Getting Started with (Just Enough) Data Governance
Small Steps You Can Take Now to Address Your Data Governance Issues
Plans to implement data governance often fail, despite the best intentions. Why? Because we go out the gate trying for perfection and become overwhelmed. We start to avoid and procrastinate. We make excuses: Too busy. Too hard. Confusing. Not fun.
The hardest part of data governance is getting started. In this on-demand webinar, we address practical, actionable steps you can take immediately, and on a small scale, to start your organization down the path to healthier data through data governance:
- Prioritizing data issues to identify a starting point
- Gaining support from senior leadership and key stakeholders
- Choosing your initial data governance team
- Applying an agile approach to the data governance process
- Building momentum to drive data governance into the organization
We share real-world examples of what’s worked for other organizations. Along the way, you will also learn some simple and efficient tools for prioritizing and documenting your data elements as well as often overlooked change management techniques.
This continues where our first on-demand webinar, Why Bother with Data Governance, left off. There, we defined the purpose and value of data governance to an organization. We highlighted the signs of risk from data complacency and spoke to the big components of a data governance program.
Mike’s 30-year career has been focused on helping organizations worldwide make better decisions through the use of data, information and analytics. Mike joined Senturus from Slalom Consulting, where he led the Chicago Information Management and Analytics practice. Before Slalom, Mike was part of IBM’s acquisition of SPSS, where he held a variety of global leadership roles in the SPSS professional services, training and partner channel organizations.
Greetings to everyone and welcome to this latest instalment of the Senturus Knowledge Series.
Today, we’re very excited to be bringing you the topic of getting started with just enough data governance.
As you know, data governance can be a big topic and we’re going to try to demystify that and skinny down so you can figure out how to get started and leverage this important area of business intelligence in your organization.
Before we get into the meat of the presentation, to advance the slide Mike.
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Our agenda today, after a couple of quick introductions, we’re going to get into, again, just enough data governance, talking about prioritizing data issues, gaining support, picking your team and then executing and building momentum.
After that, we’ll go through a brief Senturus overview for those of you who may not be familiar with our organization, and what we can offer you, as well as some free additional resources, and then we’ll get into the Q&A, so make sure you stick around through to the end.
I’m pleased to be joined today by Mike Bigenwald, my colleague.
He has a 30-year career that’s been focused on helping organizations worldwide make better decisions through the use of data, information, and analytics. Mike Bigenwald came to Senturus from Slalom Consulting where he led the Chicago Information Management and Analytics practice.
At Slalom Mike was part of IBM’s acquisition of SPSS or he held a variety of global leadership roles in the SPSS professional services training, and partner channel organizations. My name is Mike Weinhauer.
I am also here at Senturus and among my various responsibilities I have the pleasure of hosting our Knowledge Series webinars.
Now, before we get into the presentation, those of you who’ve done this before, or recognize that we, we usually do a poll and ask our audience for some feedback about their organizations.
And today’s question is, what are your data issues? This is a multiple-choice question, multiple choice, questions, so please select all that apply. Is it defining the metrics that you need to have governed or that you want to surface
master data management, establishing a single source of truth, data quality, and or data access slash security slash privacy.
We’ll go ahead and get those answers in here, about half of you, of major selections.
A few more seconds to get those in.
All right? I got pretty close to a 100 percent here.
And it’s quieting down so I’m going to close this out and share it.
So, um, data quality is leading the pack, followed by establishing a single source of truth, but really all of these had pretty substantial representation.
So, that’s, that’s interesting to see, and I would save from my perspective.
Not super surprising in terms of the results, but thank you for sharing your feedback. That’s always good to see what’s going on with our clients.
So, with that, we’re going to move on to the next slide, and I’m going to hand the baton over to Mr. Bigenwald. Mike, the floor is yours.
Thanks, Mike, I really appreciate that. Just a quick welcome to, to everyone who took some time out of their day to day in this, January to join us for this topic. And also welcome to those of you that, some point down the road, downloaded this and wanted to learn a little bit more about data governance. So today, yeah, that’s what we’re here to talk about. So just to sort of set the stage, I’d like to share. Just a few facts that we uncovered in. You know, in thinking about coming out and talking today about this topic, you know what we’ve seen, there’s just been a dramatic cultural shift with the explosion of data being created. You know, it’s being coming, available in a democratic way, across organizations.
You’ve got self-service environments really, in organizations of all sizes, you know, from start-ups to the largest enterprises. The shift has caused a lot of, it’s got a lot of firms off guard, its leader, led to mismanaged data assets and poorly defined metrics.
