Is the "Data Asset" really different?

In June 2015 for the BCS and DAMA I presented a seminar at an event called "Data, the vital organisation enabler" Information is at the Heart of ALL of the Business
During this I raised the question, "is the data asset really that much different form other assets?"  We hear a great deal that Data is an asset, it's got to be managed, few people in the business understands us and so on.

Don't get me wrong, I'm not trying to cast doubt on the importance of data as an asset, but I wanted to raise the level of debate from a subliminal nod to a conscious examination of the characteristics of different "assets" and to compare them with the 'Data asset".



Firstly, let me re-iterate that Information IS absolutely at the heart of the business, my recent white paper talks at some length about this and the diagram here briefly illustrates 4 business architecture disciplines & the vital role of data in each of these.

However what I want to raise here is just what are some of the characteristics of core assets in the business, and if as we all say data IS one of those  key assets, how, if at all do these characteristics differ in the "Data asset" compared with  other common assets that we frequently encounter in our organisations?

 

Assets & Characteristics

So first of all lets have a think about some other "assets"?  I've picked 7 other assets seen across a variety of businesses and tried to compare them with the "data asset". 

The assets I've selected for this comparison are:

* Oil
* Money
* Blood
* People
* Property
* Materials
* Intellectual Property (IP) and of course
* Data.



The characteristics of the assets themselves required more consideration. After much thought and batting the notion around with others I settled upon these 5 characteristics:

• Is the asset Copyable, i.e. without resorting to the realms of science fiction "replicator" machines
• Does use of the asset in some way deplete it
• Is it straightforward, and/or usual practice to ascribe a monetary value to the asset
• Is the asset a real tangible thing or an abstract concept
• Does the asset have to be processed in some way to yield value

Now I'm sure that I could have come up with further asset types and asset characteristics, and I may well do so as this analysis develops, but for now these are the ones that I start with.

Analysis 

So let's analyse these assets against the characteristics & see what (if any) conclusions we can draw from it?

Oil

Oil is not copyable, and most definitely using it depletes it.  It is definitely usual practice to give a value to oil (the $50 barrel for example) and it is a real concept. Finally it has to be processed to be turned into something useful like petrol, diesel or plastic.

 

Money

So you can't (legitimately) copy money, and as I know all too well with two sons at University, using money depletes it, and naturally you give a value to money.  It's mostly a real concept being underpinned by Gold stock, and doesn't have to be "processed' to deliver value.

Blood

Blood isn't copyable in the mainstream (although as we speak blood substitutes are being trialled), and use of it depletes it (it has to be re-cleaned & oxygenated after use).  It's not too difficult to ascribe a value to it, and it is a real concept.  Finally it has to be processed by our organs to yield value.

People

People as we know them are not copyable (although biological cloning is possible).  I've said that use of people does not deplete the resource as we can apply our skills & intellect many many times.  However, people do age and limbs and minds fade so perhaps this should be answered as "partly true".  It's not widespread practice to ascribe a monetary value to a person except in a few cases (e.g. professional sportsmen).  People are real and without trying to get too philosophical, they have to do something to yield a value.

Property

Property such as buildings are not copyable.  Sure you can have a plan for a building & use that several times, but its using different bricks, is on a different site and so on.  The Eiffel Tower in China is a fake!  Using a property does slowly erode it, things wear out and need to be maintained.  Property does have value & it's usual practice to give it such.  Property is a real concept, but doesn't have to be processed to generate value.

Materials

So here I'm talking about raw materials.  Again, without a sci-fi replicator they are not copyable, and just like a match the act of using them depletes them.   Most materials have a monetary value easily ascribed to them, for several that's the basis of the commodities market.  They are real not abstract things and pretty much for the most part have to be processed to yield a value.

Intellectual Property (IP)

IP is not legally copyable.  IP thrives on being reused so is not depleted by use.  There is frequently a monetary value allocated to IP and much like a thought or an idea it's mostly an abstract concept.  Finally, IP must be used (processed) to gain real value from it.

Data

So what about data; how does this stack up against the asset characteristics?
Data is copyable; with digital media any number of copies can be taken without the data being degraded.  Using data does not erode it or make it wear out. Sure the relevance of the data may decrease over time but it does not wear out.  Whilst there is much talk about "monetizing" data, this is still not a widespread practice but will no doubt become some in the future.  Data is an abstract concept since its representing something else.  Data needs to be utilised by processes to have value (and conversely processes must have data to operate upon).

