How Does a Mastermind Work?

You might have heard the words ‘mastermind’ being thrown around a lot (especially by me!) and thought, what’s that?

In the realm of personal and professional development, there exists a potent yet often underestimated tool: the mastermind.

Originating from the mind of Napoleon Hill in his seminal work "Think and Grow Rich," masterminds have since become a cornerstone of success for countless individuals across various fields.

But what exactly is a mastermind, and how does it work its magic? Let's delve into the mechanics and benefits.

What’s a mastermind?

At its core, a mastermind is a gathering of individuals who come together regularly to support each other's growth and success. These groups typically consist of like-minded individuals with diverse skills, experiences, and perspectives. The premise is simple yet invaluable: by pooling their collective knowledge, resources, and energy, members of the group can achieve far more together than they could on their own.

What are the mechanics of a mastermind?

• Clear Objectives: A successful mastermind begins with a clear set of objectives or goals. Whether it's personal development, career advancement, or business growth, members should align on what they aim to achieve collectively.

• Open Dialogue: Central to the effectiveness of a mastermind is open and honest communication. Members are encouraged to share their successes, failures, aspirations, and concerns without fear of judgment. This environment of trust fosters deep connections and facilitates meaningful collaboration.

• Collective Brainstorming: One of the most powerful aspects of a mastermind is the collective brainstorming sessions. When faced with a challenge or opportunity, members can tap into the diverse perspectives of the group to generate innovative solutions and strategies.

• Accountability: Accountability is key to driving progress within a mastermind. Members hold each other to high standards and provide support and encouragement to stay on track toward their goals. This mutual accountability fosters a sense of responsibility and commitment among the members.

What are the benefits of joining a mastermind?

1. Access to Diverse Perspectives: In a mastermind, members benefit from the collective wisdom and experience of their peers. This diverse range of perspectives can spark new ideas, challenge assumptions, and broaden horizons.

2. Support and Encouragement: Building success in any field can be a lonely journey, but it doesn't have to be. In a mastermind, members find a supportive community of like-minded individuals who cheer each other on, celebrate victories, and provide solace during setbacks.

3. Personal Growth: Through reflection, feedback, and accountability, members of a mastermind experience profound personal growth. Whether it's overcoming limiting beliefs, honing leadership skills, or expanding one's comfort zone, the journey of self-improvement is accelerated within the nurturing environment of a mastermind.

4. Networking Opportunities: Masterminds often serve as grounds for networking and collaboration. Members have the chance to forge meaningful connections, explore synergies, and even form partnerships that can propel their careers or businesses to new heights.

A study by the American Society of Training and Development found that people have a 65% chance of achieving their goals when they commit to another person. This number increases to 95% when there’s regular communication with an accountability partner to discuss progress being made. For this reason, mastermind groups are a great way of ensuring you reach the goals you set. (as cited in Leaders).

How to join a mastermind?

Navigating the realm of Data Governance often feels like being stranded on a desert island, isolated amidst the vast sea of data challenges. That's precisely why I run my very own 1 Day Data Governance Mastermind. 

Throughout the day, we foster a dynamic environment of networking and mutual support, where every participant has the opportunity to both seek and offer guidance. Each individual takes centre stage in the "hot seat," sharing their unique challenges and experiences while receiving valuable feedback and solutions from the collective wisdom of the group.

We also always kick off the day with a guest speaker who delivers a talk on an interesting and relevant topic and a live Q&A. 

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I only allow a small number of attendees for my 1 Day Data Governance Mastermind and it typically fills up fast so I do have a waitlist approach. Please sign up to the waitlist and then you will be the first to know when bookings are open.

You can also use my FREE scorecard to find out whether joining a mastermind is right for you.

Data Governance Interview with Kohinoor Mukherjee

Kohinoor Mukherjee started his career as a mainframe developer before moving onto business analysis, project and then program management. Kohinoor has been working on various data specific projects, mainly with financial institutions, for the last decade, and is now presently a Data Governance Consultant with an energy company.

Apart from data, he is also an avid reader with a keen interest in financial risk management, behavioural science, history and public speaking.

How long have you been working in Data Governance?

I have been working solely in Data Governance initiatives for the last 3 years. Prior to that, I was primarily involved in Data Quality and Reference & Master Data Management. However, with every passing day I am intrigued and fascinated by the interdependence of these disciplines and how they enrich each other.