Uncontrolled access authorizations. These things cause delays and confusion. Poorly informed decisions, and, you know, and there’s cases where data breaches of have resulted in, you know, legal liabilities and such. So, there’s a lot happening in the data space today, a lot happening in each of our organizations.
And so, here’s some facts and figures that we uncovered, according to one study that we found a self-service BI market size has been estimated that it doubled in size from 2016 through, through this year, that’s compound growth of 15% annually over a five-year period. So, we’re seeing consistent, ongoing growth and really an acceleration there, of that growth, some other data points. It materially in an IDG came out with a survey. That’s the data volumes are growing. At an average of 63% per month among the organizations that they spoke with and fully 12% of those organizations were doubling.
The data volumes in their organization on a monthly basis that’s just almost unsustainable growth.
Another data point we found reportlinker.com reports that data lakes market the market for data lakes growing at a compound rate of 27% from 2019 to 2024. They’re seeing continued demand and growth in this space. It’s 237% growth over that time.
OK, so, another study found that mid-size organizations between say 500,000 employees, when they had a data breach, the typical cost of the data breach was over $2.5 million, or $3500 per employee. That’s really tough for a small organization to recover from when you sort of hit to your bottom line.
We spoke with Microsoft and they tell us that as you’re just since the pandemic started less than a year ago, about a year ago now. They’ve witnessed a 775% increase in demand for cloud services at Microsoft. So, she’s a lot going on in. this, all sums up into a one really large number here.
500 billion dollars, that’s what the cloud migration services market is expected to reach by 2025. It’s well over $120 billion today, so there’s just a lot of activity going on to people realizing they need to manage their data differently, and in a better way than perhaps they have before.
So, you know, you registered, and you made the time to attend this webinar, so I’m going to make a few assumptions.
One, I’m going to assume that you have an interest in introducing data governance into your organization, you know, whatever your role is, you can play a part. You know that, right? You may be the CTO of regional manufacture, and you’re expanding rapidly and you realize you need to address some customer master issues before they get any more complicated. It might be a business analyst and you maybe you’re tasked with figuring out why you’re reporting does not agree across departments and data sources.
It’s possible. You may even be a Compliance Officer; you know a small bank. And you’re concerned that too many people have access to just too much business information across your organization. So, there’s a lot different roles you can be playing.
Couple other assumptions. I’m also going to assume that you’ve probably investigated starting a governance program in the past. And no prize seemed just too complicated involved, and time consuming, expensive, and this page that you’re looking at here on the screen. No, really demonstrates sort of the key components of a good, solid data governance program that we’ve talked about in other webinars here at …, and I encourage you to go check those out. Data governance can be complex, and it can be appropriately.
So, it can be a very big undertaking involving dozens of people across all levels of your organization, but, you know, if the title of today’s webinar caught your attention, you probably joined us, because you feel a little bit like this, So, this fellow here, right? I don’t know if he’s a cat or what he is, but she’s overwhelmed, right? Seems like a lot.
And you probably dialled into, you want to hear what we have to say, what our thoughts are, about just enough data governance.
So, for the remainder of this hour, what we’re going to talk about is just really some simple actionable steps to start your organization on that journey.
Data governance, before we move on, I would just be clear, you know, whitson tourists, we believe that a robust well-structured data governance program is essential, for Affirmed, to really be competitive in this data driven economy. So, what we’re sharing today is just a starting point, and the idea is to make incremental progress towards, towards your long-term data goals.
So, here, on this page, we see a pretty typical definition of data governance.
On the left side of the screen, you know, it’s like data governance is a framework for ensuring the availability, accuracy, and security of data across the organization, I think we can all agree.
That’s a pretty generic definition, and its accurate, no, frameworks are an important, useful way to break down large programs, but we’re not going to spend a whole lot of time going into deep frameworks today. Instead, we’re going to take the lessons that we’ve learned through the data. Governments work, we’ve done and then did a lot of the work that we’ve done over the last 20 years, helping companies grapple with tough data issues, in with all sorts of challenges, regarding data in the organization. And we’re going to bring some, just some useful ways to leverage the key components of a data governance framework to get started.
So just as we get into any of this, then there’s, you know, some concepts that I’d like to discuss, and you know, I had a manager lunch. There’s summed up getting started on any daunting task like this. She said, you don’t need to boil the ocean to make a cup of hot tea.
We’ve probably all heard that metaphor before, and I think we’ll get what it means. I’ll stop there. And I won’t subject you to an extended tea preparation metaphor or anything, but, note, that the concept has merit, right?