 

Conclusion

Having looked at these 8 different assets, and the 5 characteristics is there anything that jumps out at us? 
If we look for assets which have the same values of characteristics as observed in "data" then we're going to be disappointed.  Of the 5 characteristics, 3 of the assets (Money, Property and Materials) have zero common values of characteristics.
2 of assets (Oil and Blood)have one common characteristic value shared with "Data".  Intellectual Property (IP) has two common characteristics, and heading the pack with three common characteristics is People.  It's interesting to note though, that there aren't any of the assets that share 4 let alone 5 of the characteristics as we see in Data.



Thus it's probably reasonable to conclude that: the Data Asset IS different to other business assets that we encounter.

Furthermore, as described in my white paper all of the business depends upon data for its wellbeing.

Unfortunately, we still encounter organisations where the various disciplines of Information Management are not understood (or more frighteningly are knowingly not addressed).  Indeed, Professor Joe Peppard wrote "The very existence of an organisation can be threatened by poor data quality.”  So yes if as we suggest here that it is different, then the management of the data asset requires specific skills and capabilities, the Information professional.

Wise organisations are realising that Information IS a vital asset, it IS worthy of being managed professionally, and yes it IS different.

How British Airways & my Vet reinforced a valuable Data Governance lesson

A week ago my son & I took our Savannah cats to the vets for their regular inoculations.  We had the first appointment of the afternoon (2pm) and this was a 10 minute appointment.  I had a very important audio at 3pm & wasn't concerned; after all we had plenty of time.

We weren't seen for this "first" appointment for at least 35 minutes, were kept in the dark regarding status and then afterwards were attempted to be "up sold" other services.  I barely made the audio on time & needless to say was underwhelmed by the experience. 

Yesterday I headed to the U.S. from LHR to visit a client for a big Data Governance initiative I'm advising on. I'd arranged to meet the client 3 hours after my scheduled landing time into JFK. Everybody boarded the BA flight on time, the doors closed .... but we sat on the tarmac & went nowhere.  
The outcome was similar to the vets visit.  

We were late.

There was a big delay (considerably more than 35 minutes).  

However unlike my visit to the vets I didn't feel immensely cheesed off with this experience.

Why was this & why did it make me think about Data Governance programs I've observed?

In the first example, the reception staff at the vets told us nothing.  We'd checked in early for a 2pm appointment and they confirmed we indeed had the 2pm slot.  But no updates were provided, no indication of what the situation was, no advice on whether we should come back later .. absolutely nothing.  We sat in the waiting room becoming increasingly frustrated despite asking for updates several times.  

In the BA case, I and the other 300+ passengers were continually updated with what  was going on, why there was a delay and when we expected to move.  I was able to  contact the client in the US & re-set their expectation on my arrival too.

So whilst both had a similar outcome - ie we were late; one left me with a feeling that as far as they were concerned I just didn't matter.

A while back I wrote an article "Data Governance is about Hearts & Minds - Not Technology"

This message is continually reinforced as I see good (and bad) Data Governance initiatives globally.  
There are many things that characterise the successful ones from the unsuccessful.  But one of the major differentiators is the presence and quality of the communication  program that runs along side the Data Governance initiative.

You have got a communication program?
I sure hope so.

Successful Data Governance initiatives go out of their way to ensure communication is frequent, effective and covers the people who are going to be affected not just the sponsor(s).  And above all it has to be realistic and regular.  There's no point starting a Data Governance program with a great fanfare & then not keeping people appraised of progress.   This is true whether it's a full on strategic Data Governance initiative or even if you're embarking on Data Governance by stealth.

The most successful initiatives combine regular semi-formal updates with informal  communities of interest.  The COI is a great way to get involvement from a wide variety of stakeholders affected by the DG initiative and really helps to tease out issues that frequently the Data Governance program designers hadn't anticipated.  

I've run communication sessions on Data Governance initiatives where we've taken a previous data "horror story" from within the organisation and then "dry run" it through the new Data Governance model.  In fact, as part of designing the target Data Governance model I always use a number of real & hypothetical scenarios to validate the target operating model & organisation structures. This both demonstrates the effectiveness of the proposed target model and provides additional understanding to and feedback from the  stakeholders - it's frequently the ah-ha moment for many. 

So, don't underestimate or overlook the critical importance of baking in a communication strategy into your Data Governance initiative.  Your great strategy and Data Governance structures can be rendered useless without the understanding and buy-in from the people who are going to be affected by it.

The bottom line:

Successful Data Governance really is about Hearts & Minds.  

