Hi all,

It’s been some time since I last published the dataZED of Data Excellence and I’ve taken the decision to bring this newsletter to a close.

There is now so much content freely available - from experts, aspiring experts, and AI generated - that I can create value better in other ways.

I do plan to stay in touch, but that will focus more on what I’m working on; and I also want to spend more time chatting with people directly.

This newsletter will be taken offline on or after 31 December. Feel free to grab what you can before then!

I also have a couple of events lined up for January which I’m looking forward to speaking at.

Best wishes for the season and new year,

Charles

In our Generative AI world, where adoption is rocketing and a legal framework is still taking shape, people are scrambling to identify, assess and manage the risks so that Gen AI can be used effectively and safely.

Generative AI and LLMs are built on data, and ensuring that the data is reliable and trustworthy is a major hurdle towards effective Gen AI use.

Fortunately, many of the concepts that we need to manage this data have already been developed through Data Governance.

Fundamental Aspects of Data Governance in AI

1. Data Community: People remain the most important component of Data Governance. We need to know who is accountable and responsible for data, and who are the subject matter experts.

2. Data Lineage: Where did it come from, and how did it get here? We might not be able to draw a diagram on a bit of paper any more, but it is ever more critical that we can track the flow of data from source to consumption.

3. Data Definitions: What do we mean when we use a particular term. Often we might think that a definition is obvious, but rarely does a whole team define a term the same way.

4. Data Issue Resolution: You’ve found an issue or error. Now what? You need a robust process to assess issues and respond to them accordingly. Additionally, you’ll need to consider any Gen AI outputs which derived from flawed data.

5. Regulatory and Legal Standards: Data governance helps ensure that AI initiatives comply with legal and regulatory requirements, such as GDPR and BCBS 239.

These components lead us to Data Quality. High-quality, trusted data is essential for training accurate and reliable AI models.

However, we are working with larger volumes of data than ever before, and joining them together in ways which were previously inconceivable, so we need to grow our data governance toolkit for the specific demands of Gen AI.

Additional Aspects for Data Governance in AI

1. Transparency and Accountability: Transparency in AI processes, making it clear how decisions are made and who is responsible for those decisions. Who developed the AI routine, who operated it, and what machine executed it?

2. Model Cataloguing: Different LLMs exist for different purposes, and each one will have new versions created over time. We’ll need to understand what LLM was used to create a particular output.

3. Ethical Considerations: Whether they come from laws, regulations or other sources of ethical guidelines, we’ll need to ensure model outputs align with principles of fairness and non-discrimination.

4. Availability: Few business use cases require real time, 24/7 data. Stakeholder expectations of Gen AI puts additional pressure on not just the availability of data, but its timeliness.

Are you ready?

There is much to do, and few of us are anywhere near where we need to be. It’s a journey, and the most important thing is that we are continually improving.

dataZED is available to help you on that journey. I really want to see people succeed in data, and it would be my privilege to be able to help you do so.

Have a wonderful week,
Charles

Data professionals will have often experienced that sinking feeling when their analysis and insights are dismissed in favour of "gut feel".

But you shouldn't dismiss "gut feel". Maybe it's the clue you need to improve your data collection and analysis.

---

Jeff Bezos wanted to prove a point to his team when he called customer service during a meeting and wasn’t bothered about how awkward the stunt was.

Speaking on the Lex Fridman Podcast back in December 2023, he recalled a stunt he pulled to get his point across to his staff, back when Amazon was in its earlier days.

He explained that the data they had was showing that customers were only waiting 1 minute before connecting to someone from customer service if they called.

However, he had anecdotal evidence from complaints that this simply wasn’t the case.

During a meeting when one staff member tried to convince him that everything was all fine and dandy, he wasn’t having it and decided to prove otherwise.

Speaking on the podcast he said: “'I have a saying which is: when the data and the anecdotes disagree, the anecdotes are usually right.

“It doesn’t mean you just slavishly follow the anecdotes then, it means you go examine the data.

“'It's usually not that the data is being miscollected, it's usually that you're not measuring the right thing.”

Bezos explained that he then called customer service to see just how long the wait would be.

He laughed as he said he and the team just waited in silence.

They were waiting around for 10 minutes before they got connected.

He added: “It dramatically made my point that something was wrong with the data collection. That set off a whole chain of events where we started measuring it right.

Bezos said the stunt was uncomfortable but was important to get to the truth.

“That is an example of truth telling. That is an uncomfortable thing to do but you have to seek the truth even when it is uncomfortable.”

dataZED is available to help you on your data journey. I really want to see people succeed in data, and it would be my privilege to be able to help you do so.

Have a wonderful week,
Charles

I’m a big fan of Data Governance. It covers the set of foundational activities that enable you to trust your data so you can do cool and exciting (and lucrative) things with it whilst staying out of jail.

Many people in the data community feel the same way, which is why they find it all the more troubling that they can’t gain engagement to fund their data governance activities.

So don’t.

Here are three things to do instead:

Wrap Data Governance up within a broader project

Even the AI enthusiasts are making it clear that AI relies on data, and the data has to be good enough to use. So the first set of activities in your AI enablement is to get your data to a trustworthy level. Which means understanding what you need, where it is, and how good it is.

Align with Risk Management

I did a webinar on this last week and you can access the recording here.

Your risk lead (they may be hiding in Operations, Legal or Compliance) has to manage an array of risks that could impact the organisation. Check in with them that data risks are in their thoughts, and come armed with some examples.

