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Tomas Sanchez’s blog described how doing the right thing by data can feel like a curse – something that resonated with many of us, who face the same challenge in our organisations. Lisa Allen’s blog proposed some practical advice to tackling “the curse” – giving data a voice, taking a structured approach and using data storytelling. All great suggestions, but what if your organisation still resists implementing them? How can you spot the opposing behaviours and be forearmed with actions to finally lift the curse?
1. Obstacles, arguments and reasons organisations give for not implementing changes
Change is not easy for most of us, but for organisations it can feel especially daunting. Change requires effort and effort requires change. The most common arguments I have heard are:
“It’s too expensive” to make improvements there will be some cost, either in technology or people, and now more than ever, we are at a time when money for additional improvements is scarce.
“We’ve managed ok so far” or “we’ve always done it this way”. People are busy with their day jobs, they’ve worked the same way for years, so it must be good enough, surely?
“We don’t have time for this” usually accompanied by “we just need to deliver”. In other words, nobody has factored in change, they have forgotten to include this within their project/budget/roadmap.
“We have more urgent priorities” a statement that invariably means the arguer does not understand the correlation between successful outcomes and data, and how failure to manage the latter, will probably result in failure to deliver the former.
“If we get the right technology, it will sort the problem”. Surely the most flawed argument of all. Anybody who has ever heard this might as well have heard “abandon hope all ye who enter here!”
2. What are the behaviours to look for?
An organisation’s reluctance to implement change and address the curse is often due to individual behaviours. Here are some to look out for.
· Data is not in the strategy. As data professionals, we assume that everyone understands the cause and effect of poor data management on business goals. However, this is rarely the case and, if your organisation has not committed to improving data management in its strategy, it shows they don’t value it enough to commit to it.
· Quoting anecdotal evidence. As data professionals we deal with evidence and facts, but all too often you can hear incorrect statements repeated in meetings. It happens so often they pass into popular lore. A good response to the often repeated “the data quality is poor” is “can you show me the evidence?” They rarely can.
· Lack of ownership. If an organisation cannot determine who owns its data, it lacks data maturity. Establishing known roles and responsibilities for data is crucial to good data management, and the foundation of doing the right thing.
· Lack of governance. If data governance is not implemented, it shows that the organisation does not feel it is important. It is all very well having technical design authorities, but if these exclude data then a huge portion of the organisation’s assets are uncontrolled.
· Blaming others. Individuals absolve their own responsibility by pointing out others’ shortcomings. If you hear “nobody told me I had to “or “there isn’t any guidance” or “my manager didn’t tell I needed to” you know that the organisation is reluctant to promote and support changes. Roles and responsibilities around data need to start at the top or it will be too easy to find excuses.
· Technology is king. A technology-centric organisation makes data subservient. If a project has budget for technical solutions professionals, but will not fund data professionals, you know that priorities are not favourable to data. If an organisation decides which technical solution is the answer before considering data, you know that they have a long way to go.
3. What can management and organisations do to cope with the lack of change when results are needed?
Data transformations do not happen overnight and change needs two things – Firstly communication from data people. Our practices can be mysterious to those that outside our profession. Secondly commitment from management to promote and support good data practices.
So here is some advice I would give:
· Listen take some time to understand what is impacting the organisation. You don’t need specialist consultants coming into tell you what the problems are, just talk to your employees. They will tell you what is impacting service delivery, and from there it is often easy to diagnose the data problems.
· Look at what is available to help you. There are many community groups and initiatives around data. There are industry standards you can adopt. There is no need to reinvent the wheel. Us data professionals are a resourceful lot, and organisations like DAMA UK exist to help you navigate your way to improved data practices.
· Learn from those organisations who have invested time, effort and funds in making inroads into lifting the curse. Obtain case studies, both within your organisation and outside about what has worked well.
· Leverage the skills and people you already have. People fix data problems, not tools. To make lasting change, you will need to commit to empowering and supporting these people to do a good job.
· Legislate by implementing governance, policy and structures. Good data management isn’t a one-off, it needs monitoring and maintaining. That requires a commitment to invest.
With communication and commitment, data management will improve and the curse of doing right for data can be lifted!
Sarah Burnett is interim Chief Data Architect at Defra and is responsible for building a data architecture service. Sarah has thirty years’ experience of leading data transformation projects.
Find the earlier blogs here:
Tomas Sanchez – The Curse of doing right for data https://www.dama-uk.org/Blog/9222122
Lisa Allen – What is data done right https://www.dama-uk.org/Blog/9320297