The Bookbinder, the Librarian & a Data Governance story

Over the last few years I have worked upon several high end Data Governance programmes and listened to several excellent (and even more awful) Data Governance presentations at conferences.  I have also had the honour of sitting on the panel of judges at the annual Data Governance best practice awards.

Two of the recurring themes, irrespective of Industry sector are:

1) How do you make the "business case" for Data Governance; and 

2) How best to encourage "the business" to take real ownership responsibility for data.

I intend to cover the first point in more detail in a separate blog posting, but for now the key thing to mention is to keep the case "real". Make it relevant to real business problems that are or have been encountered. Collect "horror stories" (note: please don't use the phrase "burning platforms" for horror stories if you ever intend to work in the Oil & Gas sector).  Armed with the horror stories develop strategies that demonstrate how the proposed DG approach would have trapped these (I like to use simple swim lane diagrams to illustrate these scenarios) and then develop interim transition organisation structures for the client to migrate to.

Moving to the "ownership" question, just how can you best encourage "the business" to take real ownership responsibility for data?  Firstly I hate the term "the business" but I'm not going to get all prissy and go on about that.

The challenge many people face regarding Business Ownership of data in the context of a Data Governance strategy is that most business folks either a) kind of assume its IT who do this anyway; and b) have little frame of reference as to what's actually involved ... what does the "own the data" really mean?  To many it sounds like extra work!

Trying to come up with a meaningful story or analogy relating to Data Ownership has proven difficult.

Then a few weeks ago whilst interviewing several CxO's during a DG strategy at a big Bank I had a light bulb moment.  In the fabulously lush offices were bookcases with some of the old ledgers from the banks early days.

During our discussions we reminisced about the days when the Accountants & Bankers would, using their best copperplate hand writing enter details into the ledgers in best double entry book keeping style.  They would also add to separate ledgers details of debtors and creditors.   Sometimes this would be done initially on vine vellum or parchment paper and then passed to the bookbinders to beautifully bind these together inside leather book covers and fabulous seam stitching.  Following this the bound ledgers would be filed by the librarians typically in date order but with additional customer index cards so that they could readily be accessed when required.

During our reminiscences, I said to the Bank CxO's "so it was the bookbinders who "owned" the data then as they controlled where it was stored?"

Light chortles ensued, so I replied, "well if not the bookbinders wasn't it the librarians who owned it as they were the people who controlled how it was indexed and archived to provide easy retrieval?"

No, no they said.  It's was the Chief Accountant or the Head Teller, or Account Manager who "owned" the data then as they were the real interface with the customers.

Ahhhh I smiled, so what's changed now?  Why have you passed "ownership" to the modern day bookbinders and librarians ie IT?

The "light bulb" revelation moment was priceless. At that point they got it.
 

Now I realise any analogy can be picked apart & before IT folks get too defensive I know there's more they do, however the analogy worked for these guys.
 

From this point on in our discussion, the concept of business ownership of data was firmly accepted. Following the CxO's endorsement of the DG programme the organisation structures, roles and responsibilities are slotting in nicely.  A key enabler to this program's success is getting the hearts & minds culture change message sorted and providing on-going mentoring to the Data owners.
 

IT are fully bought in & still "own" the technical systems environments whilst playing a major part in data custodianship.

Data Governance is about Hearts and Minds, not Technology

Unsurprisingly, the principal point of discussion at FIMA 2012 was the area of information management and the rise of its importance within the finance sector. With regulatory pressure driving interest – hardly something the finance industry is not used to! – along with the proposed legal entity identifier which is pushing all businesses to have a growing and willing demand for detailed, even real-time, knowledge, information management was a topic that permeated almost every discussion at the three day event in London.

Of course, increasing awareness and debate around this topic can surely only be good news for the industry. There is clear benefit in those in the finance sector now realising that failure to manage data effectively – and therefore conform to legislative and regulatory requirements – can have catastrophic effects, resulting in imprisonment as well as businesses being shut down. After all, where other sectors, such as pharmaceuticals, have been deploying information management systems for some time, in the finance arena it is a surprisingly relatively new concept.

Therefore, the interesting workshops dedicated to information management at FIMA 2012 were very welcome and apt, however these could have held more relevance through cross-industry comparisons. Had these presentations and workshops shown delegates examples of successful deployments of information management systems and processes in a relatively comparative industry, than those who were slightly on the fence about the need for information management would have left with a solid understanding of how such a system can really benefit a business.

