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Nurturing a community of data professionals in the UK who champion the value of data management

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  • 1 Jul 2020 8:22 AM | Lisa Allen (Administrator)

    https://www.dataiq.co.uk/articles/walking-the-tightrope-5-tips-for-writing-a-data-strategy

    Walking the tightrope: 5 tips for writing a data strategy

    30 Jun 2020 by Deborah YatesOpen Data Institute

    Embedding a culture of data is one of the biggest challenges faced by data management professionals in the UK according to a survey of DAMA UK members. Changing cultures and behaviour takes time, and putting in place a good data strategy, aligned with the business strategy, goes a long way to help. 

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    Tightrope

    Writing a data strategy can be like walking a tightrope, requiring a fine balance of data management know-how and business acumen. It needs to be clear and concise, avoiding technical language and focussing on business outcomes. So what should be in it?

    Here’s a five-point guide on what to include:

    1. Relevance: Set out the role of data within your organisation and how it intertwines with strategic objectives and business aspirations. Canvassing widely for input to get this right is key. Speaking with each department and working together to identify the part data plays in achieving current and future goals can help create a sense of ownership of the strategy by the whole business. Don’t stop there, though - your customers need a voice and not just about the data you hold about them. Engaging user groups to understand what they want to see in the way data is used or made available and being open about your plans can foster trust and keep customers loyal.

    2. Ambition: Where do you want to get to and what will this look like? Setting the ambition will be informed by your engagement with colleagues and external users. Ask questions like:

    a) How widely do you want data to be available, by when and to whom?

    b) Do staff have the relevant data knowledge and skills? (DataIQ research has found analysing customer data is the biggest marketing challenge for 29%, for example.)

    c) What about the way data is managed – do you want to make improvements here, for example to roles, responsibilities and processes or technical infrastructure? (Legacy systems are the biggest data management challenge for 54%, according to DataIQ research.)

    Setting out clear commitments and milestones for achieving these will help everyone to know the part they need to play. The data office, if one exists, has an important role here – as the data management professionals, they are well placed to advise on best practice.

    3.Scope: It may seem obvious, after all, data is data, right? But clearly stating which type of data the strategy applies to will support implementation later on. For example, does it apply to all data held within your organisation or just certain subsets, eg, personal data, customer data, operational data? Taking an holistic approach will ensure the ambition, processes and behaviours are aligned across the organisation and you’re more likely to be successful in embedding a data culture. Note that this doesn’t mean you need to implement across all departments at the same time - you can phase it!

    4. Principles: Setting out the principles to which you will work will be your guiding light during implementation. There are a wealth of principles for data out there to draw from - data protection principles, open data principles and FAIR data principles are just a few examples. The key is to get them right for your organisation in terms of ambition and language.

    5. Measuring success: So, you’ve set out where you want to get to, what data you will be using and how you will work to get there. But how will you know when you’ve succeeded? Including measures of success is fundamental for any objective-setting exercise. Be outcome-focused and include how frequently you will review the strategy and track progress against it. Benchmarking performance prior to implementation can really help to demonstrate the progress of the organisation or of individual departments.

    However you create your strategy, and whatever you include, be sure to write it in your specific context with input from a wide range of colleagues, customers and potential customers. Only by doing this will you be able to walk that tightrope and navigate the high wire of data improvement.

    Deborah Yates is a senior consultant at the Open Data Institute and committee member of the Data Management Association (DAMA) UK.

  • 26 Jun 2020 11:15 AM | Sue Russell (Administrator)

    Data Quality and Data Governance Frameworks

    June 26, 2020

    What are they and do I need both?

    "How do a data quality and data governance framework relate to each other?”I get asked this question quite frequently and I think it’s a really interesting one, so I’d really like to help you get to the bottom of it. I think the reason it comes up is because people have been doing data quality and worrying about data quality for many more years than they have data governance.And so, they feel very strongly that there are two different frameworks in action. Another common misconception is that the two are the same. This may come from a lack of understanding of what data governance really is, so let’s break it down…..

    Is data governance the same as data quality?