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

Probably 5-7 years back it was unusual but not anymore. With the tremendous growth of different Data Management disciplines, Data Governance is becoming increasingly relevant. I think it’s a key factor, which integrates the different data management streams, resulting in a synergy for the organization. 

My entry into Data Governance was to some extent situation driven and not by choice. While working on a data quality initiative with a bank for a regulatory program, we found that our DQ deliverables were lacking context and failed to rollup to the overall objective of the program; and the missing link was inadequate Data Governance. That’s when I got involved with this topic and eventually found it to be quite interesting.

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

a.    Resilience is a must have quality for anyone in this field, as that will be frequently tested during any Data Governance rollout.

b.    Ability to see the bigger picture and finding the intersections with other data management areas helps immensely.

c.     Having good articulation and negotiation skills are also highly desirable traits to get things moving and create an impact.

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

I regularly subscribe to the DAMA and DCAM contents (papers, webinars etc.) to remain updated. LinkedIn forums on similar topics are also enriching. Anyone new in Data Governance can consider having formal training. I took the 2-day training course offered by Nicola and found that quite good.

I found that knowledge on change management techniques and tools come quite handy in Data Governance implementation. So, it would be good to have some exposure in that area.

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

Challenges are different depending on the industry and their maturity in data disciplines. I will talk about two of them: In one assignment with a financial institution, I found weak data leadership coupled with organizational politics that rendered major data initiatives less effective. In another organization, which was new to Data Governance, getting senior management buy-in and convincing them about the business value of data governance was a big challenge. Without the blessings and active support from senior management, implementing data governance is almost impossible.

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

I would love to do Data Governance for organizations where data is not a byproduct of its business processes but the product itself. For example, the financial data vendor companies. And the reason being that probably I don’t have to spend days and months convincing the decisions makers and budget approvers, the importance and value of data governance before getting into the real work.

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

It is of course important to have a sound framework and operating model but don’t wait for them to be perfect before you start. Start implementation sooner and they will evolve and get better. Most of the time we can’t envisage the practical problems lying ahead. So, let them come and make the necessary changes to your op model and framework etc.

Finally, I wondered if you could share a memorable data governance experience (either humorous or challenging)?

I am still looking for that humorous side of Data Governance. Every discussion and meeting on this topic get so serious at times that it won’t be a bad idea to introduce a ‘Joke of this quarter’ slide in the SteerCos. Jokes apart, I don’t have any one memorable event to share. Rather, almost every interaction with so many different stakeholders have been enriching in different ways and has helped me to develop both personally and professionally.

Data Governance on a ‘shoestring’ budget

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 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 i.e. 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 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.

If you want some more detailed actions on what to do when you are starting Data Governance please download this free high-level checklist.

Data Governance Interview with James Shaw

James Shaw is a Data Governance, Protection and Management professional with experience in managing data risks and assisting data transformations. He is the Data Risk Lead at esure Group.

How long have you been working in Data Governance?

I started work in Data Governance specially about 4/5 years ago, but have broader Data Management experience prior in Data Analytics and MI. Drawing on my Data Governance experience, more recently I have moved into Data Risk Management which concerns second line oversight of key risks to Data including those relating to Data Governance, Protection and Management.

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

Like a lot of fellow Data Governance, Management or Protection professionals, I started in Data analysis and reporting where I learned how to interpret and utilise data, as well as the need to manage data effectively. Through Data Analyst roles I developed a keen interest in data risk and data management specifically, and also searched for a role that was more people focused. I believe Data Governance/Management/Protection is for people who love Data, but also love people and cultural/structural change. Thankfully I was lucky enough to start my Data Governance career under Nicola Askham herself and consider myself fortunate to have had that introduction in an industry that is very young in experience.

 That Data Governance is considered ‘unusual’ is a common (and somewhat justified) view I agree, but I don’t think Data Governance as a corporate function should be as niche or unusual as it’s widely perceived. The governance and management of Data is not just desirable but essential to maintain control and discipline over any organisation just as we do with People or Systems.

If that is perceived as unusual then it is perhaps a sign that Data Governance has sometimes been boxed in as a regulatory specialism (particularly in Banking, Insurance and other highly regulated industries), or narrowly defined as data quality deficiency meditation.

 I would like to think that actually logically, the overall governance and management of Data should eventually be given as established a platform as the governance of IT assets or similar. The emergence of the Chief Data Officer and similar functions suggests that Data Governance is establishing itself in organisational Leadership and strategy plans.

