Data thinking

Hi, welcome to the webinar, what is data thinking? And why do I need it in my data strategy? My name is Thomas Maas. I am a principal data consultant in the Eliza practice of agile group. And I’ll talk you through our approach to a data strategy and how you can use data thinking in developing your data strategy.
So let’s start what data thinking actually is. And let’s make it simple. It is a combination of big data and design thinking. So it’s all about using design thinking in the opportunities that big data will bring to companies across the globe.

So let’s start with understanding what a traditional strategy actually is. And when you look at the dictionary, or when you Google, you’ll see that a strategy is a plan of action designed to achieve a long term overall goal. It has this discipline, as a discipline, it has its origins in warfare. And it’s really about choosing the resources that fit the goals you’ve set out and how to compete or stay relevant, depending on whether you’re in a commercial or non for profit environment. Then, if you look at the theory behind the strategy, you’ll be looking at the economic resources that you will make intellect decisions on being labour, capital, technology and lands traditionally.

Now, let’s fast forward to today. And let’s fast forward to a digital environment. So what is then a Theurgy? In a digital business environment, let’s have a look at what a business strategy actually is. It’s really a clear set of plans, actions and goals that outline how a business will compete in a particular market with a particular product or service. And now, when we take a look at the economic resources, I think what digital has changed is that land is not so much an economic resource that we need to be looking at, but actually data and treated as a resource into our business plans.

Let’s take a step back there and ask ourselves the questions. What about customers? And I realised I’m sitting in from my own call out? Sorry for that. But what about customers? How can we make a strategic strategy customer centric, and actually start with the end customer in mind, rather than starting with the economic resources in mind? I’ll I’ll get to that in a second.
So why should we be thinking about a data strategy at all. And so when you need to look at your economic resources, Labour capital technology data that I’ve mentioned before, you will see that all of those have strategies as well, a lot of companies will have people strategy, or people in culture strategy, they’ll have a financial strategy around how to deploy capital the most efficient, they’ll have a technology strategy. And then when we look at the fourth resource there, we will have a data strategy as well. And it’s probably worth mentioning that there really is a difference between a technology strategy and a data strategy. You’ll see businesses still
mentioning data as part of a technology strategy. It’s the technology that actually underpins your data strategy is only focused on technology and not really on the analytics in the use of that data. And that’s really where we think there is a separation between what a technology strategy is and what a data strategy is, as well.

So let’s take a step back and have a look at what’s actually happening overtime in the market and why strategy is changing. So I think this is an interesting infographic to have a look at. And it is about the adoption of certain technologies. And the chart what it shows is how long it took for certain technology to reach 50 million users. So when we look from top to bottom, we will see that airlines took about 68 years before 50 million people had use an airline. And so going down the list, older mobiles 62 years the phone, 50 years electricity, 46 years, credit cards, 28 years, the internet, seven years, Facebook still even three years. And then when we go to Pokemon Go, it only took 19 days. And so here’s this really, really where you’ll see digital come into play and making
adoption and really changing the game around strategy.

With the airlines being probably a much more technology, innovation, but taking 68 years to get 50 million users and Pokemon Go, only taking 90 days to get to that same amount of users.
So let’s have a look at what the actual problem is here. If you want to make a plan, that’s all what a strategy is about making a plan and allocating your resources smartly
that usually that plan in a bit

Since environment will take about a year to develop, and then anywhere between three to five years to actually deliver. And what you will see is that by the time you’re finished you’re planning and especially by the time you finish your delivery, the world around, you will have changed. And so it doesn’t make sense to spend a year planning around what you’re going to be doing for the next five years.
You might set out to think that a Ferrari is the thing that you’ll need to build. And then five years later, you’ll find out that people are not even driving cars anymore. And that’s sort of the the metaphor that I think is very useful when you’re thinking about understanding the strategy landscape and how to plan ahead. So the problem really is that the world is changing too fast to plan five years into the future, or really accurately planning to the future.
Probably the cause of that is the Industrial Revolution. 4.0. And the solution is really where we want to be talking about today is the use of agile and design thinking in strategic processes.
So how do you apply design thinking in business strategy, data strategy, technology, strategy, whatever strategy in a commercial or not for profit environments, you can you can think of.
And this is a framework I really like to use to, to help with the thinking here.
At the start of making our plans, you are at the left of what we call a double diamond, where
you really actually think you might have an understanding what the problem is, but you really don’t know it just yet. And you want to get to actually delivering a solution. So instead of jumping to a solution, what what we would recommend you to do is actually go through a discovery phase first, but not focus discovery on the solution, but focus a discovery on the problem instead. And actually validate the thinking that you set out to start with an output says, What do you think the problem is, discover that problem, inform yourself and then start start diverging with your thinking, and then basically convert your thinking where you say, now I have a better understanding of the problem. I’m going to redefine or restate what the actual problem is, before we then start thinking about the solutions. And again, when you start thinking about the solution, use divergent thinking again, first in designing the solution, which means not necessarily sitting in a room and listening to one person dictating what the solution should look like. But everyone individually thinking about what a solution could be diverging, and then converging again, and basically say, here’s what I think the best solution is and deliver that solution, which usually could be a prototype.