    The very short answer is no. Data quality is the degree to which data is accurate, complete, timely, and consistent with your business’s requirements. Data governance, in very basic terms, is a framework to proactively manage your data in order to help your organisation achieve its goals and business objectives by improving the quality of your data. 

    Data governance helps protect your business, but also helps streamline your business's efficiency. It ensures that trusted information is used for critical business processes, decision making, and accounting. And so, if you think about it, data governance vastly provides a fabulous foundation for many data management disciplines, its primary purpose is to manage and improve your data quality.

    To put it in much simpler terms, if data was water then…

    -          Data Quality would ensure the water was clean and prevent contamination

    -          Data Governance would make sure the right people had the right tools to maintain the plumbing.

    So, why would you want two frameworks relating to data quality?

    The simple answer is you wouldn’t. This really isn't a question about how you align two frameworks. You should only have one framework and data quality and data governance should be working in harmony with one another – not against or in opposition.

    Data governance and data quality rely very much on each other, I usually describe the relationship between them as symbiotic, as their relationship is based on mutual interdependence. Therefore, of course, you need both! You would not want to do one without the other if you want to successfully manage and improve the quality of your data in a sustainable manner.

    Sadly, in my experience, some organisations do not yet fully understand that you do need to do both. Whilst you rarely (if ever) come across a company that is implementing a data governance framework without the intention to improve data quality, it is fairly common for organisations to commence data quality initiatives without implementing a data governance framework to support them. Unfortunately, this leaves many data quality initiatives as merely tactical solutions that only have short-term results.

    And, it doesn’t matter whether you call it data quality or data governance (because let's face it, some people really react badly to the term data governance) as long as it gets your business users engaged and understanding what that framework is about.

    So, let's just have one data quality framework which encompasses the roles and responsibilities around data, and then there is nothing to go wrong, no duplication, no gaps between two different frameworks. Make this simple and make it sustainable.

    You can see the video I originally did on this topic here and if you've got any questions you’d like me to address in future videos or blogs, please just email them in to questions@nicolaaskham.com.

  • 10 Jun 2020 2:30 PM | Sue Russell (Administrator)
    Mutual Friends: Aligning business strategy and data strategy

    10 Jun 2020 by Nigel TurnerGlobal Data Strategy

    Charles Dickens published his novel, “Our Mutual Friend,” in 1864. It’s safe to say he was probably not thinking of data strategy and its relationship to business strategy when he wrote it. But it is a simple fact that in our digital, data-driven world, business and data strategies can only succeed if they are closely interlocked and nurtured as mutual friends. 

    Business Fist Bump

    Before the rise of the data-driven business, the relationship between business and data strategies was linear. The business would set out its strategic goals and aspirations. Once these were determined, a data strategy could then be built to plan the data capabilities needed to underpin the business strategy and plan. This also implied that IT departments were often the primary drivers of a data strategy.

    The idea that data is a subservient enabler is outmoded.

    Today, things have changed. The idea that data is a subservient enabler of the business, useful only to support business operations and processes, is becoming increasingly outmoded. On the contrary, in a growing number of organisations data is becoming the business.


    This has radical implications for the relationship between business and data strategies. In this new paradigm, the development of business and data strategies has to be done in parallel and interdependencies between them locked in. Any data-driven organisation (and what organisation isn’t these days?) which fails to recognise this mutuality is doomed to fail. This also means that any aspiring data-driven, digital organisation must create and implement a data strategy, something surprisingly many have still failed to do.

    For example, take a manufacturing business producing a range of consumer goods. Traditionally it focused on selling its products to wholesalers such as supermarkets and other third-party channels. As such, its knowledge of its end buyers was at best sketchy and for the most part non-existent.

    But it decides to create new digital channels to sell its products direct to its end customers, surmising that cutting out the wholesalers will increase its profit margins and enable it to gain a better understanding of and build relationships with its end customers. None of this is possible unless this new business strategy is developed alongside the data strategy needed to deliver it. Key questions would include:

    • What new data would the organisation need to generate and capture to support the new business processes that need to be developed?
    • What data platforms would need to be created to store the data?
    • How will sales be made - through direct online channels, social media platforms and so on?