Self-start with an MVP

You don’t need a lot of resource to start with Data Governance, other than your time. Consider if you can build out a basic DG Framework in support of a specific project, dashboard or activity - which you can build up over time. You may find it easier to get investment to grow an activity which is already demonstrably adding value.

I can help you

All the activities described above can be done today, without requiring any significant investment in resources, other than your time and commitment. If you go ahead with some or all of these steps, I’d love to hear about how you got on. 

But I know that you may be in an organisation which has its own specific quirks; or you work with people who can be really challenging to engage. Perhaps you simply have no time to get on with this, due to all your other commitments and deadlines.

For that reason, dataZED is available to help you on that journey – from an initial call to help you clarify your actions; to providing more tailored guidance; all the way to coming into your organisation and delivering it for you – together with coaching and support so you can carry on the good work.

I really want to see people succeed in data, and it would be my privilege to be able to help you do so.

Have a wonderful week,
Charles

As a data leader or aspiring leader, we are measured on success. If we are rolling out a new initiative, project or programme, then it needs to succeed.

If it doesn’t, then that will reflect badly in our next appraisal, job application or client proposal.

So we feel the pressure.

And when it goes wrong (when, not if), we feel the pain.

Federer is one of the all-time greats in his field. He won 54% of this points, but almost 80% of his matches.

So clearly it’s not about how many points he won, but when he won them.

As data people, we want things to be right and when they aren’t, we feel it.

Federer’s story teaches us about the importance of knowing when you need to win, and when you need to move on to the next point.

Takeaways

• Understand the bigger picture of what you are trying to achieve. Small failures on the way shouldn’t push you away from your overall goal.

• Encourage this mindset in your team - especially those who are new to the team, are more junior, or who have just been given more responsibility.

• When things do go well, don’t celebrate too soon! If we are going to be calm when things go wrong, we shouldn’t go crazy even when they go right. Celebrate when the project has succeeded.

Free stuff

• I’m doing a webinar on “Taming Your Data Risks” at 12pm London time on Friday 21 June. Register here.

• I’ve added a new booklet on People Engagement to the Downloads Page.

Have a wonderful week,
Charles

Hi there,

I’ve been working on a new guide - “How to get your people engaged in Data Governance” which is now ready for sharing.

And subscribers to this email are the first people to find out.

Not that it’s entirely free. I would really appreciate any feedback and suggestions that you have.

Go grab it from my Downloads Page.

I’m also doing a webinar on “Taming Your Data Risks” at 12pm London time on Friday 21 June. Register here.

Have a wonderful week,
Charles

Ever heard of Frank Williams?

Sir (as he later became) Frank Williams set up the Formula One team “Williams Grand Prix Engineering” in 1977, and by 1997, the team had won the World Constructors’ Championship nine times.

That’s more than McLaren, Mercedes and Red Bull have achieved to this day!

But Sir Frank retired, passing away in 2021, and the team has been in the doldrums for several years.

The current Team Principal, James Vowles took over in February 2023 and recently spoke about what he found:

Not enough data. And not the data he needed.

Does that feel familiar?

It’s an organisation built on Excel. It’s clunky but it’s all you’ve got.

Does that feel familiar?

It’s fixable and it has a direct impact on performance.

Does that feel familiar?

So what lessons can we take from James Vowles?

You can run a cutting edge business with a clunky spreadsheet and a lack of data

Williams haven’t won races consistently for a long time, but they successfully put competitive cars on the grid, at races all around the world. It was more stressful than it had to be, and others outperformed them, but they did fulfil their obligations as a Formula One team.

This is why you won’t get Board level buy-in just because the data’s not much good and the tech is outdated.

They know (and you’ll have to admit) that your organisation functions well. You are actually trying to improve things rather than fix them.

So have that in mind when you make your proposals.

The data doesn’t tell you what you don’t know. You need subject matter experts

When he says it, it’s obvious that you need to know how long a component takes to make, but would you have stated that as required data if I asked you before you read this article?

It works the other way round as well. The data or tech teams may go to a lot of effort to fix a “data problem”, but if it wasn’t something that impacted the running of the organisation, then it was effort that should have been invested elsewhere.

Not only must the subject matter experts guide you on how the business works, but by supporting their efforts, you gain them as advocates and sponsors.

Spend on improving the situation is an investment, not a cost

Sport is different to most businesses, because there is a race (or match or competition) on a regular basis where your performance is measured against your peers.

In other fields, the link to improved performance less obvious as your cost savings and revenue increases will play out over time; and comparisons to your peer companies will never be precise as you don’t have the same objective in the way that sports events do.

Where you do have an advantage is that you can work with qualitative feedback as well as quantitative.

For example, your colleagues may provide feedback or quotes about how it has benefitted them and their team; which is not as visible when successful is measured in points or profits.

Takeaways

• Always remember that organisations can survive reasonably well even when they don’t manage data that well.

• Find and work with the subject matter experts so you can understand where to focus your energy and resources.

• Think about demonstrating the benefits of your activities - this can be qualitative as well as quantitative.

What’s your biggest data quality challenge?  Tell me and I will send you back THREE actions that YOU can take to help.

I have a couple of events coming up:

• For insurance people, I’m doing the next dataZED Get Together on the afternoon of Thursday 2 May, supported by Precisely. The topic is “Better data, better underwriting performance”. Register here.

• I’m also doing an interactive webinar on "Mastering Stakeholder Engagement” on Thursday 9 May from 12-1pm London time. This is for the “Modern Data Community” so you’ll need to contact me for an invitation.

• And if you can’t make those, but want to chat data anyway, then give me a shout.

Have a wonderful week,
Charles

For the full article in The Times on James Vowles and Williams F1, click here. It’s behind a paywall, but non-subscribers can register to read a few articles per month.

