Photo by Natalia Y on Unsplash

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

Photo by sydney Rae on Unsplash

Part 2: What is data done right?

As Tomas Sanchez set out in the first blog of this series, data management can often feel like a curse of doing right for data. I’m going to talk to you about practical advice to help you address some of the challenges you may face.

As many people are aware data is a vital asset for an organisation. It enables better understanding, allows you to gain insight and make better decisions. But organisations don’t always value it as such an asset. As a data professional this is challenging, but it is also your calling to turn this around for your organisation. Here are several practical steps that can help you to do right for data.

1. A voice for data

Whereas other functions like Human Resources or Finance have departments that give them a voice, not all organisations have a Data Department. As a data professional you can ensure data has a voice in your organisation by addressing the following areas:

• Ensure you have senior buy in. Is there a senior manager in the business that understands the importance of data? Can you ask him or her to be the ambassador for the data, to give this perspective on the Board or at the Executive table? This will help ensure you have a data champion at the highest level.
• Form a coalition of the willing. In organisations you naturally have people who are data advocates. These are found across the organisation and are the ones who passionately care about data even though they fill other roles. They may not even realise they are data people. You will need to identify and work with this group. They may be early adopters of your proposed changes, and they can spread best practice throughout your organisation.
• Create a data team. You may already be lucky enough to have one or lead one. You want your data team to be the one everyone wants to work with or in. You want to show your value to the organisation by communicating the great work you are doing and how it is helping deliver business outcomes. Remember to concentrate on the benefit to the organisation and the concrete deliverables. This makes data more tangible.

2. A structured approach

Data spans so many different disciplines and touches every part of the business. It can be difficult to know where to start. Here are some tips:

• Start with what outcomes your organisation wants to achieve and how can data help achieve these. What needs to be different about the data to achieve success for your business strategy?  Is it that you need to exploit your data more?  Is it that you don’t know what data you have? Understanding how your data supports your business strategy will enable you to set out your data strategy.
• Next develop your plan of action. What areas are you going to tackle first and what does success look like?  Any data work can take a while to show real business benefit. People are impatient, and you’ll need to show returns through quick wins in the short term until, over time, you are able deliver the optimal benefit.
• Make it easy for people to do the right thing. Often with data we congratulate ourselves for fixing data problems, but we should really save our celebrations for ensuring these issues never arise. Are there things you can put in place so that you tackle issues upstream before they even become a problem? For example, automatically recording metadata for your data assets, instead of relying on people manually documenting the data they have created or updated.

3. Data story telling

Being able to tell stories about the data and why people should care helps engage your organisation and get them onboard. Here are some things to consider:

• Imagine - Can you use stories that help set out a different future? Can you engage people’s imagination to envisage a different future? Using examples of how the world is changing and how your business needs to adapt to deliver your business strategy can help spark the interests of many. Be careful though as some may think the possibilities are fanciful.
• Scare stories – no one comes to work to do a bad job, so people may become defensive about examples of bad data practice from within your own organisation. Therefore, it’s always good to have cases from other organisations.  You can find these easily in the media. Anything from data quality issues to data protection breaches and more. Use these stories to help you with your own journey.
• Progress – don’t forget to tell the story of how things are progressing. Tell people how you are delivering your data strategy and what that means for the business in meeting its aspirations. You cannot communicate too much – keep data on the radar.

And finally, for me, it’s all about positivity. Data transformations can be hard. But with drive and enthusiasm success will come and, when they do, celebrate the successes. If these things were easy then your organisation wouldn’t need you.  But they do.

Lisa Allen – Is Head of Data and Analytical Services at Ordnance Survey. A seasoned data professional with experience across government. Lisa is a committee member of DAMA UK nurturing a community of data professionals across the UK.

This is part of a three-part series. Next hear from Sarah Burnett:

Part 3: Main symptoms of the curse

What are the main obstacles, arguments and reasons that organisations give for not implementing changes? What are the behaviours to look for? What does management and those organisations do to cope with the lack of change when results are needed.

You can read part 1 here:

Part 1: The Curse of doing right for data - Tomas Sanchez

https://www.dama-uk.org/Blog/9222122

https://www.nicolaaskham.com/blog/2020/10/1/do-inbspreallynbspneed-a-data-governance-policy   

How often should You Revisit Your Data Governance Maturity Assessments?

https://www.nicolaaskham.com/blog/2018/1/4/ask-the-data-governance-coach-what-is-the-difference-between-policies-and-standards

https://www.nicolaaskham.com/blog/2020/7/23/data-governance-interview-with-ed-mathia

The Curse of doing data right

Photo by Jude Beck on Unsplash

Part 1: What is the curse?