How do you see Data Governance evolving over the next 5 years?

As mentioned above, I envisage the management of Data will become a high level function in it’s own right and no longer an oddity or ‘in trend’! If Data Governance/Management/Protection professionals can frame themselves in the right way, that is as persons in some capacity responsible for ensuring the integrity, availability, security, protection, ownership and understanding of organisational data, then they will be perfectly placed to deal with emerging and residual data risk, as well as supporting data transformations that seems to commonly underpin corporate strategy. If Data Governance is able to embrace this challenge, and not shrink to tick boxing or specifics in complex data regulations, then there will be an accelerating demand for professionals who can demonstrate this experience. Every year we see an increase in Data Governance/Management/Protection professionals and this of course solidifies the position of the function in its own right.

 I also expect Data Governance to develop beyond its focus on Data Quality and apply more equal weight to all aspects of data management, which will also blur the lines between the different factions of data responsibilities. For example, I imagine Data Governance working more closely with Information/Cyber Security, Data Engineering, Data Privacy professionals etc. to deliver common objectives. Which is not a bad thing! Finally, as Data Governance branches out, I would hope to see the expansion of principles embedded in Solvency II and similar quality regulations to other and all types of data, as I believe the principle that data should be accurate, complete and appropriate to be fairly universal. As too are the principles of Data Protection, in terms of understanding, protecting and using data for the right reasons, I don’t think these principles should only apply to personal data. The regulations can, potentially, establish universal principles and ethics as intended.

What are the biggest risks and threats to organisational Data right now?

Other than large scale cyber breaches and hacking incidents, which is not the domain of Data Governance, the biggest risks perhaps lie in the volume of data that is being accumulated and an organisations’ ability to control and manage it. Data volumes and input is being ramped up and the investment in Data Governance is not always keeping pace. When that data is sensitive, confidential or personal, the risks are multiplied. Challenging the assumption that more data is always better is a huge test for Data Governance because the corporate giants of today’s world and those perceived to be at the top of the pyramid consume data on a vast scale. The important point here though is that they are able to apply the equivalent resources to control and manage their data so it becomes a benefit and not a hinderance. Organisations have to be able to understand, catalog, control, and manage their data effectively otherwise it becomes polluted and uncontainable, aka. the dreaded data swamp. It should be understood that the potential consequence (other than the increased regulatory risk) of plugging in data that is not really needed, is that the data that is genuinely needed can be diluted, contaminated and harder to find. I would advocate smaller volumes of quality data that is understood, useful and manageable rather than drowning your systems in data.

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

I would say that building data related experience is a priority, but as too is developing soft people focused skills. Data Governance is about effecting organisational management and change. So whereas a MI analyst for example may be used to delivering independently, a data governance specialist needs to be effective in driving co-operation and initiatives with the wider business. Sometimes getting other people to do things can be harder than doing it yourself! I would recommend developing yourself as a rounded individual, with a balanced set of skills. Unlike some in the industry, that while useful, I would not recommend focus necessarily on developing technical computer language skills as a priority nor would I think necessary to become an evangelical in people persuasion.

 Two of the most important skills I would empathise are patience and tenacity. You have to be able to keep going until you deliver results and be creative as to achieving that – Data Governance is never going to be easy to achieve and instant results are rare.

How do you manage data ownership on a big data platform?

In this blog we’re going to be looking at how to manage data ownership on a big data platform. Now, this question was asked to me on LinkedIn a while back, and it was a really good question in response to a debate I'd been participating in about my very strong belief that data should only have one data owner.

I feel quite categorically from my many years of experience that you really cannot have more than one Data Owner per data set. It really doesn't work, and I don’t recommend you try it. There’s no exception this rule (believe me, I've been there, done it, still have the scars). Instead, I believe that what you need to do is find one senior person within your organisation who is going to take overall accountability for that particular data, wherever it is within your organisation.

This prompted the person to get in contact and ask: ‘How do you manage this on a big data platform when you're bringing in data from source systems which clearly have a single data owner, but are combining it with data that's owned by somebody else on a big data platform and applying some kind of algorithm to it to create some new data?’

Now that’s a big question. And I can understand if you're setting up a big data platform in your organisation for the first time this can seem fairly daunting from a data ownership point of view. But actually, if you stick to my simplified approach of one data owner for data, it's quite easy to follow forward.