And so, we think about Agile, that, you know, Agile development methodology, for software, really follows certain concepts, that are useful for, for making a large project bite size, and to keep a project on track, to deliver incremental value throughout. And just a few of those concepts that we’re going to subscribe to, that we do subscribe to its interests and then I’m going to refer to today the first one, you know, MVP.
The concept, it’s a key concept. In agile, it’s the idea of targeting as a goal, development of a minimal viable product, right. It’s just a deliverable increment to something in the Agile development process. So, it’s just enough to prove out a hypothesis, make incremental progress towards a larger goal.
And so, with that, we’re going to, we’re going to take that concept, and we’re going to think this, this light framework as a process, MVP, but rather than a product, it’s a process. Right?
So, we talk about minimum viable process as our MVP, it will give us just enough to prove value, you know, because it’s an opportunity to course, correct, early in the development of a full governance initiative, and, you know, it all while delivering incremental value at every stage of the initiative.
A few other agile concepts that I want to talk about here for a second, know, we adopt the concept of sprints, where it’s simply that’s just breaking a project down into 2- or 3-week work plans, really, with the goal of delivering a working solution to whatever was fit into that, that work time by the end of that cycle.
Scrum, Scrum? You probably heard of that before, if you’re not super familiar with Agile. Most people have experienced that, or seen it. That’s just a daily, 15-minute meetings in the morning. They’re often called stand ups. On the idea there is you get that core team together to discuss what’s complex yesterday. What’s being worked on today, and just work through any blockers towards progress.
And then, just the last concept that we will subscribe to here, in this conversation, is around backlog and backlog is really just a prioritized list of items to be worked on in each sprint during the project timeline.
So, you know, our late framework then, consists of four areas of focus to get started with what we’re calling just enough data governance, right? That MVP, if you will. We’ll spend the remainder of our time discussing these four areas. So, you know, given that we talked about prioritization and backlog, let’s start with prioritizing your data.
It just a note, in a typical data governance framework, you establish the need for governance. First. You get gained support from the organization
to gain funding, and gain resources to build a team, and then that team will get together to the hard work of issue prioritization. Because this is an MVP minimum viable process. It’s not a full-throated governance initiative yet.
You should start with just a short list of topics or issues or concerns. There, probably, you’re on a post it note on your desk. Or, you know, certainly, it’s the discussion that you’ve had with some of your colleagues, which are one of the reasons why you probably showed up here today.
But when you get this list together, it gives you something to work from, to, create a case for support involvement of resources from other parts of your organization.
So, just in terms of prioritization. There’s a few things and they might, they are a bit pulling against each other, but really important.
When you’re putting this list together, you need to be pragmatic, right. Because this is an MVP. It’s not a full-fledged program.
So, what is something that is doable and is also, so, it has some sort of value to it, right? You don’t, you don’t want to bite off more than you can chew or take on too big of a, of an initiative at the first. But you do wanna go something that’s relatively low hanging, right?
Something that’s, that’s obvious, and it’s one of those items on that, that post it note, As I mentioned, or, or it’s something that’s certainly, you know, is, is a known issue. And then the third piece is, it’s got to actually provide some sort of value. Right? It’s a key data issue that actually does drive a difference. So, you know, with those kind of competing priorities in mind, you want to think about, well, what is it that we have to work with. And, you know, some of, these, some of the issues that typically come up around data elements for, for data governance, include, metric definitions.
You know, just where you have different numbers, with the same name different, you know, different definitions of the same metric in different reporting in different parts of the organization. You might be dealing with the hierarchies or trying to you know, get control of master data? or your customer, customer master. You might have different databases with slightly different data sources, or you’re looking to establish a single source of truth.
The one place to go that, you know, has been certified as the correct definition.
You might be dealing with data quality issues. Cleanliness says she’s those sorts of things. Access to data, Compliance issues. There may be privacy concerns between GDPR, HIPAA, if PTI, PA, a PI, California’s got a new in the new privacy initiative. There’s a lot of privacy concerns that can result in in real legal issues for an organization. So, there’s a lot of things here to consider when you’re, when you’re creating that initial prioritization list today, for, as an example, we’re going to follow this real simple example of standardizing the set of metrics will go. As customer success metrics, they’re pretty easy to understand a lot of people that have worked with them and we’re just going to create a really simple ditch dictionary of govern definitions and then show you the process to get to that.