P.S. Was there anything in the newsletter that might be useful or interesting to a friend or colleague? Why not share it with them?

P.P.S. Do you have an article or ideas that you want to share? Send me an outline to be considered for a guest posting slot.

If you want better data quality then don't start by improving data quality.

• Cleaned up data feels rewarding and you can show improvements.

• But did you consider whether the cleaned data helps anything?

• Did you consider how to stop the errors from recurring?

• Why was it entered incorrectly in the first place?

Start with these three actions

Discover which data items have the most impact

Talk to a finance person and they'll tell you where faulty data has a significant impact on the year-end accounts.

Talk to a sales person and they'll tell you where faulty data affects their conversations with prospects and customers.

Talk to a distribution person and they’ll tell you where faulty data causes deliveries to fail.

So why guess? Get out there and ask people.

Find the break point

Maybe you are lucky enough to be in an organisation with well-documented Data Lineage. In that case you can track back from the consumer of the data and identify where the error was introduced.

For the rest of us, you need to follow the trail. The end users will be able to tell you which report or dashboard they use, and then it's up to you to do the legwork to find out what data fed that report, and how it got there. Sometimes, the creator of the report has left the firm and didn't leave detailed documentation, but you should still be able to find out where the data comes from.

If that report is the cause of the error, and you don't have documentation, there could be some detailed unpicking to do. It might be easier to rebuild the report itself!

Understand the cause

Occam's razor is a principle that simple explanations are more likely to be correct than complicated ones. If data is entered in a strange way, or goes through a nest of transformations, that complexity might not be necessary. More likely it is the result of multiple processes which have adapted over time.

Don't be afraid to ask why things are done as they are.

Certainly don't be afraid to ask if things can be done differently, but remember to explain who this helps (not just what and why) down the line.

Fixing the root cause of data issues means less rework, more trust, and better decision making.

What’s your biggest data quality challenge?  Tell me and I will send you back THREE actions that YOU can take to help.

Have a wonderful week,
Charles

P.S. Was there anything in the newsletter that might be useful or interesting to a friend or colleague? Why not share it with them?

P.P.S. Do you have an article or ideas that you want to share? Send me an outline to be considered for a guest posting slot.

It’s a new Formula One motor racing season, and data people love to talk about motor sports because it makes work look a bit like play.

But while all the other data people and will talk about squillions of points of real time data, here’s a story about the driver whom the Greatest Of All Time believes to be the Greatest of All Time.

Enjoy.

The 1984 United States Grand Prix. Ayrton Senna, in his debut season, ends his race on lap 47 after hitting a wall.

After the race, Ayrton debriefed with the team’s engineer, Pat Symonds. ‘It’s impossible that I hit the wall. The wall moved’. He was so insistent that Symonds agreed to go and look at the wall.

It had.

The wall was made of large concrete blocks used to delineate the circuit, but someone had previously hit the far end of a block and pushed it, causing the leading edge to come out by a few millimetres.

Senna was driving with such precision that those few millimetres were enough for him to hit the wall that time rather than just miss it.

“That really opened my eyes. I knew the guy was good but that really told me how special. Not just the driving but this conviction, the analysis and then the conclusion: I cannot be wrong, so the wall must have moved.

Everyone else would say, ‘How on earth did I do that?’‘ but the conviction he had was just staggering. And he was right.”

Ayrton was right. But the wall, and Keke Rosberg, won.

On Lap 46, Senna had great data for his knowledge of the track.

On Lap 47, his data quality had got worse, but he didn’t know, so his data analytics were wrong.

On Lap 48, he was off the project.

Don’t forget my current offer.

Reply to this email with your biggest current data challenge. I will send you back THREE actions that YOU can take to help.

Have a wonderful week,
Charles

P.S. If you are heading to Big Data & AI World at Excel London on Wednesday or Thursday, then hit reply to let me know and we can grab a coffee there.

Otherwise we can have a chat in person or online.  For those who like online calendars, you can find mine here.  If you don’t, then let me know when you are available and I’ll sort something out.

Prevailing attitudes towards data management often range from considering it a "necessary evil" to dismissing it as a "waste of time." Many organizations view data management as a regulatory burden—a series of boxes to tick.

So what do we do?

“Try to be light touch”

“Non-invasive data governance”

“Be easy to work with”

Frankly, too many data teams view themselves as a “necessary evil” or “waste of time”.

It's time to challenge this.

Data management is more than legal compliance, or even risk reduction. Data is the catalyst for bottom-line improvement. Why are organisations happy to operate blindfolded?

Manual processes and bespoke built solutions can't be the answer. Excel spreadsheets and DIY SQL code will live on, but only if living in the past is your thing. Remember Kodak?

The marketplace has exploded with new solutions, enhanced by Gen AI - both big brand vendors with new products and startups tackling things in interesting and innovative ways.

Just remember, you don’t get anywhere by buying stuff. You've got to engage your people and make it part of your ways of working.

Data teams must shed their apologetic stance. No more hiding in the shadows and begging for crumbs of budget.

Successful organisations need their data practitioners to assert their expertise and influence, spearheading data-driven initiatives and bringing everyone on board.

Easy to say.

Harder to do.

You need an ally for this.

dataZED is that ally.

Here's my offer.

Reply to this email with your biggest current data challenge. I will send you back THREE actions that YOU can take to help.

Have a wonderful week,
Charles

Whilst you’re down here:

• The next dataZED Get Together has moved online due to anticipated disruption in central London. Register here and join us from wherever you are Thursday 29 February at 1630 GMT.  This month’s roundtable is on Data Quality.