 So, let’s break it down. In this scenario it's quite clear that this second set of data isn't the same data, it's new data. If we took some data perhaps owned by sales and we combined it with some data owned by finance, we can apply some logic or an algorithm to it to create some new data.

 Now, that new data didn't previously exist so that new data can have its own data owner, and the data owner is the person who asked for that data set to be created, because they are the only person who can give you the requirements for how to create that data.

 They’re also the only person who really knows what that data is going to be used for, so they're the only ones in a position to be able to tell you what it means, what it should be used for, and if necessary, what its data quality rules should be.

 That’s why I think as a general rule - it doesn't matter whether it's on a big data platform or any other of your source systems - always consider whether or not the data has changed. If that data is combined with another set of data or more than one set of data to create a new data set, it can have a new data owner.

Now, you might be wondering ‘what about the source data owners?’ but in my opinion, for simplicity, you have to think of this new data set is exactly that - it's new - and you need to find a new data owner or agree a data owner based on who asked for the data to be created, and who's going to be using it.

 Now, if you have maybe two or more interested stakeholders interested in the same data set, you have two options: firstly you can get them together and facilitate a discussion to come up to a conclusion as to which is the most appropriate person to own it and the other will be a key stakeholder.

Another option is to consider splitting that dataset into subsets until you find a way of splitting it so that everybody's happy that they are owning and responsible for the data that they really should be. Doing it any other way, I can guarantee you, is not going to work. It's going to cause you loads of pain and is going to result in people telling you that Data Governance doesn't work or doesn't help them.

So, I really cannot impress upon you enough… Only one data owner per data set and it’s often better to break your data sets down smaller if necessary, so you can achieve that.

If you’d like to know more about how I can help you and your organisation then please book a call using the button below.

Data Governance Interview with Emma McLeod

Emma McLeod is a Global Data Governance Controller at Watson-Marlow Fluid Technology Group (WMFTG). She is fairly new to this role, however, has been with WMFTG for nearly 6 years. Emma began as a Customer Service Representative within the UK Sales team then progressed to her most recent position as UK Sales Support Manager. WMFTG manufactures niche peristaltic pumps and other associated fluid path technologies across 10 brands. There Data Governance initiative is to provide centralised support to sales and supply sites in circa 40 countries.

How long have you been working in Data Governance?

I’m very new to Data Governance as I only started my current role a couple of months ago. Through my previous roles, I have become very familiar with the challenges and frustrations associated with poor quality data. I’m now busy learning all I can, and exploring how I can leverage my skills and experience to become an effective leader in this area of our business. 

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

I’ve had quite a varied career path so far. I started out as a primary school teacher, then progressed in various sales, customer service and management roles. I’ve always been very inquisitive and while my roles have changed I have found the skills I learned, at each stage of my career, have supported my development and helped me quickly adapt to the demands of each new challenge.

The constant between them all has been my passion for working with others to enable success and improvement. Oh, and I also really like organising things… 

When the opportunity to work in Data Governance arose at Watson-Marlow Fluid Technology Group, it was a no-brainer as it was a chance to collaborate with our global network to enhance our capabilities as an organisation.

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

Resilient, approachable and patient.

I must admit, I probably struggle with the latter at times, especially when I’m excited about a new idea or project. However, patience is vital as Data Governance takes time before you really start seeing results.

Being in the early stages of redeveloping our Data Governance initiative means there is a long road ahead, with many obstacles to overcome. It can seem quite daunting at times (and I’d be lying if I said I haven’t had a few moments where I wondered what I’ve got myself into). I find it’s those times when I need to step back and revisit the core purpose of our initiative to regain my perspective. Having confidence in what you’re working towards not only helps you remain strong when challenged by others but also builds trust with those working with you.

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

The first books I picked up were Robert Seiners “Non-Invasive Data Governance” and John Ladleys “Data Governance”, as well as the DMBOK. They are great starting points with lots of interesting insight and guidance. Though I only really felt like I was starting to ‘get it’ when I started exploring the challenges and working out the specific needs. Some things that seemed to make so much sense when written in a book simply would not work in reality. I learned quite quickly not to get too caught up in the theory.

I have just finished reading Shannon Huffman Polson’s “The Grit Factor”. While it’s not a book about Data Governance, it was a really inspiring read that focuses on vital soft skills required within leadership roles. As she explores the importance of resilience and courage to overcome adversity. I found her reflections on purpose and building a network particularly insightful, and very appropriate for anyone working in Data Governance. 