    Business strategy without data foundations is a folly.

    The point here is that setting the aspiration in stone in the business strategy without being first being sure the data foundation exists to realise it is folly. So data must become a pre-eminent consideration when developing the business rationale and case for direct sales. Moreover, it’s also possible that, as the required direct selling data strategy is developed, it can help to suggest other opportunities that the business had not considered, for example, how analysing data on online consumer purchases can help to highlight purchasing trends and so help generate new product propositions.

    So, if you are tasked with developing a data strategy, how do you ensure that this close mutuality and interdependency happens? Here are some suggestions:

    • Let the CDO, not the CTO define it: First and foremost, a data strategy should not be owned and developed by the IT department. IT might legitimately lead the technology strategy which will be needed to deliver the data capabilities required, but a data strategy must be owned by the business, working in close collaboration with IT. A chief data officer (CDO) is the logical lead if one exists in your organisation.
    • Understand the business drivers: As a data management specialist, ensure you understand your organisation and its business goals, strategies and aspirations. The current formal business strategy is, of course, the ideal starting place, but you can supplement this with annual reports, external and internal websites, social media feedback and so on.
    • Engage with stakeholders: When developing the data strategy, engage with a wide spectrum of business and IT stakeholders. These should range from senior executives through to people who actually run the day-to-day business. They will all have a different perspective on data problems and opportunities, so you gain a much richer and holistic picture of the current data landscape and the drivers for change.
    • Speak the language of business: After drafting the data strategy, expect to create several initial iterations after presenting it back to the stakeholders. Use business language and not data management jargon to ensure it’s readily understandable to all. In particular, wherever possible, try to mirror the language of the business strategy to help people make the direct connection between them.
    • Be agile: Finally, a data strategy is not set in stone. It needs to change and evolve as business goals and strategy change, so ensure there is a process in place to maintain alignment with the business strategy, review it at regular intervals with key stakeholders, and update it accordingly.

    More organisations are recognising the need for a dynamic and flexible data strategy as a keystone to help them achieve their business goals. In a poll of Data Management Association UK (DAMA UK) members in May 2020, 69% of respondents stated that developing a data strategy was one of their two top data management priorities (with the other being data governance at 77%).

    We are living in hard times, but only by making business strategy and data strategy mutual friends can it hope to meet the great expectations placed on them by so many organisations. Charles Dickens would have understood that.

    Nigel Turner is principal information management consultant at Global Data Strategy and a committee member of DAMA UK.

  • 29 May 2020 11:43 AM | Sue Russell (Administrator)

    Earlier this year, in the days before social distancing, I was lucky enough to catch up Neil over breakfast and he kindly agreed to be interviewed for my blog. Neil is an independent data evangelist who has worked in large multinational companies from the early years of the data adoption. He passionately believes that everyone is a data citizen or as he says a ‘Citizen Steward’.

    He views Data Governance like safety, it stems from individual behaviour, and how we shape that to form the day to day activities embraced as a culture.

    How long have you been working in Data Governance?

    In 1991 I began my data career before Data Governance was even talked about.

    Somewhere around 2008 Data Governance started to become more of a mainstream activity with the software vendors adding the word Governance to their sales pitches.

    I have always represented the business side of data, advocating that business stakeholders must be leading and supporting data initiatives. If I take the simplest aim of Data Governance to apply a consistent lens or approach to the use of data, then the business must take the lead.

    Some people view Data Governance as an unusual career choice, would you mind sharing how you got into this area of work?

    I didn’t deliberately set out on the data path as a career, I kind of fell into it. I was working in Finance and realized I was spending all my time as a spreadsheet jockey, which made me question my value. I was fortunate to be asked onto a major finance transformation programme as the reporting lead where data through the migration was critical to my success. Many years of data migrations through a progressive roll-out, and a very good mentor, convinced me there was a fledgeling career in data.

    It wasn’t until 2005 that my Data Management role was formalised as the global MDM manager on an SAP finance transformation programme. Across the world, Master Data Management was an emerging discipline and I was lucky to be able to network with like-minded early adopters. 