• Download my guide to Kicking off Data Governance 

• Check out my previous newsletters, where you can also subscribe to get them sent straight to your inbox.

Monday's project was to improve performance at a consumer experience company.

In other words…

It was half-term so we took a couple of hours out of the day to go ten-pin bowling.

We played two games, and I noticed during the first game that the total score for all players was shown at the bottom of the screen.

And I liked that idea as it added a team element to a competitive activity.

So for the second game, we were all working to beat the team score from the first game.

The mindset switch to maximising our score did change the way I thought about the game (FYI, I'm not that good at bowling) because I was now thinking about maximising my score, rather than where I was relative to the other players.

The key to maximising the score is through strikes (hitting down all ten pins in one go) and spares (doing so in two goes) as this doubles your subsequent score. This means that going from good (8 or 9 pins) to great (all 10) is much more than a 10% or 20% improvement.

But one occasion I hit a spare, followed by a single pin, so my score was only increased by one - effectively negating the advantage achieved by the spare.

Your organisation can almost certainly deliver a great data project. And it will lead to some benefits.

But can it deliver two in a row? Or more?

And can it do increasingly well over time so you get the multiplier effect?

Or do you deliver the project; close it down; and go back to your old ways?

If you would like to chat about sustainable data management at your organisation , then my diary is open here.

Have a wonderful week,
Charles

Follow ups:

• Learn and network at the next dataZED Get Together 

• Download my guide to Kicking off Data Governance 

• Check out my previous newsletters, where you can also subscribe to get them sent straight to your inbox.

Image by Richard Reid from Pixabay

There are business leaders - the C suite.

There are data leaders - CDO, CDAO, Head of Data, Head of Analytics and the rest.

Where these players are in sync and supporting each other, great things happen.

In every other organisation, value is being lost because the tribe running the organisation; and the data tribe aren’t aligned.

I’ve previously shared guidance for data teams to improve their buy in. Some of it can be found here.

Now let’s look from the leadership side. Two of their key complaints about data teams are their cost and the lack of available resource.

Constant request for spend, with no clear Return on Investment

The data team are always coming up with requests to buy new tooling at significant expense.

Why do we need a Data Quality tool when we already know that our data is lousy!

The marketing people request spend by showing me what the likely return on my investment is. And then I can review afterwards if that was the case.

The sales team can show me that additional people can cover new regions or sectors, and again I can review if that worked out.

The data team can’t do that.

Why?

Because you’re comparing it to the wrong teams.

Every decent firm has a CFO (and team) to make sure that the money is managed correctly.

Do we ask for an RoI on preparing management accounts?

Every decent firm has an HR function to manage the people.

Do we ask for an RoI on HR policies?

Data is one of the most significant assets that a firm has.

The more you look after it, the more value you can generate from it.

We can’t find good people

There are many good recruiters specialising in data, but supply still exceeds demand and it will be a few more years before all the graduates and entry-level data colleagues have reached leadership level.

(And let’s not start on asking for people with more years experience in a technology, than that technology has existed for…)

You need to be creative, and you are rarely going to find a perfect solution.

First up - if you want to start or restart your data initiative, then that requires effective change management and stakeholder engagement, as well as sorting out the technology and the policies, processes and guidance.

That’s a different skill set to running established data management activities over time.

So consider short-term or contract engagement to do the former (yes I’m talking about dataZED) and then the requirements list for your long term (ideally permanent) data manager because much more achievable.

Second - your data people need to be knowledgeable about your industry and your business, as well as about data management.

Three out of three would be amazing. But is hard to find. And expensive.

Can you get someone with two of the three, or even one? The rest can be taught (and in a second plug for dataZED, I have worked with clients to coach some “oners” and “twoers”).

Third - when I started doing this work in the early 2010s. the approach was to start in a manual way, relying on people, with a view to automating or buying in technology as the activity matured.

For example, Data Dictionaries would start off in Excel (or Word if you were unlucky; or SharePoint if you were flash) before aspiring to buy a tool like Collibra.

That’s changed now. People are expensive and tools are relatively cheap. Moreover, the automations and accelerators in the modern data suite mean that starting slowly and manually is really a false economy.

(Is it time for another dataZED plug? We do RFPs as well.)

All in all, finding good people is hard. But there are ways to mitigate that.

TL; DR

If you are C-level leader frustrated by the data team’s constant requests for spend and for more people, then you should be:

• Viewing data spend as for other business operational functions such as that on Finance or HR.

• Thinking creatively about resourcing, such as short term contractor or consulting support; then bringing in people from non-standard backgrounds; and by using technology to reduce the need for people to carry out manual data management work.

And if you would like to have a chat/vent/sob with me about data at your organisation , then my diary is open here.

Have a wonderful week,
Charles

Follow ups:

• Learn and network at the next dataZED Get Together 

• Download my guide to Kicking off Data Governance 

• Check out my previous newsletters, where you can also subscribe to get them sent straight to your inbox.

Is Kevin De Bruyne the most data-smart footballer around?

We've all read the articles about the use of data in football. The analytics to determine transfers. The analysis of play to determine tactics.

This is equivalent of us, in our organisations, using data to do the day job.

Retailers analysing data to sell more; insurers to price better; factories to produce more efficiently - you get the idea.

In 2021, Kevin De Bruyne used it to negotiate his contract with Manchester City FC. For context, he was soon to turn 30, and in anticipation of his contribution beginning to decline, was offered a deal less valuable than his previous one.

By engaging a number of third-party companies, he provided evidence that

• His on-field performance metrics for attacking contribution ranked him as the best in Europe.

