Part Two: How to Make the Value of Data and Analytics Visible to the CFO

money

Shortfalls in Financial Reporting

To aid decision-making, C-level executives need a constant and fast-flowing stream of answers to questions like:

  • What is the current and future worth of our information assets?
  • What is our return on those investments?

As discussed in ‘Why The CFO Cannot See a Value of Data and Analytics in the Balance Sheet’, management teams often find it difficult to provide a timely response because of things like shortfalls in generally accepted accounting principles and practices.

Fortunately, we can provide teams with alternative ways of improving the visibility of ‘Data and Analytics Value Recognition and Reporting’, using concepts borrowed from the field of economics.

The Economics of Information

Classical economics identifies two types of value: ‘value in exchange’ and ‘value in use’. The former is of little use for data and analytical investments, as there is no sale or exchange of information asset — nothing ‘changes hands’.

So, the economic value of data and analytics reflects the ability of IT and data science to enhance the business performance of the enterprise. Serial use of the information to create new insights allows management to make fact-based decisions and act accordingly. This activity is measurable and can be expressed in financial terms, e.g. incremental revenue growth, cost reduction, margin improvement, cost avoidance, revenue recovery, cash flow improvement, and so on.

Information as an Intangible Asset

Now, as data and analytics don’t match the characteristics of physical assets (from an economic perspective), they can’t easily be classified as tangible assets in the balance sheet. A defining feature of data and information is that it stores, enriches, streams, and is used in the same way that water flowing in and out of the Sea of Galilee provides a healthy and vibrant ecosystem.

Another distinguishing feature of data and analytics compared to conventional assets (crude oil, a gold mine) or tangible assets (buildings, machinery) is that they are not depleted by use. So the marginal cost of exploiting them is very low and the business value generated, cumulative. This is why intangibles can be so valuable.

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Invisible Balance Sheet

Because of the limited ways information and knowledge assets are treated in conventional balance sheets, steps have been taken to establish the value of an enterprise in the knowledge economy. Dr Karl-Erik Sveiby invented the concept of an ‘Invisible Balance Sheet’ and ‘Intellectual Capital’, for instance.

These concepts provide a method of accounting for — and attributing the value of — an enterprise which the traditional balance sheet cannot offer. In essence, differences between the book value (shareholder equity) and the market value (market capitalisation) of an asset can be attributed to internal structure, external structure, and individual competency. That said, this sort of value attribution is governed by market fluctuations and is, therefore, beyond organisational control. It can also be a kind of black art for formulating a consistent process to account for value.

Tangible Value With the Business Value Framework

Data and analytics should be subject to regular accountability disciplines. And the total cost of ownership should be supported by the regular auditing and reporting of business value and quantifiable benefits, alongside profit and loss statements and the balance sheet.

Well-established frameworks for executing this in a methodical and systematic manner do exist.

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Business Value Framework (BVF) methodology offers several unique advantages. It is a highly structured and strategic way to flag vertical-industry-specific use cases, identified to make business-process improvements with the highest return on investment. BVF helps prioritise opportunities and aligns business-analytics use cases with the company’s strategic objectives.

A significant feature of the framework is that it leverages and reuses an organisation’s available data, reducing overall costs and increasing value. This process tracks business teams creating value for the organisation by empowering under-utilised data assets to make quicker, smarter decisions, and achieve their business goals.

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The regular measurement of return on capital emphasises the tireless efforts being made to create value for the enterprise. The Business Value Consulting methodology includes a framework for the regular audit of data and analytics assets, and provides a strong incentive for managers and executives to use them efficiently and effectively.

So, don’t let your data lake end up like the Dead Sea. Business users need to be able to get hold of, optimise, and analyse big data, which means configuring and monitoring data pipelines in and through the data lake so they have constant access to high-quality data.

Impress your CFO by using a proven framework to measure and report the value of data and analytics alongside the balance sheet.

Note: This part two of a  series, part one can be found here.


Sundara Raman, Senior Telecom Industry Consultant, Teradata Australia

Sundara has been a Telecom professional for the over 30 years with a wide range of interests and multi-national experience in product management, solution marketing, presales for new generation networks and services, information management strategy, business intelligence, analytics and enterprise architecture development.

At Teradata, Sundara focuses on Business Value Consulting, Business Intelligence, Discovery Analytics and Customer Experience Management solutions.

Sundara has a Master’s Degree in Business and Administration with research on economic value of information from Massey University, New Zealand.

For the last 20 years, Sundara has been living in Sydney, Australia. In his spare time, Sundara enjoys walking and maintaining an active life style. Sundara is an inventor and joint holder of an Australian patent with his clinical psychologist wife. The invention is an expert system in cognitive mental health that applies machine learning algorithms.

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