On a related note, the drive for organisations to hire a Chief Data Officer was also highlighted at FIMA 2012. It is becoming glaringly apparent that the role of a CIO (largely though their typical 

experience) is solely to manage IT systems and infrastructure, and information management rarely 

therefore goes beyond its protection and storage. In order to instead manage data appropriately as a corporate asset, organisations must therefore hire or internally develop an individual to take responsibility and ensure this data governance – a trend I would actively encourage in the near future.

Overall, FIMA 2012 stoked the coals of a rising Information management emphasis within the finance sector. It is apparent that the industry is thankfully now seeing such activity as a necessity. Whether this is through fear of legislative backlash or a drive to improve efficiency and visibility is largely immaterial, provided there is a recognition that a failure to store, manage and use data appropriately is likely to lead to regulatory or customer service-related horror stories being unveiled at FIMA 2013. 

Chief INFORMATION Officer - Not really!

The news that the Prudential has been fined by the Information Commissioners Office (ICO) after a "mix-up" over the administration of two customers’ accounts should send a further warning to CIOs and Compliance officers that managing information as real critical business asset must be taken seriously.

Chris Bradley, an well known independent Information Strategist has been evangelising the Information Management message across the world for many years said:
“Unfortunately, only a few companies are really serious regarding management of information as a vital corporate asset.  If the assets of cash or physical property or employees were treated as poorly as Information there would be major scandal, but the mis-management (all be it unintentional) of information is fast becoming a critical impedance to business success”

The Prudential mix up led to tens of thousands of pounds, meant for an individual’s retirement fund ending up in the wrong Customers account.  Bradley further commented that:
“This is important because it is the first ICO penalty served which does not relate to loss of data, but rather puts the spotlight firmly on the absence of sound Data Governance and Master Data Management in companies”

The original error was caused when the records of both customers, who share the same first name, surname and date of birth, were mistakenly merged in March 2007.

Stephen Eckersley, ICO head of enforcement, said, “In this case two customer files were consistently confused and the company failed to remedy the situation despite being alerted to the problem on more than one occasion before it was finally resolved”

Chris has been advising his clients upon the vital importance of Data Governance and the critical role Master Data Management (MDM) plays in this.  He successfully introduced a Business focused MDM approach into Global organisations in the Finance, Oil & Gas and Pharmaceutical sectors.  Acknowledged Information Management thought leader and author Bradley further commented “We’re delighted to help our clients truly see the value that effective business focused Master Data Management plays and how it is critical to achieving effective information governance”

He further continued, “make no mistake, this is one of the most important considerations for CIO’s in just about any organisation that is subject to any degree of regulatory or compliance pressure”

His insight is echoed by Gartner whose recent research stated “By 2016, 20 percent of CIOs in regulated industries will lose their jobs for failing to implement the discipline of information governance successfully” The same 2012 Gartner survey also supports his Information Management position stating: “Through 2016, spending on governing information must increase to five times the current level to be successful”.

The good news however that is some enlightened organisations are recognising the importance of managing data as an asset, however several holders of the CIO role are not really taking responsibility for Information but rather focus upon the delivery of Technology & Applications.

The realisation that Information must be managed as a corporate asset has given rise to the new phenomenon of the Chief Data Officer.  As recently as June 21, 2012, according to a survey by GoldenSource Corporation over 60 percent of firms surveyed are actively working towards creating specialized data stewards, and eventually Chief Data Officers, for their enterprise.

So a few years after the financial crisis, institutions are still struggling to get a 360-degree view of their data.  Considering organizational, policy, and behaviours within which a data control framework operates is as important as the underlying technology services that enable it. Appointing data stewards and Chief Data Officers to incite governance across these firms will be crucial to success.

Bradley concludes that the Chief Data Officer, distinct from the Chief Information Officer will be one of the top critical hires in 2013 - 2015.

DFD's and ELH's ... Back to The Future?

I was recently asked by a client and also at a conference, "we need to be able to model flows of data - how do we do that"? How to model Data Flows.....hmmm that'll be Data Flow Diagrams then.

These were one of the great features in methods like LSDM and SSADM and was pretty well supported by early generation CASE tools. I still remember with some fondness how useful these were and we used them extensively in the 80's and 90's. With DFD's there was some problem with determining how to create diagrams of the appropriate level, but TBH this was really a question of practitioner experience. Simplified Level 1 DFD

As the fundamental problem that was addresed by DFD's still exists, I'm not really sure why modeling tools now don't support this any more.

I was also asked during a data modelling class, "we know we shouldnt create separate entities for each state that one could be in, but how do we model the change of entity state"?