    It was an exciting time to be part of something new.

    Back then 90% of the focus was on the technology and IT was wrestling with adoption across their businesses. It was a hard sell to land data as a discipline, and that remains true today.

    But at the heart of any data initiative is the need to articulate what it is you are trying to manage and how you measure whether it is working or not.  Data Governance wasn’t seen as an enabler to the business processes, more a compliance and control regime which business areas could choose to adopt. To overcome these hurdles Data Governance needed a business lens applied with a focus on behavioural change.

    So, in 2006 I started to develop the processes that would help the business to adopt and embrace Data Management. We now refer to this as a culture change, but it is a ‘hearts and minds’ challenge.

    What characteristics do you have that make you successful at Data Governance and why?

    Passion, integrity, honesty, resilience, patience, adaptability, storytelling, simplicity, and being able to talk to various stakeholders in the language that they feel most comfortable. I call it ‘talk business’.

    Once you come to the realization that this is about changing people’s perceptions about data, how they contribute to its management and how they would benefit from making those changes, your approach becomes far more tactile. I cannot understate the importance of developing soft skills. And like any relationship, you must adapt your style to different personalities. For example, at C Level, the message needs to grab their interest in the first 30 seconds which means presenting a concept, with language that supports that message.

    I always put myself in the position of the recipient and try to anticipate what I would want to know and ask, how they think, their pet subject, things that would influence a positive discussion.

    I use an ice breaker unrelated to the data narrative because Data Governance will challenge their beliefs and I am trying to develop a relationship that creates trust and ultimately influence.

    At the end of the day, each of us develops our own styles through trial and error. Go with those that you feel most comfortable.

    Never underestimate the power of WIIFM, what’s in it for me. Understanding those personal drivers of your stakeholders and how they would benefit from Data Governance will be fundamental to your success. 

    Are there any particular books or resources that you would recommend as useful support for those starting out in Data Governance?

    I’m not a big reader, but in 2006 Jill Dyché and Evan Levy published, at that time, an inspirational book called Customer Data Integration. This has been the only data book I have ever taken to heart because of its narrative. Jill is a wonderful storyteller where she brings to life the data challenges. If you ever get the opportunity to talk with Jill or listen to her, go out of your way to do so. (https://jilldyche.com/)

    What is the biggest challenge you have ever faced in a Data Governance implementation?

    Getting started. We hear the saying ‘they just do not get it’ and use that as an excuse for not landing our data message. Looking under the covers I normally find that they do get it but data is not high on their list of priorities, or they have been subjected to the technology bias too often, or they cannot see the value in dedicating energy to a vague concept.

    My biggest challenge has been turning that supertanker. Convincing stakeholders who are either disinterested or openly negative to the changes being proposed to establish a company-wide data discipline. Remember changing a culture requires commitment.

    Is there a company or industry you would particularly like to help implement Data Governance for and why?

    Company or industry for me is no different. Data is data and its management are broadly the same process.

    However, I am a little different in that I look to a Chief Financial Officer (CFO) as the ultimate consumer of data, they would benefit directly through a well oiled and efficient data discipline. Harks back to my days as a spreadsheet jockey. Give me better data that I can trust.

    Many people would disagree with my target audience, and to be honest, 5 years ago I would have agreed with them.

    My rationale is that Data Governance starts predominantly through the management of master data. These are the foundations of every business process, customer, supplier, material, people etc. Every process executed in business has either an explicit or implicit financial impact that lands in finance. Much of the master data is touched by a finance process, for example, customer credit, material costing, supplier bank details, payroll.

    Therefore, by inference finance really does have ‘skin in the game’ when it comes to consistent trusted data. Why would you not at least start the data journey in finance? 

    What single piece of advice would you give someone just starting out in Data Governance?

    Keep it simple. The saying ‘think big, start small’ really rings true to Data Governance. You want to take your stakeholders on a journey of discovery and enlightenment, not a slog up Mount Everest. 

    There is no right answer, just many paths with potentially different outcomes. Choose wisely.

    Finally, I wondered if you could share a memorable data governance experience?