Um, and just to extend the example, then let’s say we’re talking about a mid-size software vendor. They’re struggling with customer churn, even as they grow new subscribers, so it’s a kind of a classic leaky bucket scenario.
So, to actually get down to the prioritization exercise, what will you do is you say, what is the most important criterion for prioritizing governance, right? You want to identify, prioritize, what’s to be governed? So how do you do that? Well, you identify, you get that list together. Well, established. Just some criteria: make it quantitative, right? So, we can actually score these, and then, we’ll map the map them out, rather onto a, kind of a simple for box matrix of value versus feasibility.
And so, in this example here, you can see on the left side of the page, is really some value measures that we’re going to score these metrics in the scorecard, you see on the bottom right side of the page, across the top, on the right side. Those are feasibility measures, really, around the effort and complexity to, to make these changes, to address these issues. So, we’ll spend a ton of time on this, but just walking through how we approach this, and this is a very simplified approach, obviously, but starting with business value.
What does that mean, right? What are their direct impacts to customers, to our channels, to our operations?
Does this include some sort of production or process, it gets impacted, these sorts of questions. And we’ll just shake a simple 1 to 5.
Scale and say, five, absolutely critical to operations to the business.
one, didn’t, even know, we had it, not important to us. And you, you assign some values to that.
And the way to think about that is, you know, how many of these direct impacts are there to we have to customers, and channels and such?
You know, who’s impacted across the organization, you may want to ask a question like that. So, breadth of views is really important, just to understand.
is this kind of a one-off metric, or is this something that impacts multiple departments?
A few things to know with that, though, you know, it can impact a small number of people, or departments, and still have a very large business impact, and at the same time, broad use across the organization, really doesn’t necessarily mean that it’s a high priority item, but this is something you want to address and discuss, and, and, and capture.
Probably as much as anything, because this is where you’re going to find where those same metrics, with the same name two metrics, are attached to various definitions across departments. So, this gives you, this is part of your research here as much as anything else. And helps you really understand the impact to the organization.
And then when you’re, when you’re, value, exercise and scoring, you wanna look at anything else, it impacts your business.
And, and in this example here, we’re choosing regulatory compliance, and, well, maybe it doesn’t make a whole lot of sense for, for these metrics, is, the point is, there’s going to be something, or many, there may be several things in this area that are just unique to your business. You want to make sure you capture, in this example of risk in regulatory compliance, it’s just binary 1 or 0, either as something that is used in some sort of risk mitigation that we are operating with or, you know, maybe some regulatory reporting that we have to do. Whatever. the reason, that’s something that we need to flag as an important value.
You may not have, this is one of yours, but know that there, this is flexible and something that you can choose what’s important to your business.
So then on the across the top, we’re really talking about feasibility here, right. And a couple of key things that drive feasibility is what’s the effort, right. Just the amount of time it’s going to take. You can measure it, you know, an hour, days, sprints, whatever is right for you.
But, well, how long it’s going to take to research and document, implement, communicate, communicate the impact of these changes across your organization. And again, just use a simple one. To five scale, five being you can define that as being over some number of hours in one, being plus some, and spread it out.
Just to, again, the gate the goal here is to stratify out these different metrics.
And the second half of that, then, that that feasibility measure is complexity and that’s different than the actual effort. This is really reflecting just the complexity of bringing a data element into being governed. And it’s the, you know, what are the people and systems involved, how many reports and processes are impacted, those sorts of things.
Just how complicated, I mean, what’s the level of complexity to, to make these changes?
So, if you look on the left side, then, we’ve got our scorecard here, filled out with the value scores for each of these metrics, As well as the feasibility scores, which gives us the opportunity to map onto that simple for box matrix that you see on the right side of the page here.
You know, in the upper right box, it’s, it’s, you know, sometimes referred to as the magic quadrants.
That contains the elements that this exercise identified as being both of high business value to the organization, as well as being, you know, relatively easy to accomplish relatively compared to the other items that were measured. And so, if you actually just take those in and map them out, I mean, a simple Excel spreadsheet, right?
This isn’t, is any sort of rocket science here.
You can stratify out those different metrics and you can see the opportunity to address, say, customer acquisition costs and net promoter score the two that are the highest to the right and top of the chart.
They’re stratified out in a real exercise.
where you’re going through no, many metrics.
This will up, you’ll see, you’ll see metrics and measures across all four of these boxes. And it really gives you an opportunity to stratify where to start.
But before you do that, there is sort of one last step that’s really important to take.
And that’s just to kind of review, make a final prioritization of this list through, though, I guess, a business lens. And there’s three components to that, that I’ll talk about a risk. And what I mean by risk is, what’s the risk of implementing this change and the impact of the business?
