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Author: jphare

15 anecdotes that may suggest its time to look under the bonnet at your Information Management practices

  |   Blog

Many organisations claim they don’t have Information Management challenges worth tackling.  More often than not however the below anecdotes are indicative of hidden costs, risks, inefficiencies and opportunities left unexploited.

 

If any of these phrases seem familiar perhaps its time to look under the bonnet?

 

“We have great people, trying to capture that knowledge in IT systems is a waste of time.”

 

“We have loads of data, no one ever looks at it anyway though so what’s the point in optimising it?”

 

“We’re too small to warrant spending time looking at better managing our data.”

 

“Implementing standards?  Everything works fine at the moment though doesn’t it?”

 

“As far as I’m aware data problems have never caused an operational incident”.

 

“Data?  That’s for IT to worry about with their databases, datacentres and datawarehouses.  They love that stuff.”

 

“If they want to record data in that system leave them to it.  It keeps them busy and content.”

 

“Static data is monotonous and easy to manage, just hire anyone in to plug it into the system”.

 

“IT don’t understand the realities of Data Management”.

 

“It’s about culture and responsibility.  Their complaints about the data management system being substandard and data models are nonsense.”

 

“We think the data quality is good enough for our needs”.

 

“In the time it takes to get the data together we could have made several decisions.”

 

“We don’t have a lot of data here.  Effective information management sounds like a nice to have.”

 

“Modelling and metadata – yet another reason for why IT departments are bloated”.

 

“Just get the data to our counterparty in whatever format.  It’s their problem to reformat it and figure out what it means”.

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Fragmented or comprehensive – the state of the ‘Data Profession’

  |   Blog

I’m continually surprised by the responsibilities and titles of new data roles emerging within the ‘data profession’.  Admittedly this is a fairly nebulous concept and I suspect amongst practitioners there are a variety of opinions as to what the composition of this space looks like.  However there are within this area certain trends that I suspect most practitioners would agree on.  Data is being taken more seriously by organisations than ever before with comparable growth in terms of dedicated ‘data people’, investment and technology.

 

As part of this industry growth most organisations with mature Information Management capabilities possess Data professionals working on both the technical side of the spectrum (data modellers, architects, metadata specialists and so on) and on the softer, business side (data quality, data governance and data management professionals).  However does this maturity necessarily translate to effectiveness and efficiency?  Has this growth in resources and profile led to healthy development for the profession or fragmentation?

 

Our experience has been that many organisations believe hiring data professionals across the spectrum is the most important requirement for a cohesive approach.  More often than not however we come across the same problems that have dogged the industry since its earliest days.  Technical experts unable to convey the business benefits of their proposed solutions whilst those at the ‘data coalface’ continue to exhibit a shortfall in technical appreciation and understanding.  This often leads to incoherent Data Strategies and many organisations’ data functions being viewed as academically rather than business driven.  Worse still sometimes data fiefdoms emerge with rival tribes battling for control of an organisation’s data landscape.  Hardly productive for a discipline that still has some way to go to achieve its Public Relations ambitions.

 

So what is the solution?  Appoint a benevolent data dictator in the form of a Chief Data Officer to unite the tribes?  Clearly divide up responsibilities and requirements into separate expertise areas?  Rarely does a data problem area however simply consist of modelling conundrums, quality defects or accessibility issues – usually there are a range of compounding issues signalling a need to get the experts working together.  A coherent, well sponsored and widely agreed Data Strategy is clearly a must.  However one thing we’re often surprised about is how little time is spent by each end of the spectrum understanding the other’s day to day problem set.  Data Management, Governance and Quality teams too infrequently engage the Modelling, Architecture and Metadata experts with queries and requirements and vice versa.  If we are all experts working in the ’data space’ what are we afraid of?

 

So what does this future look like?  Are these barriers likely to dissipate and are there any ways to avoid these situations?  Certainly on the tools front the technology is becoming more accessible and intuitive than ever before.  There are an array of adaptors for instance in most cleansing, modelling, reporting & visualisation tools meaning loading data is itself no longer a hugely significant requirement.  The social media generation also have far more of an appreciation of both sides of the data spectrum than before.  However this has also encouraged a somewhat ubiquitous view of data – it should just work with minimal effort.  There is a risk that less and less time will be dedicated to getting the fundamentals right.

 

There will always be many ways to skin a cat.  In our view in order to become less cyclical in nature and a trusted advisor within organisations the most successful data professionals of the future will possess wider skillsets, deeper technical understanding and also the softer skills necessary to clearly articulate problems and solutions.  Whilst specialists will always exist to as efficiently as possible deliver proven solutions simply referring data problems to them will diminish as an acceptable solution.  A cohesive Data Strategy should always originate and be owned by the business, however it also has to be inclusive if it is to succeed.

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