• His substantial following of more than 30 million on social media generated value for the club.

• Not only would his departure reduce the team's chances of winning the Champions' League; but his joining a rival team would impact those chances further.

It was a compelling story. De Bruyne received a more valuable, and longer-term contract; and he remains one of their most influential players.

This is the lesson that we need to take if we want to be data-driven organisations instead of just pretending.

We’ve got to take data and use it to make a tangible difference to our decision making; and then take action.

It's not enough to make a dashboard and then carry on as before.

It's not enough to look at the data and then decide to do the same thing anyway.

It's not enough for data people to stay siloed and complain that the business isn't paying them enough attention.

KDB didn't collect the data or analyse it. The experts he engaged made sure that the data was robust before they analysed it, making inferences that enabled De Bruyne to negotiate to what he wanted.

That's why dataZED takes a similar approach.

1. Getting your data to sufficient quality that you can trust it.

2. Engaging with the business so that the right questions are being asked; and the right content is being provided.

3. Enabling data teams to work with and for the rest of the business, avoiding the silo mentality.

Download my guide to Kicking Off Data Governance here or book a free discovery call with me at https://www.datazed.co.uk/get-in-touch 

Have a wonderful week,
Charles

P.S. One lucky person has already won a bottle of wine (chosen from all my email subscribers as of 31 December) but I’ve got another one for January.

This month, I'd like to focus on the dataZED of data events calendar.

We've all seen posts on LinkedIn from events that we would love to have attended, if only we'd know about them earlier, so I've been putting details of events on the dataZED website.

It's not just for the major events like Big Data London - I also want to see the get togethers and networking groups that are taking place every week of the year.

So, everyone who tells me about an event by 31 January will be entered into a draw for another bottle of wine. Alternatives can be given for those outside the UK or who don't want wine!

Don’t forget!

1. The next dataZED Get Together is coming up! Click here to sign up

2. dataZED of Data Events calendar. Share an event and View the calendar

3. Event and other sponsorships. I’ve created a sponsor pack about the ways that dataZED can work with you to advertise your business or collaborate on an event. Message me for a copy.

You’ll find a tagged, searchable website of these newsletters at datazed.beehiiv.com, where you can also subscribe to get them sent straight to your inbox.

I like having conversations with people. It’s more fun than doing sales and marketing, and usually leads to better outcomes.

To that end, I have a link where anyone can book some time in my diary for a quick chat. I don’t always know what to expect…

Client confidentiality usually means I can’t share too much, but it turns out that Santa is generous kind of person so he let me tell you about some of the ideas we discussed.

Understand what data you have got

Santa’s organisation has got legacy data going back centuries. The old stuff is stored offline but some of it’s been scanned. For the last few years, everything’s digital.

There’s a discussion to be had about what data he should be holding onto, and how to ensure that he complies with GDPR and its international equivalents; there’s also a historic value to the records.

First though, we need to consider cataloguing the systems and data sets that they have. Then we can consider where the value is.

Evaluate the use of Generative AI

Santa’s no different to the rest of us. He loves ChatGPT and can see Gen AI making a massive difference to his operations, especially when you consider all that legacy data.

He needs to be careful about over-reliance on Gen AI though. The reputation of Santa relies on getting the right toys to the right children; and any mistakes that occur through relying on a SantaLLM could have a massive reputational impact, which could easily be exploited by rivals such as Amazon.

Whilst Santa should definitely be investigating Gen AI, I’m recommending that he proceeds slowly and with an abundance of caution.

Beneath it all is Data Quality

Santa relies on knowing who each child is, and where they live; together with an understanding of the requested items, along with constraints such as budget and age-restrictions.

Strong validation on data entry helps here, along with checks for inconsistencies. The reliability of the data can be furthered strengthened by acquiring third party data sets of name and address data.

The power of Master Data Management

A known issue comes from the cheeky kids who submit multiple requests under different versions of their name. He’s also found some adults who try and game the system by amending their age so they remain fifteen years old. Again.

By bringing together customer data from multiple sources and over previous years, we can build a “golden record” so we can understand each individual person, their relevant data, and what items they have received over the years.

Decision Intelligence - the thing that most people don’t know about yet

Santa was already aware of the power of dashboards to analyse his data so he could make better decisions. News to him was the new wave of Decision Intelligence tools which not only report on the data, but can make recommendations on the next best action.

As we wrapped up the call, I asked Santa if he could give me the gift of bringing me lots of new and interesting clients to work with.

He said that wasn’t possible because

• I’m too old

• He’s a fictional character

But he did suggest that I ask all my friends and contacts to do that instead, and to let people know that if they click https://www.datazed.co.uk/get-in-touch then they too could have an interesting conversation with me.

Do dreams come true?

Have a wonderful week,
Charles

P.S. On 31 December, I’ll be picking one subscriber out to receive a bottle of wine from me (UK addresses only). I probably shouldn’t share that here because you’ll be thinking that getting me more subscribers lessens your chance of winning; but think how happy your friends would be to not only get a reasonably interesting email, but to also get something to drink at the same time.

Don’t forget!

1. The next dataZED Get Together is on 10 January. Click here to sign up

2. dataZED of Data Events calendar. Share an event and View the calendar

3. Event and other sponsorships. I’ve created a sponsor pack about the ways that dataZED can work with you to advertise your business or collaborate on an event. Message me for a copy.

You’ll find a tagged, searchable website of these newsletters at datazed.beehiiv.com, where you can also subscribe to get them sent straight to your inbox.

In our final extract from November’s dataZED Get Together with Change Management expert, David Cowan, we look at the people skills that we are looking for, and how to find them.

