1) Describes the market impact of data assets
    Part (2) Explores the methods of market data valuation
    Part (3) Constructs a framework for valuing data
    Part (4) Suggests a path forward, circa 2017

Part (I) (p.17): Introduction - Uses two instances where companies needed to determine data valuations, to illuminate the impact of knowing the value of a company's data (Microsoft purchasing LinkedIn; the Chapter 11 bankruptcy proceedings of Caesars Entertainment Corp).

Part (2) (p.17-18): "Exploring Data Valuation" - Discusses project activities: Interviews and research into 36 North American and European companies and nonprofit organizations. The interviewees spanned several sectors, and most earned US$$1 billion+. They discovered that most were focused on managing big data; not valuation; and used the discovery knowledge to determine the business impact of data assets, by:

• Interviewing "chief financial and marketing officers and, in the case of regulatory compliance, legal officers"; and
• Identifying significant business events triggering the need for data valuation, such as mergers and acquisitions, bankruptcy filings, or acquisitions and sales of data assets.

As is now (2024) widely known: Every company was overwhelmed with data, the volume of stored data "was growing on average by 40% per year"; that teams were hard pressed to manage their data assets because it is "time-consuming and complex"; and this was placing extreme "on management to know which data was most valuable."

Part (3) (p.18-19): "A Framework for Valuing Data" - The authors used research results to classify business data as a composite of three sources of value:

1. Data as Strategic Asset (asset value or stock value) - "Monetizing data assets means looking at the value of customer data"; i.e. using customer data to generate monetary value either directly (sell, trade, acquire) or indirectly (data not sold; availability of data used to create a new product or service).
   
2. The Value of Data in Use (activity value) - the impact of the cost to access, use and frequently use data; with the additional impact that "data has the potential...to increase in value the more that it is used";
3. Expected Future Value (the determination of value for recording on balance sheets) - as intangible assets that are "co-mingled with other intangible assets, such as trademarkets, patents, copyrights, and goodwill."

Part (4) (p.19): "What Can Companies Do?" - Moving forward, the authors suggest "three practical steps" to improve company practices:

1. Make valuation policies explicit and shareable
2. Build in-house data valuation expertise
3. Choose a top-down or bottom-up metadata control process

Top-down approach: 

1. Identify critical applications
2. Assign a value to the data used in critical applications
3. Defining the main system linkages (systems connecting systems' data flows)
4. Use 1-2-3 to develop internal IT and business unit partnerships
5. Use 1-2-3-4 to develop a prioritizing system
   

Bottom-up approach - Define value heuristically:

1. Create a map of data usage across core data sets
2. Assess data flows and linkages
3. Produce a detailed usage patterns analysis

Notes and analysis by blogger. Image: Pxhere. CC0: 114437 

Labels: Dimensional;Company;Valuation;Data;Metadata;Control