    My first day in a company as the Global Data Governance Manager I attended the inaugural Data Governance Executive Forum, only to be told by my boss that he was not able to attend and that I should chair the meeting.

    My first day.

    I didn’t know the people, their subject areas, what had gone in the past, even the format of the meeting. This was to be my introduction into the world’s largest multinational of this industry.

    I was petrified.

    I learnt a great deal about the people, but more importantly about myself. In the room were a group of supporters, a group of antagonists with the remainder ambivalent.

    By the end of the 3-hour meeting we had achieved a consensus on the way forward for Data Governance, the challenges I would have to overcome and most importantly the frequency in which I would sit down with them one on one over a coffee.

    The outcome was the embryo of Data Governance that would ultimately get established and span the entire company.

    On reflection, if I had made a mess of that first-day induction, Data Governance would have been consigned to the ‘failed project’ bin.

  • 15 May 2020 1:33 PM | Sue Russell (Administrator)

    A while ago I wrote a blog about things you should consider when choosing the right software to help facilitate your Data Governance initiative, but once you have selected and purchased the tool do not assume that everything will now “just happen”. 

    One of my clients was worried (and rightly so) that it was at this point of the project that mistakes could be made which would impact the successful implementation of their Data Governance tool.  I thought my advice to her may help others too:

    Technical implementation considerations:

    Firstly you need to understand exactly what support you will get from your chosen vendor so you can plan what additional support you may need for implementation.

    Then make sure that you agree who is going to manage the technical implementation of your tool. Is it going to be an in-house project team or are you going to engage a systems integrator? If the former is the plan, you need to liaise with the vendor to be very clear on what technical skills training they have available. What do they recommend to make sure that your team are suitably skilled before starting the implementation?

     If you're going to use a third party to implement the tool, make sure you do due diligence to ensure that they understand the tool and have significant experience in implementing it. I have worked with organisations where a consultancy has been employed and they stated that they had experience in the tool.  However, it became clear that while the consultancy as a whole may have had the required experience, the consultants working for that particular client did not have any experience and were learning on the job.  This caused unnecessary delays and poor advice on what was and was not possible with the tool.

    I also recommend focussing on one area or functionality of the tool for the initial implementation. Just because the tool has lots of features that doesn’t mean you need to implement everything at once.  Choose the most needed functionality and implement that first, then look to implement other features as needed.  Remember, at this stage, this is about giving your business users a tool to help them do Data Governance, not to confuse them with a complex tool and functionality they haven’t asked for. As your users become more comfortable with both Data Governance and using the tool you can implement more Data Governance requirements and tool functionality.

    Post-implementation considerations:

    It is never a good idea to implement a data governance tool over the whole of your organisation at any one time. So I recommend not seeing the implementation as a one-off project.

    It is better to think of it as a phased process with the initial implementation being a pilot or trial. Once you have completed the pilot it is likely that the users and the Data Governance Team may want some changes.  This is common as you are introducing something new and not replacing an existing tool or process.  This makes it very hard to get your requirements exactly right on the first attempt.  So you may wish to make some tweaks to the setup of the tool before continuing a phased implementation across the whole organisation.

    It could take a very long time to implement the tool fully.  You need to make sure that this is well planned and that you are constantly working out what the next phases are going to cover.

    You also need to consider how you are going to keep the data in the tool up to date. I recommend that you have a regular review of the content, for example, an annual review where Data Owners look at the content for the data owned by them.  They can then either confirm that the definitions are still correct or, if necessary, provide updates to keep the tool up to date and useful for the business users.

    How to roll out a data governance tool to Data Owners and Data Stewards:

    As I mentioned in my previous blog about choosing the right Data Governance tool, it is essential that your Data Owners and Data Stewards (or at least a representative number of them) are involved in the initial implementation project. Often they have not asked for this tool and they do not react well to having the tool forced upon them.  It is vital that they are involved in the design stage, to make sure that it's set up in a way that is going to appeal to them and make them happy to use this new tool.

