I was asked the question appearing in the title of this short article recently and penned a reply, which I thought merited sharing with a wider audience. Here is an expanded version of what I wrote:

Let’s start by considering some related questions:

1. Why are so many businesses still doing a bad job of controlling their costs in 2019?
    
2. Why are so many businesses still doing a bad job of integrating their acquisitions in 2019?
    
3. Why are so many businesses still doing a bad job of their social media strategy in 2019?
    
4. Why are so many businesses still doing a bad job of training and developing their people in 2019?
    
5. Why are so many businesses still doing a bad job of customer service in 2019?

The answer is that all of the above are difficult to do well and all of them are done by humans; fallible humans who have a varying degree of motivation to do any of these things. Even in companies that – from the outside – appear clued-in and well-run, there will be many internal inefficiencies and many things done poorly. I have spoken to companies that are globally renowned and have a reputation for using technology as a driver of their business; some of their processes are still a mess. Think of the analogy of a swan viewed from above and below the water line (or vice versa in the example below).

I have written before about how hard it is to do a range of activities in business and how high the failure rate is. Typically I go on to compare these types of problems to to challenges with data-related work ^[1]. This has some of its own specific pitfalls. In particular work in the Data Management may need to negotiate the following obstacles:

1. Data Management is even harder than some of the things mentioned above and tends to touch on all aspects of the people, process and technology in and organisation and its external customer base.
    
2. Data is still – sadly – often seen as a technical, even nerdy, issue, one outside of the mainstream business.
    
3. Many companies will include aspirations to become data-centric in their quarterly statements, but the root and branch change that this entails is something that few organisations are actually putting the necessary resources behind.
    
4. Arguably, too many data professionals have used the easy path of touting regulatory peril ^[2] to drive data work rather than making the commercial case that good data, well-used leads to better profitability.

With reference to the aforementioned failure rate, I discuss some ways to counteract the early challenges in a recent article, Building Momentum – How to begin becoming a Data-driven Organisation. In the closing comments of this, I write:

The important things to take away are that in order to generate momentum, you need to start to do some stuff; to extend the physical metaphor, you have to start pushing. However, momentum is a vector quantity (it has a direction as well as a magnitude ^[12]) and building momentum is not a lot of use unless it is in the general direction in which you want to move; so push with some care and judgement. It is also useful to realise that – so long as your broad direction is OK – you can make refinements to your direction as you pick up speed.

To me, if you want to avoid poor Data Management, then the following steps make sense:

1. Make sure that Data Management is done for some purpose, that it is part of an overall approach to data matters that encompasses using data to drive commercial benefits. The way that Data Management should slot in is along the lines of my Simplified Data Capability Framework:

   
    

2. Develop an overall Data Strategy (without rock-polishing for too long) which includes a vision for Data Management. Once the destination for Data Management is developed, start to do work on anything that can be accomplished relatively quickly and without wholesale IT change. In parallel, begin to map what more strategic change looks like and try to align this with any other transformation work that is in train or planned.
    
3. Leverage any progress in the Data Management arena to deliver new or improved Analytics and symmetrically use any stumbling blocks in the Analytics arena to argue the case for better Data Management.
    
4. Draw up a communications plan, advertising the benefits of sound Data Management in commercial terms; advertise any steps forward and the benefits that they have realised.
    
5. Consider that sound Data Management cannot be the preserve of solely a single team, instead consider the approach of fostering an organisation-wide Data Community ^[3].

Of course the above list is not exhaustive and there are other approaches that may yield benefits in specific organisations for cultural or structural reasons. I’d love to hear about what has worked (or the other thing) for fellow data practitioners, so please feel free to add a comment.
 

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Notes

[1]	For example in: 20 Risks that Beset Data Programmes Ideas for avoiding Big Data failures and for dealing with them if they happen Ever Tried? Ever Failed?
[2]	GDPR and its ilk. Regulatory compliance is very important, but it must not become the sole raison d’être for data work.
[3]	As described in In praise of Jam Doughnuts or: How I learned to stop worrying and love Hybrid Data Organisations.

Another article from peterjamesthomas.com. The home of The Data and Analytics Dictionary, The Anatomy of a Data Function and A Brief History of Databases.

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