Just in case youre not sure what this means, a simple example of state change would be how a Suspect becomes a Prospect, then a Customer, then a Gold Customer and maybe a lapsed Customer. All these are examples of when the entity has changed state over the history of its lifetime.
Here's a simple example for a Purchase Order: Purchase Order Simplified ELH

So for a bonus point, the approach to modelling the change of state of a Data Entity over time is Entity Life Histories and State Transition Diagrams.  Simple State Transition Diagram
These were also very popular in the 80's and 90's but again seemed to become unpopular for a while and not widely supported by modeling or CASE tools. Fortunately their importance is now being recognised again & I've increasingly seen these (or similar approaches) being picked up again recently.

The problems that DFD's, ELH's and State Transition Diagrams address haven't gone away, so let's use the approaches that actually work! Maybe it's a case of back to the future?

Data Governance; a vital component of IT Governance

Thursday 1st March ... I have just completed delivering a presentation on Data Governance at Ovum London conference http://governance-planning.ovumevents.com/
I'll add a link to the slides here shortly.

I'm sure that we all know that data is growing at a vast rate, however there's been an even bigger problem concering uncontrolled growth that I have recently read about ...
 ... 12 Grey Rabbits were brought to Autralia in the 19th Century for sport.  After 2 years there were in excess of 2million per year were being shot & still the population wasnt dented.  A few years later the population was over 400million.  So, even the Data explosion highlighted by the 2011 IDC Digital Universe study hasn't yet reached these proportions.

IT Governance:
From several of the well established frameworks (eg ITIL), the common key components of an IT Governance framework seem to be:

1) Strategic Alignment: 

Alignment of the business and IT strategy with regard to the definition as well as the review of and improvement in IT’s contribution to value.

2) Value Delivery:
Within their service cycle, IT services in their entirety bring a benefit in respect of the corporate strategy and generate added value for the enterprise.
3) Resource Management:
Efficient management of resources such as applications, information, infrastructure and people, as well as optimization of the investment.
4) Risk Management:
Identification and analysis of the risks in order to avoid unpleasant surprises and to gain a clear understanding of the company’s risk preference.
5) Performance Management:
Monitoring and control of all performances in terms of their orientation towards the corporate strategy.

Looking now at  Data Governance, some of the key areas that need to be considered, certainly to folks more used to IT Governance are:

1) There are usually 3 main drivers for Data Governance:
Pre-emptive:  Where organisations are facing a major change or threats. Designed to ward off significant issues that could affect success of the company.
Reactive: Where efforts are designed to respond to current pains
Pro-active:  Where Governance efforts designed to improve capabilities to resolve risk and data issues. This builds on reactive governance to create an ever-increasing body of validated rules, standards, and tested processes.

2) Data Governance can be implemented in 3 ways, often these may overlap (Tactical, Operational, Strategic).

3) There is certainly no "one size fits all" approach to Data Governance. Need to have a flexible approach to Data Governance that delivers maximum business value from its data asset.
Data Governance can drive massive benefit, however to accomplish this there needs at least to be  reuse of data, common models, consistent understanding, data quality, and shared master and reference data.
Organisationally, different parts of the business have different needs, and different criteria for their data.  A matrix approach is needed  do these different parts of the organisation and data types are  driven from different directions.
However, no matter how federated the organisation may be there will be some degree of central organization required.  This is to drive Data Governance adoption, implement corporate repositories and establish corporate standards
The IPL Business Consulting practice have a flexible DG framework that can be tailored to help.

4) Communication & stakeholder engagement is key.  No matter how brilliant the framework is, or how great your polices or DG council are, if you dont adequately engage and communicate with the stakeholders, the DG initiative will go nowhere.

5) Finally, all of this is only important if Information REALLY is a key corporate asset for your organisation ..... so ask yourself, is it?

So IT Governance vs. Data Governance?
In summary, Data Governance is a vital frequently overlooked component of an overall IT Governance approach.  Remember the 5 commmon components of an IT Governance approach?  Well, lets apply these in a Data Governance context and we see ...

1) Strategic Alignment:

Alignment of the business information needs and the IT methods and processes for delivering information that is fit for purpose.

2) Value Delivery:
Delivering information to the requisite quality, time, completeness and accuracy levels and  optionally monetising the value of information.
3) Resource Management:
Ensuring people, and technology resources are optimised to ensure definition, ownership, and delivery of information resources meet business needs.
4) Risk Management:
Information security, backup & retention and delivery are balanced against regulatory and accessibility needs as befits the company’s risk preference.
5) Performance Management:
Monitoring and control of Data Governance roles, responsibilities and workflows such that they meet the demands of the corporate strategy.