    Even if your Data Owners and Data Stewards have been involved in the early stages, remember that doesn't mean they won't need additional briefing and training when the tool gets implemented.  I recommend having a section of your overall Data Governance Communications and Training plan dedicated to the implementation of your data governance tool.  This will include things like initial high-level briefings to explain what the tool is and why it will be useful to your organisation.  You will then need some specific focused sessions:

     ·     Sessions with Data Owners to tell them what they're expected to do with the tool and showing them exactly how to do it.

    ·      Sessions for Data Stewards which will be a little longer and more detailed as they will be doing the bulk of data entry and review of data in the tool.

    Both sets of training need to be accompanied by some kind of user guide or aide memoir, to make it very easy for them to quickly check what they need to be doing once the training is over and they are using the tool for real.

    Taking all the above into account may seem like a lot of undue effort when you just want to get on with implementing the tool, but doing so will make a huge difference over whether it is a success or not.

    If you have other tips for a successful Data Governance tool implementation that I haven’t included above please let me know!

    Nicola Askham

  • 7 May 2020 10:10 AM | Mark Humphries (Administrator)

    There is a global consensus; test, track and trace is the way out of lockdown. To enable track and trace in the UK, NHSX will be rolling out an app that is currently undergoing trials[1]. The UK government has opted for a centralised data collection method, whereby phone to phone contacts are logged centrally and users receive a notification that they are at risk and need to self-isolate. An alternative de-centralised model has been proposed by Apple and Google whereby all users receive details of confirmed cases and the phone itself then notifies the user that that they are at risk and should self-isolate[2].

    The effectiveness of track and trace will be highly dependent on take up. The more people who use the app, the more effective it will be in identifying and notifying people at risk so that they can self-isolate, preventing the spread of the virus and keeping the all important reproduction rate, R, below 1 so that the rest of the population can safely go about their business.

    What do you think? Do you intend to use the app as it is? Or does data privacy trump public health for you? Would you use an app that was based on the de-centralised model?


    [1] https://www.bbc.co.uk/news/technology-52532435

    [2] https://www.bbc.co.uk/news/technology-52441428



  • 29 Apr 2020 12:29 PM | Mark Humphries (Administrator)

    I’m a data geek, so I tend to see things through the prism of data. The COVID-19 pandemic is no exception. While some people are glued to the daily theatre of government briefings, I’m looking for reliable sources of information. In particular I’m looking for evidence of how the pandemic is evolving, how long we’re likely to be in lockdown and what is the impact likely to be for me, my family, my colleagues and my clients.

    There is certainly lots of data out there, but how do you filter out the noise, because the data is very noisy at the moment with some very wild claims and data that appears to support such a broad range of positions and theories. The conspiracy theorists are having a field day, but let’s not go down that rabbit hole.

    My favourite source for information at the moment is the FT. In particular the work of John Burn-Murdoch (@jburnmurdoch) and Chris Giles (@ChrisGiles_). John collates data from around the world to produce a series of daily trackers showing high quality visualisations including daily rates of new cases and deaths, which are the two trackers that I check every day. Chris merges the official daily count of COVID-19 hospital deaths from the Department of Health and Social Care with the weekly total death statistics from the ONS, to produce an estimate of the total COVID-19 deaths.

    There are two things that I really like about their work. The first is that the information is presented in a very clear way. John uses logarithmic scales which means that the slope of the line is the most important thing. A straight line represents exponential growth, and while we were in that very scary phase, the straight line showed very clearly how serious the situation was. The same visualisations are also now showing that lockdown measures are working and both deaths and new cases are coming down. I can see all this in less than 30 seconds every morning. Meanwhile Chris’s visualisations show the difference between weekly deaths now and the five year average, with the implication being that the difference is down to COVID-19, which is clearly much worse than seasonal flu.

    The second thing I like is the fact that they both show their working and they are clear about the uncertainties and the assumptions that they make. John has a useful and informative video clip explaining why he uses the logarithmic scale, where his source data comes from, and what the inconsistencies are. He is open about the fact that the data is very noisy, and what he has done to compensate for this. He has settled on a 7 day rolling average, for example, to smooth out some of the noise in the daily reporting. Chris documents the assumptions that he makes about merging two separate data sources, and he is clear about when and why he changes those assumptions. The fact that they show their working in such a transparent manner, and that they patiently respond on Twitter to questions and criticisms allows me to validate their output for myself, to the extent that I trust it. I feel confident that I understand what their work shows and what it can’t.

    The COVID-19 pandemic is topical, and it’s putting some data under the spotlight, but it’s highlighting some unchanging truths, fundamentals if you like. To make sense of data requires rigour, including understanding where data has come from (lineage), how reliable it is (quality) and what it actually means.


  • 24 Apr 2020 6:02 PM | Lisa Allen (Administrator)

    Hi, I’m Suzanne, I’ve been on the Committee for DAMA for over 3 years and I run the Northern meet ups. Normally we’d meet up several times a year. Here at DAMA we’re not letting Covid hold us back! As we can’t have face to face meetings, today we held a virtual Data Management Northern meetup instead.

    The event was really successful. We had 7 people join us. Hot topics included implementing a data catalogue, data quality skills and tools, data strategy, governance tools, deleting records for GDPR as well as updating data policies, processes and procedures due to home working. We shared our success and horror stories of these topics and gave each other some useful tips and points of contact.

    We will schedule more events in May. I’m afraid we must limit invitees to allow everyone to share their hot topics and get advice from each other. But don’t worry we will book in more, so they’ll be ample opportunity to join.

    Not a member of DAMA yet and want to attend our events? Find out how to join here: https://damauk.wildapricot.org/Join-us


  • 24 Apr 2020 1:56 PM | Sue Russell (Administrator)

    April 2020, Nigel Turner, Principal Consultant, EMEA

    Over the last ten years I have been lucky to work with a variety of client organisations on Data Governance engagements.   These organisations have ranged greatly in size, complexity and global reach, spanning many industry verticals including government, manufacturing, services, utilities, charities, pharma-technology, insurance and so on.

    Although every client engagement throws up unique challenges and opportunities, there are always three questions which arise time and again, and are key to creating and establishing a successful and enduring Data Governance initiative.  These questions are:

    • How do I convince senior executives and key stakeholders in my organisation that Data Governance is worth investing time and resource in?
    • What data domains, types, sources etc. should be within the scope of a formal Data Governance programme and how should this be determined?
    • How should responsibility and accountability for these data domains be allocated?

    In my previous January 2020 blog, I explored ways of how Data Architecture can help to answer both the ‘What data?’ and ‘How should responsibility?’ questions.   In this blog I will highlight some key tips about how to sell the value of Data Governance to executives and those in your organisation whom you need to get on board to successfully sail (and sell) your Data Governance ship to the new promised land.

    For those of us who work with data every day, we recognise its critical importance and the need to leverage it and so increase its value to our organisations.   We also know that to achieve this we need to make people personally responsible for data through data ownership and data stewardship, practices that are at the core of Data Governance.  But to the people we are trying to convince to give us the backing to make this happen, it’s often not as obvious.  They invariably see the world through a different lens, one where day to day priorities and short term deadlines usually dominate their daily working lives.  So Data Governance has to compete with these for attention, and at best can often be seen as a nice to have, but maybe next year… or the year after.

    So how can you convince people that Data Governance is something to be invested in today, and not in the future after today’s problems are solved?  Here are a few suggestions that might help you to achieve this if this is a barrier for you in your Governance efforts:

