Navigating the Value Conundrum - Azcoitia (2023)

The next several blog posts will discuss the current state of the art of data valuation.

Azcoitia, S.A., Towards a Human-Centric Data Economy, Ph.D dissertation; Telematics Engineering, Universidad Carlos III de Madrid https://doi.org/10.48550/arXiv.2111.04427 and https://sandresazcoitia.com/2023/04/24/towards-a-human-centric-data-economy/

 

 

 

 

 

 

Unravelling the Complex Web of Data Valuation: 

In the ever-evolving landscape of data-driven economies, understanding the true value of data has become a paramount challenge. This 2023 study is a comprehensive exploration that delves into the intricacies that arise when determining the worth of data assets; whilst navigating the dynamic realm of commercial data marketplaces. The study sheds light on the data value chain, and the nuances of trading data assets through the internet, through a meticulous examination of market dynamics.

• Part (1) explores the data value chain and the trading of data assets.
• Part (2) reports development and execution of a measurement study.
• Part (3) reports development of novel algorithms and methods to streamline data transactions.
  
• Part (4) concludes, presents new research topics, and proposes policy changes.

Part 1: (p.3) Begins by dissecting the data value chain and delving into the trading mechanisms of data assets facilitated by the internet. A detailed survey and analysis of commercial data marketplaces and vendor strategies makes up this section. The subsequent market review, starting on page 29, is highly recommended for a comprehensive understanding of the current landscape.

Part 2: Reports development and execution of a measurement study that estimates the value of data and the setting the price of a dataset; and uses this to analyse the behaviour of market data prices. This activity is aimed at estimating value to establish a pricing framework. 

Part 3: Azcoitia reports development of a framework of “algorithms and tools to reduce the complexity”, improve market efficiency, and improve buyer profitability. The framework is used to analyze the behaviour of market data prices; providing valuable insights into the dynamics of this complex ecosystem. 

Part 4: Discusses the findings and includes proposals for policy changes arising from the author's observation that:

• The elusive nature of data as an economic good has proven to be a central problem (pages 12-16). 
• Estimating value has proven to be challenging, leading to seemingly contradictory estimations. 
  
• The quality of data emerges as a crucial production factor, comparable to traditional factors such as land, capital, labour, or infrastructure.

The author emphasizes the critical need for a nuanced approach to assess the true worth of data in economic terms. Intricacies of pricing strategies are unravelled in pages 17-18, providing a comprehensive review of various approaches such as usage-based, subscription-based, and package-pricing.

In conclusion, the study examines the complexities that are part of estimating value and proposes algorithms and tools to reduce this complexity, enhance market efficiency, and improve buyer profitability. The author advocates for policy changes to foster a more transparent and adaptive data marketplace. 

As industries grapple with the evolving data landscape, this study serves as a crucial guide for navigating the intricacies of today's data markets.

- Summary notes re-written with the assistance of a paid ChatGPT account.

And what of the valuation?

 

 

 

 

 

 

The Value of Data: A Crucial Component of Goodwill in Accounting

In today's digital age, data has emerged as a powerful currency that drives decision-making across various industries. Beyond its traditional role as a byproduct of business operations, data is now increasingly being recognized and valued as a crucial component of goodwill by accountants. This shift in perspective reflects the growing importance of data stories, data quality evaluation, and data valuation in assessing a company's overall worth.

First and foremost, data stories are at the heart of understanding how data can contribute to goodwill. Accountants are not just crunching numbers anymore; they are weaving narratives from data points. These stories offer insights into a company's past performance, current operations, and future prospects. Investors and stakeholders look beyond financial statements to comprehend a company's strategic direction and potential for growth, and data stories play a pivotal role in conveying this information.

Moreover, data quality evaluation is paramount in determining the worth of data as part of goodwill. Just like any other asset, data can vary significantly in quality. Accountants now employ sophisticated tools and methodologies to assess data quality, ensuring that the information used in financial reporting is accurate, complete, and reliable. Poor data quality can erode trust in financial statements and, consequently, reduce the perceived value of a company's goodwill.

When it comes to data valuation, accountants employ various tools and techniques to estimate the worth of a company's data assets. This valuation takes into account the unique nature of the data, its potential for revenue generation, and its strategic importance to the business. Data valuation can be complex, as it involves not only the tangible aspects of data but also intangibles like brand reputation and customer trust, which can be significantly influenced by data-related activities.

Data's role in goodwill valuation has evolved as our civilization becomes hyperconnected. Accountants now recognize the importance of data stories, data quality evaluation, and data valuation in assessing a company's overall worth. As data continues to drive businesses forward, its value as a component of goodwill will only increase, and accountants will continue to refine their methods for evaluating and incorporating data into financial reporting.

And what of the waste?

 

We can discuss tangible vs. intangible. What about the waste? Especially the intangible waste? We can touch everything that data touches, but not data itself. Which leads to serious issues when preparing data for service. It is like managing fog.

Consider "80%' - the reported time cost to clean data.

Data has no current value; so how can we say we know the cost to clean it? Is the 80% rule-of-thumb is accurate; hyperbole, or as UK Data Scientist Leigh Dodds puts: “bullshit stats”? .

The source of the rule-of-thumb appears to originate with a citation error in a 2018 Harvard Business Review article:

• “Yet today, most data fails to meet basic “data are right” standards. Reasons range from data creators not understanding what is expected, to poorly calibrated measurement gear, to overly complex processes, to human error. To compensate, data scientists cleanse the data before training the predictive model. It is time-consuming, tedious work (taking up to 80% of data scientists’ time), and it’s the problem data scientists complain about most.” Thomas C. Redman, If Your Data Is Bad, Your Machine Learning Tools Are Useless, April 02, 2018: https://hbr.org/2018/04/if-your-data-is-bad-your-machine-learning-tools-are-useless

• This is cited in "AI starts with data, AI Business eBook Series in collaboration with Telus International, 2021: https://resources.aibusiness.com/ai-starts-with-data/

• Redman errs in citing Edd Wilder-James who states that "80% of the work is acquiring and preparing data"; Edd Wilder-James, Breaking Down Data Silos, December 05, 2016: https://hbr.org/2016/12/breaking-down-data-silos?autocomplete=true.

• And Wilder-James cites Gil Press who states: "Data preparation accounts for about 80% of the work of data scientists", breaking this into six tasks that data scientists spend most of the time doing (again, not 100%!), with “Cleaning and organizing data: 60%”: Gil Press, Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says; Mar 23, 2016:  https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/?sh=1104aab46f63

 

 Does anyone really know? Here are notes assembled from reports: