Category Archives: Good Strat

The Big Data ‘Wow Wow’ Factor

08 Sunday Feb 2015

Posted by in Big Data, Consider this, Good Strat, Good Strategy, Martyn Jones

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wFactor

The Wow Wow Factor! Trading, Big Data and 7 HabitsHi, I’m Ricky Jones, boss and co-founder of Becci Boo International Investments. Last week we said goodbye to our best ever Big Data energy commodity trader. He’d been with us for years.

Sadly, Coco Jones was determined to retire to the countryside, to his birthplace, to his real home, a snug little village in the hills of Montseny, and there was absolutely nothing we could do to convince him to stay. The thing about Coco is that he is not like you or me, he’s a highly intelligent Catalan sheepdog.

So, you might ask, how did Coco get to be a star trader at the Becci Boo Hedge Fund? Was it the tools he used? Was it the techniques he adopted? Was it the food he ate? What was so special about him and his amazing abilities? It’s a long story that I will relate as briefly as I can.

Back in time, there was one particularly disastrous week of trading at Becci Boo. Something had gone really wrong with our once reliable Big Data Trade Analytics platform, and wrong bets were being placed right, left and centre – and against trader’s better judgement. The CFO was livid. Out he comes onto the trading floor, swearing and blinding. “God! You guys are the damn pits! What the hell do you think you are doing? Can’t you get anything right? A Catalan sheepdog could trade more effectively than you feckless lot of feckless things.”

I try and diffuse the situation. “Come on, Jordi, don’t be like that, we’re only human and this is a tough business.” “You don’t believe me” he replies. “Sure, but you’re not going to convince me that a Catalan sheepdog could be a substitute for a highly experienced human trader are you?” “How much do you want to bet? These superior four legged beings would never place any faith in things that they don’t understand or can’t control, and they certainly wouldn’t need your Big Data gizmo to make a success of things.”

So, in the following weeks we arrange to run an experiment. We ring around all the owners of registered Catalan sheepdogs and ask them if they would like their dog to take part in our simple trading experiment. Food, lodging and generous expenses all included. We easily manage to get together 64 Catalan sheepdogs and their owners from all over Europe. The experiment we design is quite simple.

We give each dog a gadget (and actually for the more sophisticated dogs it was a smart phone and specially designed app) with two big buttons on it, a red button and a yellow button.

Every Monday morning we ask the assembled dogs to press one of two buttons depending on whether they think that the chosen energy commodities market will close higher or lower at the end of the week.

Red button for a higher closing price, yellow button for a lower closing price. As you might imagine dear reader it’s a walk in the park for these cunning canines. To make sure that all of the participating dogs have equal and fair chances in the experiment, we provide guardians with access to all our big data analytics, and we have multiple screens set up in each studio apartment so that each dog can follow all of the financial news that they need to take in and then view the results of machine learning, data mining and predictive analytics.

At the end of week one, 32 dogs remain in the experiment, and the other 32 are sadly sent home. Every week the exercise is repeated, and every week less dogs move on to the next round. By the end of week 5 only two dogs remain. By the end of week 6 we have a sole winner, Afi Bastò, who has accurately predicted the market movement of our energy commodities market for a record six weeks in succession. It’s an amazing success that proves Jordi right. Unfortunately, this is where things start to go wrong. Our marketing department tweets the news of the amazing Afi and it immediately goes viral.

Articles appear in the Wall Street Journal, Financial Times, Economist, Cinco Dias and Les Échos, hailing the amazing brilliance of the newly discovered Catalan commodity trader.

Hundreds of interviews are held and millions of photos make the rounds of the social and professional networks. Over the following year Afi is interviewed, researched, studied and investigated. Academic papers are written about him.

Amazing claims are made about his canine knowledge, wisdom and experience. This is shortly followed by the publication of a plethora of bestselling business books, with titles such as: The 7 Habits of the Highly Effective Canine – Personal Lessons in Trading; Be More Afi and Grow Rich; The Gos d’Atura That Conquered Chicago; Learn to Trade like Afi; Good Sheepdog, Great Afi; Who Moved My Dog Food; How Afi outperformed Big Data Analytics; etc.

Which unfortunately all goes to Afi’s head, and he begins to seriously lose his commodity trading mojo. So much so, that by the end of the year, we let Afi go, and we bring back “the incredible” Coco Jones, the dog that managed to get the market movements right for five weeks in a row, and who was only pipped at the post by “the amazing” Afi.

Since then, Coco has helped us to win far more than we lose, he has had an innate knack of being able to combine market knowledge with statistical analysis and a certain indescribable ‘insight’ that no one quite manages to understand never mind emulate, and all combined with an amazing caring character.

So, as you might imagine, we were really sad to see him go. After his epic leaving party, where he is acclaimed by all and sundry, but especially by Jordi, all the staff assembled outside the office entrance to see him off, and as he was slowly chauffeured away down the driveway he turned and looked back, and his face said it all. “I was lucky — I found what I loved to do early in life”.

Many thanks for reading.  

Here’s Coco paddling in his Big Data Lake

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File under: Good Strat, Good Strategy, Martyn Richard Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro

Consider this: 7 Handy Phrases To Unhinge Your Boss

06 Friday Feb 2015

Posted by in Consider this, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones

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See no evil, speak no evil, and hear no evil. Bad managers love to hear good news, leaders thrive on adversity, contradiction and criticism, but some bosses don’t know their right foot from their left ear.

Sure, there are things a lot of us would prefer not to hear. But sometimes things are just unavoidable. We are told that honesty is the best policy, but what happens when honesty goes wrong?

Here are some examples of comments that might piss your boss off, together with some suggestions on how to finesse your way out of such situations and crawl back into favour.

One: “Honey, I shrunk the Big Data…!”

This is a really difficult one. On the one hand your boss might take the news badly and run around like Chicken Licken for days on end lamenting the ‘fact’ that the sky has just fallen in. On the other hand, you might have a sensible, intelligent and sane boss, in which case you might like to follow it up with a “shall I stick it back in the spin dryer and give it another whirl?”

Two: “Isn’t it your bedtime already?”

No, no and no! This is so wrong, and on so many levels. First, avoid a question that ends with an ‘already’, this is far too formal for office banter. Next, consider the time. If it’s before 21:00 it is really not the moment to start asking these sorts of questions. Save this type of question for the regular night out with the project team or for anonymous SMSs.

Three: “Yes, your bum does look big in that 1k USD suit…”

Nobody likes being told that they have spent ‘loadsa’ money on sharp ‘schmutter’ that doesn’t exactly flatter, especially when it comes to naturally portly or big boned types. One way out of this difficult situation, if you really want a way out of this difficult situation, is to add a quick “sorry, only joking, you don’t look even half as bad as me dear old granddad”.

Four: “What did your last slave die of?”

Say you were busily serving tea in the Oval Office and President Obama asked you to pour some more milk into his cup, this would not be the phrase to use under any circumstances if this happened. In other circumstances it might be perfectly acceptable. Just imagine that instead of Obama it was Uncle Joe Biden who was asking. Then you would possibly be right to use that phrase, and would be free to follow it up with a “and who gave you permission to use the office of the POTUS to entertain your mates, huh?”

Five: “My Mum won’t be happy with this… and you know what that means”

Yes, he gets it, its blackmail, and he won’t like it. He won’t like the thought of not getting any… Well, you know what I mean. No need to spell out these sorts of things, is there, especially before the nine o’clock watershed. But, if this just slipped out accidentally then the best way of retracting it is to deny that you ever said it in the first place. Yes, I know this is not very ethical, but it’s the height of modern day ‘professionalism’.

Six: “You run like a girl…”

Your boss may behave like a highly socialised two year old, but the use of gender specific insults is a definite ‘no, no’. Of course there are exceptions. Your boss may be from a tribal ethnic minority and may have been named Runs Like Girl by his parents, so in that case it might be totally acceptable to use the name. But, it’s really best to ask first, just to be on the safe side.

Seven: “Okay, keep your hair on Mussolini, you’ll never sell any ice creams with an attitude like that!”

Dodgy one, under almost any conditions. There are however times when this might just work positively in your favour. For example, if the bald headed and rotund boss is a keen and nostalgic fascist sympathiser, one with a penchant for the old gelati celesti. In which case, you might want to follow it up with a rousingly jolly accusation such as “Fascist!” If that’s not the case or you are unsure, then it’s really best to avoid such language.

None of these expressions particularly bother me, but there are horses for courses, braces for races and boors for moors. If you are one of those charming ‘holier than thou’ thin-skinned puritans then you probably have a plethora of pet peeves of your own, in which case please join in the fun, and contribute your own ‘phrases I like to hate’, below.

Many thanks for reading.

Channels: : Getting Started

File under: Good Strat, Good Strategy, Martyn Richard Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro

Big Data in Social Studies

03 Tuesday Feb 2015

Posted by in Big Data, Consider this, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones

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You walk down the street, maybe in London, Paris and Rome – well, of course, not all at the same time.

You look out of your spacious and luxurious apartment in Manhattan, Unter den Linden or Mahim.

You are cycling slowly along the streets of Amsterdam, Bonn or Zurich.

Got the picture? Continue reading →

Consider this: Hedge Funds are Evil

30 Friday Jan 2015

Posted by in Banking, Consider this, Good Strat, Good Strategy, Martyn Jones, Martyn Richard Jones, Strategy

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Hedge Funds are evil, right? Go on, you know you want to say yes. Almost everyone has an opinion about them, but very few people can actually tell you what they are.

Indeed, there’s am awful lot of nonsense written about Hedge Funds, and this piece might just end up being a worthy addition to that body of baloney. But, the intention is somewhat different.

The objective behind this piece is to provide a quick look at where the modern hedge fund started; what they are; how they work; the mechanics of participation; and who traditionally has put their money into them.

Of course, this piece is a necessary simplification of what is a fascinating aspect of the alternative investment universe.

To begin at the beginning

In 1966 Carol J. Loomis[i] blew the lid on one of the best kept investment secrets of the 20th century.

In an article penned for Fortune titled “The Jones that nobody keeps up with”, Loomis revealed that over a five year period a fund run by Alfred Winslow Jones had consistently outperformed the Fidelity Fund, the most successful mutual fund of that time, by a remarkable 44%.

Not only that, but between 1956 and 1966 the Jones fund had outperformed the Dreyfus Fund, the best performing mutual fund of that decade, by a massive 87%.

Jones was born in Melbourne, Australia, but from the age of four he lived in the USA. He graduated from Harvard in 1923, and before becoming involved in the finance industry he toured the world working on steamships. He was to serve as a diplomat in Germany, and also worked as a journalist covering the Spanish civil war.

In 1941, with conflict raging in Europe, Jones returned to the USA. He then studied for, and obtained a doctorate in sociology at Columbia University, and became a reporter for Fortune.

His thesis, Life, Liberty and Property, is a reference text in sociology.

In 1949 Jones formed a company, A. W. Jones & Co., arguably the first modern Hedge Fund.

Robert A. Jaeger characterised the fund as “an opportunistic equity hedge fund”, that relied heavily on discerning stock picking abilities, combined with bets on long positions (rising prices) and short positions (falling prices).

In 1952 the fund was converted into a limited partnership, and during the 50s other such partnerships were set up, including the sage of Omaha’s Buffet Partners, and WJS Partners, founded by Walter Schloss.


What they are; how they work

Hedge funds are loosely regulated, exclusive and limited-membership investment clubs, usually run as partnerships or as corporations. They focus on absolute returns on investments, for themselves and their members, regardless of market conditions.

Direct participation in Hedge Funds is theoretically limited to between 100 or 500 investors, depending on the class of investor. Moreover, because of the unregulated status of most hedge funds, they are not allowed to actively market their products, so they have to use more exclusive means to attract investors, typically word of mouth.

Hedge Funds typically invest in traditional securities, such as stocks, bonds and commodities, but they can also invest in real estate, art, wine or any number of other non-traditional areas of investment. In fact, they are free to use virtually any pick and mix of strategies from the entire range of investment possibilities.

That said, a lot of Hedge Funds will opt for a specific investment strategy and will stick with that strategy for the life-time of the fund.

So what’s in it for the Hedge Fund managers and administrators?

Hedge Fund Managers typically charge a management fee of between 1and 3 percent of the value of the assets under management, regardless of performance. They may also – almost always in the past – charge a performance fee, which can start at around 20 percent of any fund gains above a certain minimum performance hurdle or target value. Managers also generally ‘eat their own dog food’, in that they will also invest in their own Hedge Fund.

Investors in Hedge Funds are informed of the value of their investments via a statement that shows the calculated value of shares in the fund, the Net Asset Value (the NAV). This can be calculated monthly, quarterly or even yearly, depending on the fund. In addition funds are free to choose if they wish to publicly disclose performance figures or not.

Some hedge funds may require additional fees and commissions, and may impose lock up periods, and strict and narrow redemption periods. They may also reject some applications for subscriptions without giving any reasons, and they may also forcibly redeem shares held, and without having to justify their actions.

In addition, some hedge funds use equalization methods – and there are a number of variants – to equitably distribute hedge fund fees amongst its partners, yet other hedge funds do not use equalization methods at all.

The mechanics of participation

So, briefly, how do you get to invest in a Hedge Fund (subscribe), how do you liquidate that participation (redeem), and what happens between ‘subscription’ and ‘redemption’.

Offer: A Hedge Fund details what’s involved in a particular offer in an ‘Offering Memorandum’, also known as a ‘private placement memorandum’. This is typically an ‘enriched’ business plan tied to a specific issuance of shares in a fund. It basically sets out the stall.

Subscription: In order to subscribe to a fund the potential participant in the fund signs up to a subscription agreement, and the conditions laid out in that agreement. Conditions may cover aspects such as minimum subscription amounts; minimum share increments, rules governing the liquidation of participation in the fund; management and performance fees; and, so on and so forth.

Redemption: This is the liquidation of shares in a fund. Typical redemption points can occur from anything from 15 days to up to 180 days, and sometimes more than this, depending on the fund and the rules related to lock-ups, redemption. In addition, redemption options may be linked to other financial charges, penalties and constraints.

So what happens between subscription and redemption?

Custody: A hedge fund subscriber may wish to use the services of a large and reputable financial service provider to act as custodian of their hedge fund shares. In addition to holding securities for safekeeping, most custodians also offer other services such as account administration, transaction settlements, collection of dividends and interest payments, tax support and foreign exchange. (Source: Investopedia).

Dividends: It is very uncommon for hedge funds to pay dividends, as any accruable earnings are realized upon redemption of the shares. However, some fundsdo incentivize the maintenance of subscriptions through the payment of dividends.

Calculating NAV: Periodically – or even on a real-time basis – a hedge fund will recalculate the Net Asset Value of the fund shares. This is done by dividing total value of all securities held by the number of shares. The NAV is more or less subjective in cases where the associated assets are more or less liquid. An extreme example of this may be the calculation of a NAV that has to take into account the theoretical market value of art.

Of course, the mechanics of participation is typically more involved and complex than this.

Who plays, who pays

Investors in Hedge Funds are individuals and institutions (such as foundations, endowments, family offices, pension funds, insurance companies, private banks and funds of funds).

One of the key criteria in the Hedge Fund business is that only people and institutions with money can invest in them. On face value this prerequisite seems a tad bizarre, but there are some very valid reasons for it.

In order to be able to invest in Hedge Funds an investor will need to meet certain legal requirements. They have to be either a credited investor or a qualified purchaser. The qualification is based on net worth and individual income. The qualified purchaser has a higher net worth than a credited investor.

In my opinion the practical rules of Hedge Funds are clear, albeit wrapped up in more indirect language. The biggest rule is: ‘do not put any money into a Hedge Fund that you are not prepared to lose’. The second big rule is: ‘only subscribe to a Hedge Fund with money that you can lose and without the risk of significantly and adversely affecting your lifestyle.’

Of course, this didn’t stop people from jumping on the Hedge Fund bandwagon with little or no clue about what they were getting themselves into.

That’s all folks

Hedge Funds are subject to a dire circle of misleading, banal and frequently reactionary published and public opinion. Which is unfortunate, because it ignores that almost all of the Hedge Funds reflect a culture and style of their managers and administrators, and that in the business there is a a lot of plurality and diversity.

Some Hedge Funds have been the epitome of sharp investment practice, hubris and good old fashioned albeit legal duplicity.

There are macho funds, and non-macho funds, high risk funds and risk-averse funds, highly leveraged funds and funds that use little or no leverage.

Some funds are discrete, some are ugly, some are charming, and some are boisterous and incredibly aggressive. Some are socially responsible, and others might ask “is social responsibility part of the NAV calculation?”

Some funds delight in planning raids on markets, mounting shark attacks on political systems and find ‘justifiable’ enchantment in destabilizing economies and currencies. Other Hedge Funds – in my opinion the vast majority – would never dream of doing such things.

Furthermore, some funds actively encourage responsible investment in developing countries and in other ethical investment strategies. Moreover, there are plenty of examples of funds that sit somewhere between the extremes, so there is no one size fits all when it comes to characterizing funds.

But whatever the style of the Hedge Fund, at the heart of each individual Hedge Fund culture is the culture of the team leaders and team players.

So, are Hedge Funds intrinsically evil?

No, I don’t think so. But that sort of headline grabs a lot of people’s attention, and frequently for all the wrong reasons.

Thank you so much for reading.

[i] Loomis, Caroll J. The Jones that nobody keeps up with. Fortune, April 1966

File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro

All Data: It’s about statistics

30 Friday Jan 2015

Posted by in All Data, Consider this, DW 3.0, Good Strat, Good Strategy, Information Supply Frameowrk, Martyn Jones, Martyn Richard Jones, statistics

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A big computer, a complex algorithm and a long time does not equal science.

Robert Gentleman

To begin at the beginning

Fueled by the new fashions on the block, principally Big Data, the Internet of Things, and to a lesser extent Cloud computing, there’s a debate quietly taking please over what statistics is and is not, and where it fits in the whole new brave world of data architecture and management. For this piece I would like to put aspects of this discussion into context, by asking what ‘Core Statistics’ means in the context of the DW 3.0 Information Supply Framework.

Core Statistics on the DW 3.0 Landscape

The following diagram illustrates the overall DW 3.0 framework:

There are three main concepts in this diagram: Data Sources; Core Data Warehousing; and, Core Statistics.

Data Sources: All current sources, varieties, velocities and volumes of data available.

Core Data Warehousing: All required content, including data, information and outcomes derived from statistical analysis.

Core Statistics: This is the body of statistical competence, and the data used by that competence. A key data component of Core Statistics is the Analytics Data Store, which is designed to support the requirements of statisticians.

The focus of this piece is on Core Statistics. It briefly looks at the aspect of demand driven data provisioning for statistical analysis and what ‘statistics’ means in the context of the DW 3.0 framework.