    • Show them how today’s problems are often caused by data issues that Data Governance will start to address and solve. To do this first link your Data Governance initiative directly to the priorities and goals of your organisation.  Many businesses want to grow revenues, reduce costs, improve productivity, digitise their processes and so on.  All these aspirations have one thing in common – they rely on good data to achieve them.  Growing revenues usually relies on better marketing; better marketing relies on better data.  Low productivity is often associated with the need to rework orders, invoices etc. because the originals contained errors; errors are often the inevitable outcome of bad data.  Whenever I have been challenged about the value of Data Governance, I often say that Data Governance is not a choice between doing it or not.   Every organisation that manages data (and who doesn’t today) is doing Data Governance now, but usually doing it inefficiently and badly, waiting until problems arise, fixing them reactively and doing the same when the problem rears its head again, which it inevitably will.   That constitutes Data Governance, but is doing it inefficiently and expensively.  Formal Data Governance does it better through being more proactive by preventing problems before they occur.  So the choice is really between how you do it, not whether you do it.
    • Don’t try to sell Data Governance by wide ranging arguments around exploiting data assets, optimising the value of data etc. This is too generic and so meaningless for most people.  Instead be specific.  Talk to a wide range of business and IT people across your organisation about the data problems they encounter every day.  Collect these and create real use cases, for instance a failure of a marketing campaign caused by an inability to target it at the relevant segments of your customer base, or the level of returned packages at the Despatch department because orders could not be delivered because of incorrect or incomplete addresses.  These stories help to bring Data Governance to life by putting real flesh on the bones of your Governance proposition.  It’s a well established scientific fact that people tend to connect with and remember stories far better than broad, sweeping generalities.
    • Link your organisation’s goals and the data dependencies to these specific stories. Use this to show how badly governed data impacts your company’s ability to achieve its overall aims, and use the stories to highlight the day to day realities of these failures.  Doing this helps to link the overall organisational goals with coal face problems, thereby emphasising both the strategic and tactical benefits of a Governance programme as it works to tackle the failures.
    • Once you’ve gathered this evidence, it’s important to develop a sales pitch for investment (finance, people and time) in Data Governance. I always suggest preparing three specific pitches:
    1. A 2 minute ‘Elevator Pitch’. This should simply state what Data Governance is, why your organisation needs it, how are you going to deliver it, and what the expected benefits will be.  It’s best memorised and replayed whenever you need it.  This is useful when asked what your job is and what you are trying to achieve.  It is also valuable when you are part of a Data Governance core team to ensure all members of the team relay the same basic messages.
    2. A 10 minute ‘Taster Pitch’. Create a PowerPoint deck to expand on the above which can be delivered by you and others when you get an opportunity to talk to a senior manager or if you can get an invite to scheduled team meetings held across the organisation.    Try to use pictures to illustrate your key points – again these are more impactful than text lists and remember to include some of the stories you’ve collected.  Use the ones most relevant to the audience of any particular pitch.
    3. A 30 minute ‘Full Pitch’. Expand on the Taster Pitch above to provide a more in depth overview of your Governance plan.  This can be used to brief key potential stakeholders and to convince people to become data owners and data stewards.
    • One final tip, as this is often forgotten when we try to sell the value of Data Governance. When preparing the pitches and talking to others avoid at all costs using jargon and language we often use as data management specialists.  People won’t be inspired to act and support you if you talk in a way that makes it all sound very complicated and difficult.  Using the technical language of data management when talking with non-specialists is a total turn off.  When selling you are trying to connect with people both at an emotional and logical level.  If you want people to be enthusiastic and active participants in your Governance journey, use simple business language that all can understand and relate to.  The great business leaders and orators always use simple language to inspire and motivate others.  You need to do the same.

    Keeping it simple can be hard for many people who end up in a Data Governance role as they often come into it from technical data management backgrounds such as database administration, BI analysis, data quality, metadata management and so on.  Having to actively sell new ways of working to a business and persuading others to act does not always come naturally to those of us with these backgrounds.  So it’s well worth investing some of your time in learning how sales people operate.  Like most things in life, Data Governance won’t sell itself. To paraphrase Dale Carnegie’s classic 1936 book title, you need to win friends and influence people.

    If you have further questions about enabling a data governance initiative in your organisation, you can reach Nigel at nigel.turner@globaldatastrategy.com .

    Nigel will next be running his two day onsite course ‘Data Governance: A Practical Guide’ in London, UK on 19 & 20 November 2020.  For further details see: https://irmuk.co.uk/events/data-governance-practical-guide/ 

    In addition, Nigel is also planning to hold a streamed online version of the course on 18 & 19 June 2020.  This is also being presented in partnership with IRM Training.  Please check https://irmuk.co.uk/#public-courses where further information will be posted in the near future.

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