If you are a new reader, then I’d encourage you to catch up the last two newsletters.

• In part one, we explained how to create a compelling business case to gain the support that you need from a sponsor with authority; and

• In part two, we considered elements such as storytelling, articulating financial benefits, incremental delivery and emotional intelligence.

Project teams can be assembled in one or more of the following ways:

• The regular BAU team

• Specific change or project management experts brought in to support the team

• Consultancy firms and individual contractors

• Implementation and Customer Success people from your tooling vendor or systems integrator

Wherever the team comes from, the person responsible for the success of the activity has to build it and blend it for the required purpose.

Balancing Technical Skills and Communication

• Don’t solely focus on technical brilliance in hiring. There is a need for effective communication and stakeholder management skills.

• The skills required for influencing senior stakeholders differ from those needed for detailed technical discussions. and it’s a common issue that technically brilliant individuals struggle with presentation and communication.

• This does not mean that you shouldn’t bring in that technical person. You may be able to develop their skills or complement them in the team with strong communicators.

Using interviews effectively

• Contractors expect a minimal number of interviews or tests in the recruitment process, given the limited duration of the assignment. Consequently, interviewers need to find out the most important information in minimal time.

• Ask specific and relevant questions during the interview. We don’t want a generic overview, we know what project we want to deliver and it’s the skills for that project that we need to identify.

• Interviewers should stretch the candidates beyond their stock answers by asking follow-up questions. By probing deeper, it will reveal actual experiences and problem-solving approaches.

After the interview

• Score candidates based on their responses to assess their suitability. Some firms have developed a template where responses are graded, and then the total score considered. This helps to mitigate bias. An additional benefit is that each interviewer can score privately, making it easier (for example) for someone more junior to disagree with a senior leader.

• After the interview, leverage your network to gain more insights into potential hires. You’re not looking for a simple “good or not good”; rather you want to know if they will advance the project.

Interested in chatting through these principles a little further? Why not book a discovery call?

dataZED grows through referrals! Whether it’s by sharing this newsletter, pointing people my way for advice, or introducing dataZED to a client, please help me get the word out about our services.

Have a wonderful week,
Charles

Don’t forget!

1. The next dataZED Get Together is on 10 January. Click here to sign up

2. dataZED of Data Events calendar. Share an event and View the calendar

3. Event and other sponsorships. I’ve created a sponsor pack about the ways that dataZED can work with you to advertise your business or collaborate on an event. Message me for a copy.

You’ll find a tagged, searchable website of these newsletters at datazed.beehiiv.com, where you can also subscribe to get them sent straight to your inbox.

If you are a new reader, then I’d encourage you to catch up on part one - in last week’s newsletter, where we explained how to create a compelling business case to gain the support that you need from a sponsor with authority.

Now let’s return to November’s dataZED Get Together with Change Management expert, David Cowan.

Storytelling

• The success of a project lies in the outcome rather than the details of how it's achieved.

• Use storytelling to convey the benefits and impact of the project.

• Think about this from the start. If the project was a complete success, what would that look like at the end? Once you have clarity on that, you can plan towards it.

Finance

• You are almost certainly going to need the CFO or Head of Finance on your side.

• The business case must clearly articulate financial benefits and efficiency gains.

• If you find it hard to quantify, then come up with something anyway, and share it informally with your stakeholders - ask them for advice. Not only will they help you with the quantification, but you are also making them aware of the aims and goals.

Incremental approach to delivery

• Advocate for an incremental, agile approach to project delivery.

• Demonstrate quick wins to gain credibility.

• Avoid the risks associated with a big bang approach and focus on delivering value continuously.
  

Emotional intelligence

• Emotional intelligence must be a core capability. It involves understanding and navigating emotions in oneself and others, enhancing interpersonal relationships for effective project management.

• This is particularly critical when building a project team whether it’s internal colleagues from different business teams, or outside contractors, consultants or vendor partners.

• Building trust and influencing others requires a combination of technical ability, communication skills, and stakeholder engagement.

Interested in chatting through these principles a little further? Why not book a discovery call?

And that wasn’t all! I have even more to share in my next newsletter.

dataZED grows through referrals! Whether it’s by sharing this newsletter, pointing people my way for advice, or introducing dataZED to a client, please help me get the word out about our services.

Have a wonderful week,
Charles

Don’t forget!

1. The next dataZED Get Together is on 10 January. Click here to sign up

2. dataZED of Data Events calendar. Share an event and View the calendar

3. Event and other sponsorships. I’ve created a sponsor pack about the ways that dataZED can work with you to advertise your business or collaborate on an event. Message me for a copy.

You’ll find a tagged, searchable website of these newsletters at datazed.beehiiv.com, where you can also subscribe to get them sent straight to your inbox.

…and the answer is…

“Start”.

You’ll have two reactions right now.

One is “why did he put the answer at the beginning of the article when you normally need to scroll through the text to get there?

The other is “what an obvious answer. Where’s the value in that?”

Actually, there might be a third. “Did I miss some of these emails?”

Sorry about the latter, it’s been too many weeks since I last shared my thoughts with you.

Back to the topic…

As always, we need to drill a bit deeper. The ability to start a data project is rarely in our hands alone. To get started, you normally need sponsorship, and that’s the bit that we can find really tricky.

That’s why the dataZED Get Together for November featured Change Management expert, David Cowan.

We had a fantastic round table discussion, which continued over the drinks kindly sponsored by Profisee, and in this newsletter I’ll share some of the discussion.

Project success often hinges on having a sponsor with authority and a solid business case and indeed, lack of sponsorship often leads to project failure.

To get going, a great business case is essential, emphasizing the importance of the outcome, not just the tools or solutions.

How can we make our business case great?

1. Tailor your approach to your stakeholders. That means working out who they are. Sponsorship should come from someone with power, not just a title.
   

2. Tailoring also requires you to understand their motivators and drivers. For some this could be revenue generation, for others it’s regulatory, legal or risk driven. Another group may be focused on your financial efficiency and likely return on investment.
   

3. Emphasise a compelling reason to initiate the project. Not everyone gets excited by clean data, and your organisation’s leaders are considering all sorts of possible activities that they can invest in. What makes the data one so special?
   

4. If you want to invest in tools or tech, remember that the success of the project will lies in the adoption of the tool and the outcome delivered. Your stakeholders, especially senior leaders, are more interested in the outcome than the details of how it's achieved.
   

5. Advocate for an incremental, agile approach to project delivery. This enables you to demonstrate quick wins so you gain credibility. It also avoids the risks associated with a big bang approach as you focus on continuously delivering value.

This points in this outline are the keys to developing the business case that will get your project sponsored. To chat through them a little further, why not book a discovery call?

And that was just part of our session. We continued with discussion around project delivery, emotional intelligence and getting the right people in the right roles.

I’ll share some of that in future newsletters.

A quick plug for my services, I currently have availability for client work and would love to work with some new organisations who want to trust their data more so they can get value from it. Hit reply to this email or use the link above to book a call with me.

Have a wonderful week,
Charles

Don’t forget!

1. The next dataZED Get Together is on 10 January. Click here to sign up

2. dataZED of Data Events calendar. Share an event and View the calendar

3. Event and other sponsorships. I’ve created a sponsor pack about the ways that dataZED can work with you to advertise your business or collaborate on an event. Message me for a copy.

You’ll find a tagged, searchable website of these newsletters at datazed.beehiiv.com, where you can also subscribe to get them sent straight to your inbox.

One week to go!

dataZED Get Together on 6 September with Alex Plenty, CDO at Lloyd's of London, sponsored by Pyramid Analytics. We’ll be looking at how you can manage your Data Governance and Data Quality roles in the oncoming wave of AI.

Alex is an insightful and outspoken speaker and worth coming to hear from.

Wednesday 6 September in London EC3 from 4pm to 5pm, with drinks nearby afterwards.

Click here to sign up

A different approach

So I did take a break from writing (and mostly from posting) over the holidays, but this had a benefit of creating some thinking time.

And you, my loyal readers, are the first to find out about it.

The premise is this. For the ten years plus that I’ve been doing Data Governance, Data Quality and Master Data Management, I have focused on pragmatic, value adding activities that are there to help the organisation by increasing revenue, reducing costs, or mitigating risks.

I’m not going to say that everyone felt that - other teams may take the credit for the revenue growth or cost reductions; no one really likes doing the actions which are necessary to mitigate risk - but it was the over-riding focus.

What this leads to is a very nuanced and specific offering which is perfect for a small group of people and organisations. If you know that you want to improve your data, but can’t (or don’t want to) do it in house, then dataZED can give you the support and insight that you need.

We own a small niche but for much of the world we are irrelevant.

On the other hand…

Everyone has a view on data.

Everyone knows when they see bad data, or data which is used badly.

Everyone wants to fight it.

The details of how and what will vary, but everyone wants to fight bad data.

So I’m launching a campaign with a five point pledge to #FightBadData

• Verify Before Trust

  Before making decisions based on data, I pledge to always verify its source, validity, and timeliness to ensure it's credible and relevant.

• Stay Informed

  I will educate myself about best practices in data quality and data management.

• Promote Transparency

  I pledge to always be transparent about the origin of my data, its collection methods, and any modifications, ensuring anyone who accesses it understands its context and limitations.

• Speak Up

  If I encounter bad data, misleading visualizations, or misrepresentations, I commit to speaking up and challenging it, advocating for clarity, accuracy, and reliability.

• Empower Others

  I commit to sharing my knowledge and resources with peers, colleagues, and my community, ensuring we're all equipped to fight bad data together.

and of course, we need a brand and a website…

As I said earlier, you’re the first to see this, and I’d love to get your thoughts.

Will you take the pledge?

Check out FightBadData.com and have a wonderful week,
Charles

Don’t forget!

1. dataZED Get Together on 6 September with Alex Plenty, CDO at Lloyd's of London. Click here to sign up

2. dataZED of Data Events calendar. Share an event and View the calendar

3. dataZED Fantasy Premier League, raising funds for Data and Tech Aid. Read more here and Register here.

You’ll find a tagged, searchable website of these newsletters at datazed.beehiiv.com, where you can also subscribe to get them sent straight to your inbox.

First the reminders:

1. dataZED Get Together on 6 September with Alex Plenty, CDO at Lloyd's of London, sponsored by Pyramid Analytics. We’ll be looking at how you can manage your Data Governance and Data Quality roles in the oncoming wave of AI.
   
   Alex is an insightful and outspoken speaker and worth coming to hear from.
   
   Wednesday 6 September in London EC3 from 4pm to 5pm, with drinks nearby afterwards.
   
   Click here to sign up
   
   

2. dataZED of Data Events calendar. August is quiet for obvious reasons, but September is filling up.
   
   Share an event and View the calendar
   
   

3. Less than one week to the start of the season!
   
   dataZED Fantasy Premier League, raising funds for Data and Tech Aid. Read more here and Register here.
   

Would you like people to be reading your thoughts here?