 Very few people will disagree that having a solid foundation for data is a good idea. This includes many of those things that we hear all the time, such as good data quality, common standards, or a good catalogue where we can find all our data assets. This is the same for more technical foundational topics, such as metadata, master / reference data, data modelling, and so on. Once we, data professionals, can explain what these things mean, the general response tends to be positive: essentially, everybody agrees these are good things to have.

 Getting to implement this foundation, however, is a different story. Because data is the backbone of most organisations these days, any serious attempt to review and modify how data is managed will affect users and could fundamentally change their ways of working. And it is then, when users realise that they are at the core of implementing changes across the organisation. It is their responsibility too, not just the data professional job and here are where the problems start. The same organisation that has probably invested in hiring data professionals to “fix” its data problems, will start making excuses for why many of the measures can’t be implemented. Users and management will argue that operations will be disrupted, deadlines will be missed or that it is not the right time. Some organisations will agree to proceed with the less disruptive proposals, but of course, those will also be the ones with less impact or will incur technical debt that will need to be fixed later. Chances are that months and years will pass and little will change; at best, there will be an improvement of awareness and some minor initiatives will be carried out. At worse, the organisation will dismiss advice or try to solve the problems by investing in technology products, which will never work because the foundations were never fixed.

 Data in an interesting technical field. It is in so much demand, but so misunderstood. There are a wide variety of areas of expertise with radically different skill sets, and yet often the field gets oversimplified by putting all data professionals in the same bucket. For example, data scientists / analysts are many times thought of as holding the key to solve many data issues. But the issues normally stem from poor data quality or interoperability, which is not something that data scientist or analysist can (or should) address. Along the same lines, data problems are something to be resolved upstream i.e. once the data has been collected and stored. But, to solve quality issues one needs to start at source i.e. where data is collected, stored and shared.  This misunderstanding is one of the main reasons why there is such a disparity between the willingness to invest in data professionals and the reluctance to carry out their advice.

 Organisations are ill-prepared to undertake the scale of changes that are being advised. They put in place obstacles to attempt managing the disruption, and so the main reasons why these change projects take such a long time are not technical, but cultural. Work is carried out to justify investment, but there is unwillingness to fix the root cause of the problem. Data scientists / analysts are hired to produce results, which invariably means creating inefficient and inconsistent shortcuts to put the data in a form that can be analysed. In turn, the data professionals that provided the advice for change in the first case, see that advice dismissed or underappreciated, and fall victims of the contradiction in which the organisation finds itself.  As those professional become frustrated, and as they continue pointing out how the organisation is doing things in the wrong way, they sometimes can become disliked and even ostracised, which increases their frustration as, in their view, they are just trying to achieve what they were hired to do.

The cycle continues, with investment in data infrastructure but without the willingness to implement the necessary changes.  For those responsible for the change, sometimes this situation might feel like a curse, the curse of doing right for data. But it’s not all doom and gloom; there is a growing community of data professionals who are supporting each other with doing data right. Which is exactly what our next blog will talk about!

 Tomas Sanchez

This is the first blog of a 3-part series looking at the challenges of data professionals within their organisations. Join us next month where Lisa Allen will talk about what data management changes are commonly required and why organisations find them challenging.

Tomas is currently the Chief Data Architect at the Office for National Statistics, where he is leading ONS’s data strategy as well as being responsible for a number of data products. Tomas also regularly gets involved in forums and initiatives to foster the use of good data management practices across government.

Can there be more than one Data Owner per Data Set? – Nicola Askham – click here

Data Quality – a Multidimensional Approach

In my blogs and articles over the lockdown period I’ve avoided talking about the impact of the Covid 19 pandemic and the heavy reliance on good quality data to support the models needed to combat and mitigate its effects.   I have decided to break my silence in this blog  as a major data story recently hit the headlines in my part of the world, Wales in the United Kingdom. This story was literally so close to home that I felt impelled to highlight and comment on it, and use it to stress why the need for good data quality is more important than ever.  Click here to read the blog in full 

https://www.nicolaaskham.com/blog/2020/8/7/data-sharing-agreements