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

I have limited experience in specific Data Governance initiative implementation, however, we did focus on data quality more locally in my previous roles. 

I found the most difficult thing at that time was retaining engagement from all data users, and building the consistent behaviours required for the processes to work. Communication is key, you can create the best process going but if others don’t buy into it then it’s not going to work.

I’m trying to apply this learning to my new role by having a strong communication plan that helps me make Data Governance accessible across the business.

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

Test the foundations before you start sprinting ahead. It’s really easy to get caught up in all the different priorities that fall into “Data Governance”, especially if you’re lucky to have people already keen to start getting things done. 

Start by establishing a solid plan with a clear vision, linked to your organisation's strategy. Then take small, steady steps to achieve some initial success. This may give you some ‘quick wins’ that will help others see the benefits, but is also not going to cause any major disruption should you find you need to rework anything.

You can find out more about Emma and connect with her on LinkedIn by clicking here.

Data Governance Round-Up 2020

Happy New Year! 2020 was a year like no other, so I hope you all managed to get some well-earned rest and are ready and refreshed. After the previous year we’ve all had, I hope we’re all ready for what 2021 might bring, and I also hope, now more than ever, that more people are ready to implement Data Governance in their organisations. In case I haven’t mentioned…I think this is an essential component of any organisational structure.

The New Year is associated with lots of stereotypes and cliches: ‘new year, new me’, ‘out with the old, in with the new’, the list goes on and on, as people set their new year's resolutions hoping for bigger and better... I usually find myself avoiding these and focusing more on reviewing and consuming content that’s going to support me in the upcoming months. And I know I’m not alone in harbouring this January habit, proven by the astonishing number of Data Governance initiatives that start-up or re-launch at the beginning of the year. 

Last year I released a ‘Data Governance 2019 Round Up’ (which you can view here); after last year’s one being so well received and useful to so many,this is a round-up of my most popular 2020 blogs. There might be one you missed, or perhaps, one of these might be more relevant than ever. 

1. How to select the Right Data Governance tool

2. Data Quality and Data Governance Frameworks

3. Cyber/Data Security and Data Governance – Siblings from the same Parents

4. How to successfully implement a data governance tool

5. Communication Perspective

6. Where can I find a standard data governance framework

7. Can there be more than one data owner per data set?

8. How Often Should you Revisit your Data Governance Maturity Assessments

9. Why is it so hard to write a data governance policy?

10. Do I Really Need a Data Governance Policy?

I hope you find this list useful, and if you have any topics you would like me to write about in 2021, please get in touch and let me know! 

I wish you all the best for 2021, let’s hope this year will be a good one! 

If you need a deeper dive into a structured approach to design and implement a Data Governance Framework successfully, don’t forget that I offer both face-to-face and online training! You can find out more about these on my website here: https://www.nicolaaskham.com/data-governance-training/  

If you want to chat about your Data Governance Training requirements, why not book a call by using the button below? 

The tale of Dick Whittington and the missing data

2020 has been an odd year to say the least, and as we approach the festive season it’s hard not to think about all the things we’ll miss out on this Christmas. One of those things being the traditional British pantomime.

 For those of you who have no idea what I’m talking about, a pantomime is a musical stage production which takes traditional fairy tales and retells them with larger-than-life characters, slapstick comedy, jokes, gags and a big baddie we can all boo at. They’re a staple of the British festive season that we won’t be able to enjoy this year.

 But, fear not, because today I have for you our very own pantomime tale - a data pantomime. So, sit back, relax and let me tell you all about our hero Dick Whittington…

 Once upon a time… there was a man called Dick Whittington. He was a humble man. Loyal, dependable and hard-working. Devoted to his wife and to his work. He was employed at the Dense Doughnut Bakery and loved it. One day, his boss, Mr Dense, tasked him with finding a way to improve customer experience within their organisation and this is where our story begins…

Our humble Dick set out on his journey to improve customer experience. Being the eager employee that he was - and desperate to please Mr Dense - Dick went looking for a magical and instant solution. He trawled and trawled the internet but was getting nowhere fast. Distracted, he decided he would start his Christmas shopping, and headed to eBay. There, he discovered a listing for a magic lamp which he thought his wife would love. And, not one to hang around, Dick clicked ‘buy it now’ and selected express shipping.

 When his magic lamp arrived, Dick polished it furiously and to his surprise out popped a Genie, who offered him three wishes.