I would like to expand the scope of The Datazed of Data Governance to include more voices, and I know that there are many people who have written, or are ready to write, the occasional piece without commiting to a regular newsletter.

If you would like to be a guest writer, then feel free to get in touch. You can submit something for inclusion which you have already written, or chat to me about your ideas.

I was going to skip the newsletter over the holiday period, but the readership has been growing over the year, so instead I’m re-sharing a popular article from March.

The power of perseverance

Nobody ever started a data governance programme to deliver immediate results.

You might find some quick wins, and these are essential to build up your credibility with stakeholders, but it takes longer to deliver real value.

I was given a personal example recently when I got a positive reply from one of these Datazed of Data Governance emails - three months later!

Why does it take so long for data governance to pay off?

We don't roll out data governance as a big bang

Well, we don't if we want to be successful.

If you consider the components of a data governance framework (people, process, tools), then it takes time to obtain the information you need from the business, and then to build out the content to a level sufficient to be useful.

If you try and launch data ownership, a data dictionary, a lineage map and a data quality process all at once, then you will find that you will be asking for a lot of time and insight from your key subject matter experts.

Not only is this hard to obtain, but it's also putting an unfair load on people who are already busy with their day jobs.

Our data community will take time to add value

First you will work to identify your Data Owners and Data Stewards. You'll do some training so they know what to do; and some of them will engage.

You'll start collating a series of data issues, which will be of differing levels of impact, severity and solvability (is that a word?).

The first set of data issues won't be the critical ones.

As the data community gets used to the idea of challenging their data and flagging issues, the significant data issues will start to emerge.

Then they need to be solved.

Again, this doesn't happen overnight.

Data Governance is not a project

Whilst kicking off Data Governance, or implementing tooling, may well be structured as a project; Data Governance itself is an ongoing activity that needs to become part of your regular business activity.

For example, it's not just about populating your data glossary, but it's about refining those definitions and building out its scope over time.

Your data lineage will be continually changing and diagrams from last year will no longer be accurate.

Where Data Owners have taken on a new role, a new person will need to be trained in what to do, and why they need to it.

Data Governance is not about immediate results. Success comes from good habits, carried out consistently.

To discuss this, or anything else around Data Governance and Data Quality, then click here to get some time with me.

And if you think this article might help someone else, then forward this email or share the link with them. It’s through your referrals that I can grow this newsletter and reach more people.

Have a wonderful week,
Charles

Don’t forget!

1. dataZED Get Together on 6 September with Alex Plenty, CDO at Lloyd's of London. Click here to sign up

2. dataZED of Data Events calendar. Share an event and View the calendar

3. dataZED Fantasy Premier League, raising funds for Data and Tech Aid. Read more here and Register here.

You’ll find a tagged, searchable website of these newsletters at datazed.beehiiv.com, where you can also subscribe to get them sent straight to your inbox.

First the reminders:

1. dataZED Get Together on 6 September with Alex Plenty, CDO at Lloyd's of London. Click here to sign up

2. dataZED of Data Events calendar. Register an event and View the calendar

3. dataZED Fantasy Premier League, raising funds for Data and Tech Aid. Read more here and Register here.

Having someone like me to accelerate your data governance and quality projects, or to run things on an interim basis, is great, but in the longer term your firm will probably want a perm employee to manage things.

There is a lot of sense to this, and I've even helped clients with the selection and interview process as I'm independent, but finding the right person and getting them through the recruitment process is hard.

This article tackles the three biggest challenges that firms face.

We don't have budget for headcount

This challenge could come from a COO who really wants a data lead; or a data lead who needs a manager; or a manager who needs an analyst.

The person doing the role realises that they are doing a lot of work which could be delegated to more junior (i.e. cheaper) resource, but the budget holder does not have this as a priority.

The challenge here is to make an effective business case, focusing on the value gained by bringing in the extra support.

If your time is now freed up, what extra value can you add?

State it. Quantify it. Validate it.

We can't find someone who knows our industry

Your firm is successful because it is really good at what it does. And that's a specialism established over many years.

So how can someone from outside your special world possibly know how to lead or manage your data?

Sorry to burst your bubble but they can.

These people are not here to do your front-office work of widget making, lawyering, insuring or doctoring; they are joining you to work with your data.

And your data doesn't know what industry it belongs to. It's just letters and numbers.

I agree it's much easier to bring in someone who knows your world.

But you tried that and couldn’t find them, so look wider and get them trained up in the jargon of your world. They are smart people and they'll pick it up.

There's no one else in our business who understands data

You've disagreed with the last section (that's cool, I won't hate you) and insist that the new person has to know your sector really well.

So what about the people already in your business?

At any given time, there will be enthusiastic people looking to stretch themselves into their next role, and their promotion path might not be clear for a while.

Or maybe they don't want to be more senior, they just want to try something new.

And a lot of people are interested by the idea of working in data.

So why not bring them onboard and get them trained up in the relevant data specialism? They already learned how to do their current role.

To discuss this, or anything else around Data Governance and Data Quality, then click here to get some time with me.

And if you think this article might help someone else, then forward this email or share the link with them. It’s through your referrals that I can grow this newsletter and reach more people.

Have a wonderful week,
Charles

Don’t forget!

1. dataZED Get Together on 6 September with Alex Plenty, CDO at Lloyd's of London. Click here to sign up

2. dataZED of Data Events calendar. Register an event and View the calendar

3. dataZED Fantasy Premier League, raising funds for Data and Tech Aid. Read more here and Register here.

You’ll find a tagged, searchable website of these newsletters at datazed.beehiiv.com, where you can also subscribe to get them sent straight to your inbox.