So I can almost hear you thinking thanks for that framework. But what do I really do now. So
again, we’re looking at the four stages here, that you saw on the previous slide, discover, define, design, deliver. And really in a discovery phase is all about divergent. So familiarise yourself with an environment, understand the market, understand the trends, then explore the goals of an organisation, challenge those goals, even if you want
to find what you think is the problem and start brainstorming about solutions. But don’t rule anything out. Don’t select anything just yet be crazy. Think about the the impossible, it’s all on the table at this point. The next bit that you want to do is define and converge. So you want to organise your ideas, you want to actually re select a little bit of those ideas, you want to find some of those solutions that you’ve got in mind, you want to prioritise those solutions, and you want to select the best one.
And obviously, there’s all frameworks,
evaluation frameworks that can help you with that as well. What you want to do is you want to pick the best one.
And then it’s all about refining what you think is the problem or the goal, and broke down how that solution is going to achieve the goal. So this is really where you start connecting, especially in a data environments where how you think a technical solution might actually contribute to a business for
the next bit that you want to do is then design. Because you don’t want to stop with like something on paper, you actually want to start designing straightaway and testing that in the real world. So you want to explore the data, you want to explore its technology and the tooling you’ve got available. You want to identify how it might work. Instead of thinking this is not going to work because of XYZ you actually want to be in the can do part of the worlds in this stage. You want to figure out who can help you and who you need help from to actually deliver this project. And this is where you start branching out and understanding that as a data professional, you are gonna need to rely on other parts of the business to help your product that you might want to be building actually be valuable in the business as well and valuable for users.
You want to understand how to use will adopt your solution. So really think about business requirements gathering in a traditional sense of the world where it’s,
but really want to work together with the user and understand how your data products can help help that person forward. You want to test your assumptions
and solution idea you’ve got in mind. So you don’t necessarily need to build the entire product to test if it’s working yes or no, you actually want to see what your crucial assumptions are what they start the critical factors for success, and you want to test those, and you can be smart about those like you, you might be able to test them in already functioning products and see and maybe take a reference point there and saying, like, look, we’ve gone through a data governance approval process before with a similar solution like this. So the critical assumption that we’re not going to get this approved might be wrong, because I’ve got an example in the market or an example in your company already, where it can work. And then what you want to do is you want to refine your solution based on what you find before you then go into delivery. And this is really where you start building your prototype, building your model. Check all your assumptions. And most critically, an important as well measure success really needs to define how success will look like it’s not just about model accuracy, it’s about driving business value. So how is my model going to be used? How is it going to get adopted, what business value is creating,
and showcase your solution and how to achieve those goals as well. So really making sure in delivery phase of any product that you’re building, or any solution that you’re building, that you bring everyone along your journey to understand how it achieves business success.
Let’s take another meme from the internet and have a talk about what happens if you don’t take this approach. So I really liked his visual, I’m a very visual person.
And this is not just for data, this is for any product development in general is if you don’t align and continuously iterate, and validate your thinking, you will get someone’s expectations completely different from what the end product looks like. So really make sure you iterate you validate throughout all of it.
And you’ll see that an analyst make something completely different from a programmer from a developer bought operations installed,
what the finance team thought was the financial model and sitting underneath, and actually in the end for what a customer needed. And so you’ll see if you look throughout these slides here that
what a customer really needs is actually where you need to get to, so you need to get to that tire that you see in the bottom right corner. And actually the question is, how do you how do you make sure that throughout the process where when you start you don’t know what exactly it looks like? How do you make sure throughout that process that you can get to this point.
And that was it for now. So thank you very much, everyone. I hope this was an enjoyable and informative webinar and make sure you follow this space for more for more content. Thank you very much