 “Aha!” exclaimed Dick. “That is the answer to all my problems! I will use these wishes to improve customer experience at Dense Doughnuts and still have the lamp to give my wife for Christmas!”

 So, Dick set about making his first wish and he asked for lots of shiny new technology tools to help him manage his customer experience. Secondly, he wished for an instantaneous data migration from their old systems to put the data into his shiny new systems. And thirdly, he wished for a self-service portal, so that customers could access their own records and manage them themselves.

 These were good wishes, but the old saying is true ‘you get what you ask for’. It was a disaster. The Genie hadn’t thought about the data that was going into these tools. And why would he… he’s a magical Genie, not a data scientist!

 Dick couldn’t launch his online portal because the customer data was so very poor, and they didn't want their customers to know how bad it was - that would’ve had the opposite effect of improving customer experience!  Even worse, half the data they thought they had about their customers didn't appear in the shiny new tools.  So, Dick had to go on a hunt to find the missing data.

 He searched high and low, low and high shouting (and sometimes crying) out: “Data, data - where are you!” “It’s behind you” would be the loud and increasingly angry reply from a mysterious chorus of voices.

 “It’s behind you, it’s behind you… IT’S BEHIND YOU!”

 But every time Dick turned around… it was gone. Poor Dick Whittington was on a wild goose chase.

 Feeling lost and confused Dick cried out: “Oh, woe is me. If only there was one place I could go to see what data we have in this company and where I can find it. If only we had a catalogue of Data. That’s what I should have wished for.”

 After hours and hours of searching, Dick finally found some data that he thought might do the job. But, worried of repeating past mistakes - or using the wrong data and damaging customer experience rather than improving it, Dick wasn’t sure what to do. He shouted: “IS THIS DATA GOOD ENOUGH?!”

 “Oh, yes, it is!” shouted the mysterious chorus. Quickly followed by “Oh no, it isn’t!” This went on for another hour.

 “Oh, yes, it is!”

“Oh no, it isn’t!”

“Oh, yes, it is!”

“Oh no, it isn’t!”

 Poor Dick was more confused than ever. Sherry Trifle, the resident know it all spotted poor confused Dick and came over to see if she could help make sense of his predicament.

 She said: “Well if you're not sure, do you really dare use this data?”

 “But I’ve wasted hours searching and this is all I have to show for it - I have to do something!”

 “What if you find out that it’s really bad quality, or even worse that it's the wrong data and we make the wrong decisions on it?” replied Sherry.

 “If only there was some way I could know if the data was good enough to use” replied Dick.

 Sherry said she knew a guy who could help and introduced Dick to a wonky looking chap with a big basket of dirty data.

 “Hello! I’m Wishy Washy - pleased to meet you! That looks like a lot of data you have there. I could cleanse that for you if you like?” he offered.

 Dick wasn’t really sure if that was the right thing to do but he was running out of options and took Wishy-Washy up on his offer and followed him to the laundry.

 The laundry was full of hustle and bustle. It was noisy with machines spinning round and round cleansing data and making it ‘better’. Dick asked Wishy how they knew what they were doing and how they would figure out which parts of his data were good or bad.

 “I don't know - we just put it in the washing machines, and it comes out clean!” replied Wishy. Dick was worried. And he was right to be. When Dick got his data back it was definitely different. But he was still not convinced that it was right.

 It all got too much, and Dick sunk to the ground feeling hopeless and defeated, wondering why on earth Mr Dense chose him to take on this project to improve customer experience…

 That’s when suddenly POOF! In a big puff of sparkly fog Dick’s Data Fairy Godmother appeared! She explained to Dick that all the things he’d wished for from the Genie were wrong. BUT, all hope was not lost as the things he'd wished for during his journey (one place to document all the data and where it is held, a way of knowing how good, bad or otherwise the data was and finally a sensible way to fix bad data) were the wishes he should have asked for to begin with. And that he didn't need a Data Fairy Godmother or a Genie in a lamp to give him those wishes – he could simply start a Data Governance initiative!

 The END!

 Now you see, pantomimes can be fun to visit once a year but are a long, drawn out nightmare to live in. Don’t be like Dick and don’t make the wrong data wishes! Avoid working in a data pantomime, implement Data Governance and remember Data Governance is not a project - Data Governance is for life, not just for Christmas!

 If you need help getting started then I have a free Data Governance checklist, which will set you on the right path. You can download it here

 Merry Christmas!