The risks that the business might be exposed to, but what also is the risk of not doing something and quantifying that are really understanding, recognizing what that is is important. In addition, then, you do want to measure the business impact, right?
What’s the business effort weighed against the, the benefits of mitigating the risks above and delivering on this.
And then the final lends it you want to make sure that you’re thinking about, and this is really for any activity you do in your business courses, is just, is there strategic alignment? Are we, are we addressing the data issues that are most directly impactful of our strategic direction, and the strategy of the company is implementing?
And with those, then at that point, you’ve got a prioritized list of data issues. And so, you’re equipped with some tools and evidence you need to work on gaining support for the state of governance MDP.
Good chance. Because you’re here, there’s already a grassroots support for fixing some of the obvious or glaring data issues that are out there. But there’s a few groups of stakeholders. They really need to be supportive and you need to make sure that you’ve brought them along on this for if you want this to succeed.
And, you know, while you may personally believe strongly in your analysis, and you wanna get, started on the items in that Magic Quadrant right away, you know, you can’t go it alone. And then, for any data governance initiative, even an MVP like we’re discussing here, which is pretty lightweight. You really need to have active support from at least these three groups of stakeholders.
And there’s a chance that you’re probably one of these, you represent one of these groups of stakeholders, so it’s really a matter of identifying the remains bull.
And what we’re talking about here is senior leadership, IT Teams, and Business users. These are the three key groups in your organization that are going to be impacted and in it for an MVP, certainly need to be part of this process. So, just talking about senior leadership for a second, when you’re, there are really key to success.
I mean, without senior leadership support, it’s really, really unlikely that a data governance program will get very far.
Because this is change, and change is hard, and, in any change in an organization that impacts large groups, needs to have that, the support from the top, right so that your organization knows to get behind it.
And so, when you’re talking with senior leadership, few things you want to talk about, you want to emphasize that people’s behaviours are governed, right. You’re not really data that’s governed, data is managed, managed, and handled by people, and so it’s those behaviours that we’re really talking about here. You want to emphasize this. This this will be lightweight, right? It’s an MVP today.
But, you know, even a full data governance program, it doesn’t have to blow up your current organizational charts, or, or, or, or move people off of the, of, the priorities that they’re working on today.
You just need to provide some structure and support to help people do this better. That’s what we’re asking from senior leadership. To tell them it doesn’t need to cost a lot of money, right?
Incremental improvements can be made by improving processes and just supporting people in the jobs they’re already doing.
And you see your leadership, they’re going to wanna know if the expense in time, the expense in opportunity cost and the real dollars that will be spent on this. Justifies, justifies the effort, right. So, you need to be prepared to talk some numbers and what the return on this investment would look like.
In our example, let’s just say, the senior leaders going to want to know that our customer success metrics are accurately representing our success or challenges, and reducing churn. So, that’s their goal, is, they want to know that what they’re looking at is accurate, and it’s actually representing their Net Promoter Score and nurture.
The second group that you’re, if you’re not part of that, you will absolutely have to gain support from is IT there. There is an essential partner in this, they have access to the source data and the systems.
So, when you’re when you’re looking for help and you’re, you’re bringing on board support from IT, you want to talk with them about, you know, they weren’t.
They’re going to wanna hear clearly defined accountabilities and responsibilities, they’re going to want to understand, who’s got what roles, how are they defined around data assets. You know, what can we prioritize? They’re going to wanna understand how this will impact their workloads, will impact other priorities that they’re held responsible for, and who’s going to do the hard work of rolling this change out, you know, so updating the reporting, the training, and the communication.
And then if you’re out of business user, when you talk with the business users, and you’re gaining looking to gain their support, you have to realize that the business unit users have a very vested interest in data quality Because, you know, their, their career is based on their performance, and their performance is based on decisions that are making using this information every day. So, to them, it’s, it’s, it’s the utmost importance. Business user is going to be interested in how this impacts their ability to do their job.
They’re going to want to know like, specific, like, how it impacts resolving specific known issues, Like, I wanna know what’s expected of them in the process, and what, what the commitment is. Ultimately, they wanna make better, faster decisions. They want to have more certainty in those decisions. They want to reduce the risk through data quality, consistency, and security. That’s what they’re looking for.
OK, so, um, in building that support for data governance, you’ve actually started the process of building your team, because that those same groups of stakeholders that you wanted support from are going to have some skin in the game, right? They’re going to be represented in and participants in this process.
And so, just like we talked about, IT Teams, business users, and senior leadership really make up the core Data Governance team in any initiative and certainly included in, in this MVP.
So, two roles that are probably the most important to talk about today are data stewards, and data owners, and data stewards.
Know, they’re the people who have responsibility for the day-to-day management of the read data resources. They could be the data producers, you know, they know the systems. They know how to access those systems. They’re responsible for quality of data, fight, A defined dataset really on a day-to-day basis. They are working closely with the data owners who will talk about in a second, to address data governance related issues.
The data owner, then, is really the primary data user, data consumer there, the, the representatives from the data domain or subject area, they are the experts on, on how the data is used and what it means. So, they’re the ones that are creating, reviewing, adhering to data definitions.
They’re accountable for quality of assigned datasets, so it’s up to them to make sure that they’re using good data and third, working closely with those data stores, then to address those same data governance related issues and activities.
Those two roles tend to come from the business and IT, it really depends on the organization, and where they sit, and, and, and what specific parts of the organization are involved in a governance initiative. The third area, though, that, that is consistent, is as that representation from senior leadership. I had mentioned earlier that that’s really critical for, for any, any major change.
Like, like a governance program would be, they ultimately in this, in this model, they have oversight responsibilities there, You know, runner, or members of the data governance committee, which is really, it’s made up of those kind of strategic, senior level representatives from all the stakeholder areas, Business and IT, and others, They’re kind of a primary issue resolution body, right, So, so, really, senior leadership is there to co-ordinate, drive co-operation, and communication across your organization In a up in a full-fledged data governance program. Something we’ll talk about in this MVP model. But in a full-fledged program, there’s also another level of executive steering committee, which is made up of the most senior enterprise leadership, think the C suite in a corporation, it. You know, typically this is an existing group that meets on a variety of steering topics and this would be added to those regular, you know, monthly meetings, or whatever the case might be.
Know, their role is to support sponsor these governance initiatives actively, and ultimately, they’re the ultimate decider when it comes right down to it, right? I mean, if you can’t make a decision by them, then they will make a decision for the group.
No, good way to think about who does What is, by creating what’s known as a racy matrix.
And you’ve probably seen something like this before RACI, meaning identifying who is responsible, accountable, who’s consulted, and then who is simply informed throughout the process.
No, in a formal data governance program, this document can look very complicated, it can have multiple tabs and different levels and you know, different roles and responsibilities and accountabilities based on part of the organization based on types of data element, et cetera. This very simple example here, we’re showing where the data steward is, the responsible party, the data owners accountable. And this is really what we’re talking about here, is that example of the data definitions and making them consistent.
At this point, at this point in your data governance MVP. You’ve identified areas of focus on youth. You’ve gained support for this initiative.
You’ve got key stakeholders, The organization on board. You’ve recruited some people to actually help you do the work and the effort. Now’s the time to execute right?
Now, is the time to actually put all this work, into action.
And so, you’ve identified your target data elements, right? You prioritize where to start.
You’ve decided on one consistent definition for your organization and gain agreement. You know where, and have documented. what, and who will be impacted by the change? What reports where they are? Any other data use? Any other uses for these data elements? So, you’re ready now, for the last step, and that’s to deploy.
You’re newly governed data elements, into your organization. In this case, we’re deploying these newly governed customer success metrics into our operational systems.
So, you know, this, this is a non-trivial activity, even for an MVP. It often involves groups that maintain data, sources of record data marts, your data warehouse. Those that generate an update reporting maintain data sources for self-service, really anywhere.
That these metrics touch the organization, you’re going to, you’re going to have to touch in deploying the metric. And that’s why it’s so important that, for an MVP, especially you think about the complexity. And you think about the, just the reasonableness of the ability to launch one of these.
But this is where the hard work pays off, right? This is where you see the tangible output in the use of those govern metrics.
And so, following our example here, very simple, definition lists four.
Given that this is an MVP, we’re keeping it very simple, right? We’re not going to produce an array of documentation like I showed you on that. one of those first slides, this morning, we’re going to just focus on one deliverable in this case, and that’s getting that customer success data dictionary out so that, you know, six or so data elements, we’ve identified the owners. We’ve got a written definition, we’ve got the calculation, and a single source of truth location all laid out here.
So, what that this becomes, this, this data dictionary or what it is, is very simple version, is the foundation for building an entire governance program. Because this is what you can use then as part of the MVP. To prove out and demonstrate the value and Tangible output of governance program. All right.
This is this something that’s very control up.
Controls In the small enough sense, that it’s, it’s something that you can use to to make your case, and it really should be used to actually deploy. These. Are these agreed upon definitions. I wouldn’t just use this as simply as an example.
So, you know, by following this data governance MVP with a very simple framework, you’ve been able to prioritize your issues, gain support. You’ve got a team in place, you’ve actually been able to execute on something, and show value. Now’s the time to build momentum.
And do that by sharing what’s been accomplished, got there, and measure the impact.
Quantify the return-on-investment data governance, so you can follow up on those initial conversations with the proof. Show the benefits.
Be ready for pose your next area for governance, because when you show them the results of this, there’s going to be a lot of excitement in your organization.
This is where you can continue to build on this MVP is, you can slowly or rapidly ramp up data governance, depending on your needs, and your, your organization’s ability to, to do that, at the very least, want to take these learnings and drive towards, you know, more conversations, and more awareness of the need and value of data governance programs in your organization.
So, that really leads us to, you know, are you ready to get started? And I recommend you do that, you know, you probably have that list. Identify those areas to address.
Make your case, recruit your help, and get your momentum going, right. It’s, we shared pieces of our playbook here on approaching data governance. Try it for yourself.
Senturus would love to help you as well.
We’ve got a lot of experienced this area, and we could help you accelerate that.
Mike, that’s what I’ve got to share with the team today. I’d love to hand the reins back over to you.
Yeah. That’s great.
Mike got a great presentation, please do get your questions into the question pane, and we will address those after just a couple of slides about Senturus.
But, as Mike said, there are a lot of areas where, where it’s a great idea to have help getting started on this process, or wherever you are on the journey.
And, we can help you wherever you are, and whether it’s doing that governance health assessment, whether it’s establishing that minimum viable process, or MVP, including prioritized governance programs, training, and education, as you roll it out to your organization. So, wherever you are, any, and all of those things, those are areas that we can help you with.
First, couple quick slides, before we get to the Q and A, and about Senturus, we concentrate our expertise here at Senturus on solely on business intelligence, with a depth of knowledge across the entire BI stack.
Our clients know us for providing clarity from the chaos of complex business requirements, disparate and ever-changing data sources, and constantly moving targets.
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We deliver solutions that give you access to reliable analysis, ready data across the organization, so you can quickly and easily get answers at the point of impact, in the form of the decisions you make, and the actions you take.
Our consultants are leading experts in the field of analytics with years of pragmatic, real-world expertise, and experience advancing the state-of-the-art.
We’re so confident in our team, and our methodology that we back our projects with a 100% money back guarantee, that is unique in the industry.
And we’ve been doing this for quite a long time.
We’ve been focused exclusively on business intelligence for over two decades now.
Working across the spectrum from Fortune 500 companies to the mid-market.
Solving business problems across many industries and functional areas, including finance, sales, marketing, manufacturing, operations, HR, and IT.
Our team is both large enough to meet all of your business analytics needs, and yet small enough to provide personalized attention.
Couple of quick extra resources here, we encourage you to visit.
The Senturus.com/resources link, where you can find hundreds of free resources from webinars like this one.
On all things BI two are fabulous up to the minute easily consumable blogs.
We have an upcoming event there. If you go to Senturus
.com/events, you’ll see that our next event is Cognos data module architectures and use cases.
We’re delivering that at our usual time on Thursday, February 11th, so you can head over there and take a look at that.
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I’d be remiss, of course, if we didn’t talk about our complete BI training across the top three BI platforms, Microsoft Power BI, Tableau, and IBM Cognos Analytics. And we offer that training in those form modalities that you see making us ideal for organizations. Running multiple platforms, or those moving from one to another.
And we can provide training in any of those modes, and can mix and match to suit your user community and training needs.
And then last thing, before we get to Q and A ton of different resources, again, on senturus.com, we provide hundreds of free resources on our website, and have been committed to sharing our BI expertise now for over a decade.
And with that, we’re going to jump over to the questions. So, anybody who has questions, please get them into the question pane and we’ll try to get to them, we have a little time here.
First question is what do you do if your partners have competing priorities?
Yeah, that’s a good question, and it’s probably something that each and every person will experience, right. Because we’ve all got very full plates.
I would say that, you know, certainly for an MVP, the point is finding like-minded individuals, right?
And people who have a similar, sort of outlook on the need for governance in the organization. So, I would say, it’s the people that you’re working with have competing priorities. You that’s part of the prioritization process, right? In the planning process, and how large you make, each of your sprints, how much goes into that prioritized backlog at any one time? So, its flexibility, right? Yeah. It being MVP minimum viable process here, you do want it to be as sort of low impact as possible.
At the same time, you need people who will be committed to, to being involved actively, to try to make this successful.
What if senior management doesn’t recognize the issues or understand the importance?
Yeah. You know, I always jokingly say maybe it’s standard to look for another job. But in all seriousness, it’s not unusual for senior management to be insulated from a lot of these issues. Because people in the organization, typically, when they’re dealing with data quality issues or questions about the validity of different metrics, they have workarounds. Everybody has their little workaround, and that’s what I mean by those sheets of paper or post it notes on your desk. You may be, remember to look for this number in report A versus these other metrics in the report Z?
I think the best thing you can do is collect this evidence, right? And that’s sort of the purpose of, the MVP, actually, is to create the case. So, you can take it to mission senior management to truly paint the picture in a way that they understand around the impact on the business, and the cost of doing versus not doing.
Let’s get a good answer with regards to start assessing out that minimum viable process.
How long does a project like that typically take?
Yeah, that really depends on how long that list that prioritized list that you’ve come up with is right. But I would I would recommend that for an MVP, you can really get that done in eight weeks or less. Just if people are not dedicated to it, but working consistently, because really what you’re doing, there’s a few steps here that we talked about, right? None of them should take a lot of time.
If this is truly MVP.
I would say that, you know, in terms of the agile approach, a couple of sprints, right? Maybe two sprints. And you’ve got enough evidence there to make your case.
Or for either for rolling out what you found, and measuring those results, or for moving out to a broader, either making a decision either way, right? Either moving on to a broader type of governance initiative, or deciding that you don’t need to, I mean, that’s, that’s a possible outcome as well.
Yeah, so it doesn’t have to be this all-consuming, internal project, you can take it on in sort of bite size chunks.
That’s exactly with an MVP, is bite sized. That’s a good way to put it.
My question is the same, senior leadership supports data governance, but only because they want their 3 at 3 AM request handler, nine AM, request done by lunchtime.
And when he tells them that it actually means doing a development cycle that takes to day to day.
Or when, I tell them that it actually means doing a development cycle that takes 2 to 8 weeks, to get into production. How do I keep support when their expectations don’t match the time to do it? right?
That’s age-old problem, isn’t it? That’s it. That’s a tough one.
I mean, it’s, again, you know, the whole point of the MVP is collecting quantified evidence.
That you can present to management, too.
Help explains process A and process B, the impacts, the costs, and the outcome.
So, uh, there’s not an easy answer to a question like that, But it’s really, again, the whole point of, of, of doing something like this, is, it’s low impact. And you’re able to collect in a relatively rapid amount of time, at a relatively low cost and low effort for the organization. Some, some good data points to make some decisions on investing in one of these programs.
Yeah, then, being able to document exactly what that process is going to look like or at least frame it up.
To help demonstrate why you need the resources you need to do it right, and that you’re not just sort of patting your numbers or pulling them out of the air.
Absolutely, probably easier said than done in a lot of cases.
Thanks for that question.
Then, once you sort of roll this thing out, how do you ensure that people are following the procedures once you, once you’ve rolled them out?
Sure, I mean, in a full-fledged data governance program, you’re going to have those roles of data steward and data owner kind of consistently in place.
Those roles, manage maintain, you can either do it, you know, simply through Excel, spreadsheets, those sorts of things. I don’t recommend that if you’ve got a large, complex organization. But there are tools out there like Leiber and others that, that help you manage and govern, actually govern the use of data through certification of reports and certification data metrics.
Certainly, publishing and making known, at the very least, the single source of truth for each metric is the best way to facilitate adherence to these.
End of the day, though, it gets back to senior leadership in every part of the organization being part of the program and asking for the govern data points and the certified if you will metrics.
Well, thank you for those questions. Thank you all for your participation. And that’s all the questions that I have here in the question panel here.
So, we can advance to the last slide.
And of course, firstly, I’d like to thank our presenter, Mister Bigenwald here today for a fantastic presentation on making data governance more manageable and bite size. And with that, we’d like to thank you, our audience for taking time out of your busy days to spend an hour with us here to learn a little bit about this topic.
Please do give us a call.
If you still pick up a phone, the 888 number there, we, or you can e-mail us at info@Senturus.com. And again, bookmark our website and check out our upcoming events.
We look forward to hearing from you and seeing you on another one of our Knowledge Series events. Thanks a lot, and have a great rest of your day.