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: