corporate assets, data governance, DW 3.0, Good Strat, information supply framework, ISF, Martyn Jones, traditional assets
To begin at the beginning
You can use all the quantitative data you can get,
but you still have to distrust it and use your own intelligence and judgment.
There is a touching belief that all markets are rational and that a company’s value is accurately reflected in the current share price multiplied by the total number of shares in circulation.
The thing is, this doesn’t square up with formal accounting practices and financial reporting, as they now stand. So, there is a sentiment that the gap between capitalisation and accounting valuation must be due to some less tangible factors, which is true, and for many years it was accepted that aspects such as good will, were not in fact measurable or even easily manageable.
In the nineties, Leif Edvinsson and a team of accounting and finance specialists at Skandia pioneered the accounting and reporting of non-tangible assets, which they called the Skandia Navigator, the focus was clearly on reporting the progress of the creation and use of Intellectual Capital (IC). What the Skandia IC team did, how they went about it, and how they presented their results, (as a supplement to their annual report,) made a lot of good sense.
Now there is a movement to measure, value and manage all data as if it were a highly tangible asset, because, according to some, IT has been incapable of managing data because they don’t know how to measure it and they aren’t particularly well equipped to do so, a claim that is paradoxically made by people who are very much in the IT camp.
But this is not about treating data as an asset but focuses on potential risks due to data loss, data corruption, quality issues, misinterpretation, misuse or whatever. Simply stated, a risk prevented from arising or a risk that is mitigated is not an asset, and risk management is not primarily about rigidly theoretical asset management and vice verse.
Now, I won’t argue that it’s not a good idea to have an idea of the value of business assets, from either a quantitative or qualitative perspective, but I think there are more moderate, coherent and less fundamentalist approaches to the understanding and valuation of data, approaches that are more aligned to contemporary views of the understanding of the value of intellectual capital assets – unstructured and structured. What follows is a brief and high-level view of some potential options.
Data Assets in MOSCOW
What follows is a simple explanatory technique that is part of the DW 3.0 Information Supply Framework approach. It focuses on qualitative aspects of data as an asset. The following diagram provides an overview of foundations of the first phase of this simplified risk, classification and prioritisation approach:
On the horizontal access we have used the MOSCOW classification to break the problem down into simple terms. Here is an example of its configurable application:
MUST – Must treat the data as an asset: If this data is compromised in anyway then it would possibly result in serious negative implications for all (or most) customers and severe financial, contractual and reputation impacts on the business
SHOULD – Should treat the data as an asset: If this data is compromised in significant ways it would result in intolerable negative financial implications for a number of customers and would also lead to negative financial impacts on business
COULD – Could treat the data as an asset: If this data is compromised in significant ways then it could result in financial consequences for the business, but without impacting customer churn
WON’T – Won’t treat the data as an asset: Data that if compromised would not result in financial consequences wither for customers or for the business
It is important to understand that these definitions are examples and that in practice the descriptions and parameters must be determined and aligned in cooperation between the distinct business stakeholders, including business IT.
Data Value Chains
Another aspect that is important to the understanding of ‘data as an asset’ is to be found in data value chains. The following diagram illustrates the DW 3.0 technique used to simplify the attainment of a consensual view of the understanding of this value.
DATA: “Data is a super-class of a modern representation of an arcane symbology.” –Anon
In order for data to be more than an operational necessity it requires context.
Providing valid data with valid context turns that data into information.
Data can be relevant and data can be irrelevant. That relevance or irrelevance of data may be permanent or temporary, continuous or episodic, qualitative or quantitative.
Some data is meaningless, and there are cases whereby nobody can remember why it was collected or what purpose it has.
Taking all this into account we can now ask the pragmatic question: what value does this data have? Which is sometimes answered with a: ‘no value whatsoever’.
INFORMATION: “My sources are unreliable, but their information is fascinating.” –Ashleigh Ellwood Brilliant
In order for information to be created to drive organizational operations and tactics we must have valid interpretations of data.
Providing valid information with valid interpretations potentially turns that information into knowledge.
Information can be correct, partially correct or incorrect; it may be relevant or irrelevant. The validity of information may also be permanent or temporary, continuous or episodic, and qualitative, quantitative or both.
Some information may be relevant, irrelevant or misleading. Information derived from erroneous and incomplete data may be usable, but the outcomes of using that information may be unpredictable.
Taking all this into account we can now ask the pragmatic question: what value does this information have? A question which can sometimes be answered with an ‘it is probably too early to say’.
KNOWLEDGE: This necessarily refers to a subset of ‘business knowledge’ known as structured intellectual capital.
The adequate, appropriate and timely application of knowledge (structured intellectual) requires wisdom; knowledge alone will no longer be good enough.
Providing valid knowledge with valid interpretations potentially turns that knowledge into valid and executable strategies.
Knowledge may be useless or useless; it may be relevant or irrelevant; it may even be wrong, even if we have named it ‘knowledge’. The usefulness of knowledge may also be permanent or temporary, continuous or episodic, qualitative or quantitative.
Some knowledge may be relevant, irrelevant or misleading. Knowledge derived from erroneous and incomplete information may be usable, but the outcomes may be unpredictable. As they might say in Córdoba: ‘Give knowledge to a donkey and it will still remain a donkey’.
Taking all of this into account we can now ask the pragmatic question: what value does this knowledge have? A question which can sometimes be answered with: ‘Isn’t it getting rather hot in here’.
What does it all mean?
To paraphrase George S. Patton, for the purposes of business data, an imperfectly good pragmatic valuation of data executed today is better than a perfect theoretic valuation of data made in the next life.
The valuation of data, information and knowledge is complex and involves many intangibles, and although some may view the data, information and knowledge chain as a closed process, this is not in fact the case, as each step of the process is influenced by a number of factors that fall outside of a simplified view of any theoretical or practical progress from data to wisdom.
When I started in IT over 30 years ago I worked on some of the first large-scale OLTP projects in Europe. On one such project, the development, test and production the databases were continuously replicated. Every day, not one, but ten backups of the data were made, and then shipped to different physical locations.
Thirty years on and some people are saying that IT has never treated data as an asset?
The fact of the matter is that data is managed as an asset. In fact, some IT organizations may be taking exaggerated measures in order to protect the data and information in their care. That some people have issues with the contemporary management of data does not change the facts on the ground, and the perceived shortfall in the commercial exploitation of data is frequently and erroneously interpreted as the absence of asset appreciation and management.
Moreover, even with generally accepted accounting principles there are tangible assets that are either of dubious value or are cost-absorbing liabilities.
Finally, there may also be unintended consequences of an overzealous approach to the financial reporting of intangible assets such as data, and just as the value of tangible assets can be accidentally or deliberately overstated, so too could the value of data, which may well lead to a significant overvaluation of a business. Moreover, it’s not beyond the realms of possibility that big data lakes, data universes and dark data dungeons – the ones that apparently will drive trillion dollar economies – are in fact somewhat worthless.
In this respect I think there is in some quarters an unhealthy fixation on the supposed (and as yet mainly unproven) value accruable from masses of data.
That’s all folks
Steve Jobs didn’t turn Apple around by relying solely on data and information, it was knowledge, and more especially the wisdom of knowing where, when, why and how to apply that knowledge that made the significant difference.
So, until the next time, hold this thought: ‘Google market cap slashed and Facebook in freefall as accountants ask “where’s the big data beef?”… Breaking news!’
Many thanks for reading.
Doug Laney (@Doug_Laney) said:
Interesting and well thought-out piece Martyn! I’m including it on the Infonomics Wikipedia page: http://en.wikipedia.org/wiki/Infonomics. Cheers! –Doug Laney, VP Research, Gartner, @doug_laney
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