Demand Driven Data Provisioning

The DW 3.0 Information Supply Framework isn’t primarily about statistics it’s about data supply. However, the provision of adequate, appropriate and timely demand-driven data to statisticians for statistical analysis is very much an integral part of the DW 3.0 philosophy, framework and architecture.

Within DW 3.0 there are a number of key activities and artifacts that support the effective functioning of all associated processes. Here are some examples:

All Data Investigation: An activity centre that carries out research into potential new sources of data and analyses the effectiveness of existing sources of data and its usage. It is also responsible for identifying markets for data owned by the organization.

All Data Brokerage: An activity that focuses on all aspects of matching data demand to data supply, including negotiating supply, service levels and quality agreements with data suppliers and data users. It also deals with contractual and technical arrangements to supply data to corporate subsidiaries and external data customers.

All Data Quality: Much of the requirements for clean and useable data, regardless of data volumes, variety and velocity, have been addressed by methods, tools and techniques developed over the last four decades. Data migration, data conversion, data integration, and data warehousing have all brought about advances in the field of data quality. The All Data Quality function focuses on providing quality in all aspects of information supply, including data quality, data suitability, quality and appropriateness of data structures, and data use.

All Data Catalogue: The creation and maintenance of a catalogue of internal and external sources of data, its provenance, quality, format, etc. It is compiled based on explicit demand and implicit anticipation of demand, and is the result of an active scanning of the ‘data markets’, ‘potential new sources’ of data and existing and emerging data suppliers.

All Data Inventory: This is a subset of the All Data Catalogue. It identifies, describes and quantifies the data in terms of a full range of metadata elements, including provenance, quality, and transformation rules. It encompasses business, management and technical metadata; usage data; and, qualitative and quantitative contribution data.

Of course there are many more activities and artifacts involved in the overall DW 3.0 framework.

Yes, but is it all statistics?

Statistics, it is said, is the study of the collection, organization, analysis, interpretation and presentation of data. It deals with all aspects of data, including the planning of data collection in terms of the design of surveys and experiments; learning from data, and of measuring, controlling, and communicating uncertainty; and it provides the navigation essential for controlling the course of scientific and societal advances[i]. It is also about applying statistical thinking and methods to a wide variety of scientific, social, and business endeavors in such areas as astronomy, biology, education, economics, engineering, genetics, marketing, medicine, psychology, public health, sports, among many.

Core Statistics supports micro and macro oriented statistical data, and metadata for syntactical projection (representation-orientation); semantic projection (content-orientation); and, pragmatic projection (purpose-orientation).

The Core Statistics approach provides a full range of data artifacts, logistics and controls to meet an ever growing and varied demand for data to support the statistician, including the areas of data mining and predictive analytics. Moreover, and this is going to be tough for some people to accept, the focus of Core Statistics is on professional statistical analysis of all relevant data of all varieties, volumes and velocities, and not, for example, on the fanciful and unsubstantiated data requirements of amateur ‘analysts’ and ‘scientists’ dedicated to finding causation free correlations and interesting shapes in clouds.

That’s all folks

This has been a brief look at the role of DW 3.0 in supplying data to statisticians.

One key aspect of the Core Statistics element of the DW 3.0 framework is that it renders irrelevant the hyperbolic claims that statisticians are not equipped to deal with data variety, volumes and velocity.

Even with the advent of Big Data alchemy is still alchemy, and data analysis is still about statistics.

If you have any questions about this aspect of the framework then please feel free to contact me, or to leave a comment below.

Many thanks for reading.

Catalogue under:

[i] Davidian, M. and Louis, T. A., 10.1126/science.1218685

File under: Good Strat, Good Strategy, Martyn Richard Jones, Martyn Jones, Cambriano Energy, Iniciativa Consulting, Iniciativa para Data Warehouse, Tiki Taka Pro

Consider this: Does all data have value?

30 Friday Jan 2015

Posted by in Assets, Consider this, Data governance, data science, Good Strat, Martyn Jones

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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.

Alvin Toffler

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.