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  • 2 Sep 2020 9:07 AM | Sue Russell (Administrator)
    Can there be more than one Data Owner per Data Set? – Nicola Askham – click here
  • 28 Aug 2020 7:33 PM | Sue Russell (Administrator)

    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 

  • 3 Aug 2020 7:05 PM | Sue Russell (Administrator)

    Data Governance on a ‘shoestring’ budget - yes, it’s possible

    Demand for data governance is increasing as the result of Coronavirus, and at a time where resources are scarce. There’s a huge focus on data because people want to be sure what they have is of good enough quality to make decisions about the future of their business and its survival.

    However, at the same time demand’s going up, budgets are being cut. People are not spending money on data governance but, they want more of it! So, we've got this conundrum. If we're going to deliver data governance, it has to deliver some benefits. There's no point doing it for just the fun of it but how are you going to do that if you've got little or no budget? We need to deliver data governance of a shoestring budget.

    So, the question I asked myself is what can we really do that's useful on that basis? Well, after 17 years in data governance I've learned that, practically, you can’t do data governance over everything and that it's also not useful to as all data is not of the same value to your organisation. We always need to consider carefully where we put our Data Governance focus on and now, more so than ever we need to be pragmatic - that’s where ‘minimal data governance’ comes in.

    But what does this really mean in practice?

    Well, it’s not the bare minimum to keep your regulator happy. It is not ‘just enough’ so you can say you are doing it. Minimal data governance has to deliver real value. If it doesn't, there's absolutely no point in doing it.  

    But just because it's minimal it doesn't mean it's going to take less time. Data Governance takes a long time and I'm afraid the bad news is that minimal data governance also takes a long time.

    Apart from anything else, you won’t get any value from it by trying to do it quickly because you won't do it properly. And therefore, you won't get the value.

    So, can take minimal data governance be effective? 

    Yes, I think it can, because I think it's probably the way I've been increasingly approaching Data Governance over recent years.  What I'm encouraging you to do is to be even more pragmatic and focused than I usually am, but I think if you do that, you should be able to deliver something on an inadequate budget that can deliver some real value to your organisation.

    And, the secret to ‘minimal data governance’ is to identify one priority benefit because if you get Data Governance in place to deliver that correctly, some of the other benefits will start coming through anyway, and you'll be in a good position to then focus on delivering more of them. Benefits can include:

    • Improved efficiency/reduced costs
    • Accurate reporting
    • Facilitating compliance with regulation.
    • Protects your reputation with customers and suppliers.
    • Supporting your corporate strategy
    • Supporting innovation ie AI

    Once you’ve focussed on what you want to get out of your minimal approach, you will need to define your scope - are we dealing with customer data, finance data or a subset of one of these categories? Really take this opportunity to identify a very limited scope. I think the best way of thinking about it is of doing data governance incrementally. What we're doing is our first phase is just going to be very tightly defined. Then when we deliver that, we'll be in a good place to roll it out further.

    So, to be truly effective, you need to really bear three things in mind:

    • Be very focused on your scope - you know you should never be doing Data Governance over everything, but right now, let's have a really narrow scope and focus on just know one thing.
    • Do it properly - minimal Data Governance doesn't mean ‘let's just do it quick and dirty’. Do it properly - just with a very limited scope.
    • Do it in a way that is planning for the future - do it to deliver some very focused benefits now, but in a way that that framework can be evolved and implemented across the organisation in the future. Make sure it's going to deliver some benefits now, but you that can scale it - because you don't want to have to revisit this and do this again.

    Nicola Askham - Data Governance Coach - https://www.nicolaaskham.com/



  • 3 Aug 2020 4:32 PM | Sue Russell (Administrator)

    https://www.nicolaaskham.com/blog/2020/7/1/cyberdata-security-and-data-governance-siblings-from-the-same-parents

  • 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.

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