April 9, 2020
4 experts talk data governance, data driven business & analytical mindset

4 experts talk data governance, data driven business & analytical mindset


To understand data, where does is it come
from, how trustworthy is it. We want to be data driven, but we want the
data to say this. Getting people to understand and to believe
in data that adds value. Governance without return is just a cost. Data governance is the technology and the methodology and the processes of making sure
that the data that you collect, manage and integrate is high-quality. It’s one of the prerequisites for anything you do in data. It’s one of the foundations
that most people just don’t see or don’t want to do because it’s cumbersome. It’s
the underlying rules, processes and methodologies on how to treat data, how to tackle access,
how to tackle ownership for data sources for example. How to tackle ownership for business
intelligence system or marketing intelligence system, ownership for ETL for example et cetera,
et cetera. That’s a big part of it but I think the term itself is quite fluid and quite
big. And everybody understands something different. For me, as dumb as it sounds, sometimes data
governance is about the definition of governance. Data governance is really two different things.
Right, because one thing ist the traditional thing about data quality and actually making
sure that the quality of data is something that is legally within limits, right? So it’s
the GDPR thing making sure that you are legal in your set up when you have the right different
aspects of things. But where we find data governance has something interesting is also
tabbing into the next level of it and actually saying: We are now in a place where data can
do stuff on its own. So, making sure that if data does stuff on its own, I need to trust
the data I have. And very often we are in a situation that data might be legal but worthless
and then they damage only semi-governance. In the sense that I have it and it is compliant
with the legal side of things but I cannot use it to create any value for the organization.
And then it is just a cost. Data governance is collecting, using, applying
data in a way that is appropriate to conform with best practices in law and what the intent
of your organization is. Well, what you organizational intent is for how the data is to be used.
So, you want to use the data properly, legally, accurately and transparently. I think It’s structure. In order to make data governance work in a
corporation you need some sort of technology, it does not really matter what. But mostly
you need solid processes that everbody in the company agrees to. So you have to have
that change management process of getting people to buy into the value, so they are
willing to execute on the process. At the core of a good data governance is top-down.
Where you need to buy in from leadership so that all the practices with the new organizational
tide to appropriate data governance and compliance. Because one of the problems I have seen in
the organizations when they try to skirt, get around, avoid the responsibilities if
that is coming from the top. Then parts of the organization will find ways to avoid doing the things they should do. So it really needs to start with the top and having the culture of the organization aligned with proper data governance. Basically this should be at the core what the company owns. But you can have somebody external
who helps the structuring, who helps with best practices, wo helps with the framework maybe even on how to tackle the various parts of data governance that you should look into.
Also the parts that people usually don’t want to look into like data security for example,
cybersecurity. If a company is looking at outsourcing some
of their data governance efforts the technology can be brought in from wherever. You do not
have to build your own. And you could bring in a consulting company to do a data governance
audit to find out where you are and where do like to get. But actually executing on
the creation of a data governance program is again, it’s corporate change management. So it
requires people inside the company getting people to agree and that is not going to happen
from the outside. I think, the difference is that classic companies
simply rely on gut feeling and on hypotheses that are not validated by data. And data driven
companies do the same thing. They form their hypotheses. They have a great gut feeling in many cases. But where possible they add a layer of data to confirm, to make it faster.
That is on the decision-making side. On the automation side, I mean, there is so much
that companies can do in terms of the data driven automation. It doesn’t always have
to be a great Tableau Dashboard of Blablabla to make a better decision. Sometimes it can
be just like a stupid process that can be done by computer and good data driven companies
are just like, yes, why don’t we just automate the hell out of it and then ged rid of it
on the human side. And now you take that human labor and add human intelligence to the processes. It is the ability to act on the data, right? Because you can actually say there
are a lot of companies that go in and spend a lot of resources on gathering data but do not do anything with it. And that is not data driven, that is just data hording. The drive is making things happen based on the data. So, the key thing is probably that data is
respected as a source of activation. That there is a reason why we have the data. The
data will help us to get inside and make decisions. The first thing they need to do is, they need
to have the why on place, right? So they need to understand why they are doing analytics
and data in the first place. Because it is really easy to do a lot of data gathering
but if you don’t have a direction then data is pointless. And you can spend too much time
on governance and too little time on business. And very often that is what we see, that data
gathering becomes the key focus and not the value creation from the data. It is in the value creation that we justify analytics. So I have been talking a long time about a term called return on analytics which a lot of people don’t like because it actually means that
analytics is an investment. But I think it really is important to focus on that part
because governance without return is just a cost. To be data driven a company has to understand the value of data from the top down. So we
have gone thousands of years of making decisions without data but we have made decisions based on experience. That is never going to stop. We do not want a company that makes decisions
only on data. We want them to have their heart and soul in it and to be data informed and
perhaps insight driven. But insights come from inside based on the data. So being data
driven? But being data informed, being insight driven. That means everybody
really understands that data is an important piece of the decision making process. I would say in a lot of cases now with the speed of data, data can do things on its own. So, I actually used to picture a lot saying that in the old days we used to talk about
data being a really tight thing, would be like a symphony orchestra trying to work together
and everybody has their specific roles. But what we found is from our perspective that
the world is way too complicated for that. There is too much stuff going on. So very
often it is much more like a jazz band. People have to have room to kind of play instruments
together, but we have to support each other. Much more freestyle, there we have to make
room for solos and we have to make each other sound good. So, it’s not about one conductor
standing making sure everybody play their notes, but it is making sweet music together. What we’ve done is, we’ve taken steps, small tiny steps towards it, where we saw that this actually works very well. All the organizations want to be data driven
that I see today. But one of the interesting parts of a data driven mindset is that there
is sometimes with some of our clients. They say they want be data driven, but then when
you show them what the data says they say: But that’s not really what we’re interested
in. What where interested in is: We want to be data driven, but we want the data to say
this. And part of a true data driven organization is to contextualize the data, understand the
data, use the data where the data leads you. But one caution: That does not mean, that
the data is always true or correct. And so just because the data says something that
does not mean that this is true. In the sense we want to understand the processes data can
be collected incorrectly, incompletely. There can be biases in the data. So, data driven
does mean you follow where the data lead you but it mostly tells you too ask why. Why is
the data looking like it looks? So you can uncover what the truth and reality of what
your customers or whatever you daily collecting really is about. I think, this is one of the hardest questions of all. Yes, it is necessary for employees to have an analytical mindset and that’s gonna require data literacy. It is not about mathematics, which we had trouble with on school because we had terrible math teachers. So I am not good at math, I hate math. That is fine. But to understand data, where does it come from, how trustworthy is it, how difficult is it? That is important
data literacy. Then that‘s just training that is getting people used to the idea. So
that they then understand the value. So that they can apply it and become an insight driven organization. For organizations where there are many people are driven by a gut feel and they’re hesitant to adopt a data driven mindset or analytical mindset,
I think, small wins are very important. So, projects where analytics are used to help inform decisions, that are not, as we say in the US, not hitting home runs, but you
are hitting singles. You are doing small things and small victories. Because the ideal analytics solutions to me tell you 80% obvious, 20% a little head scratching und people go: Huh! I never thought of it this way. And then after a little thought they go: Yes, but that makes
sense. So those kinds of hidden insights and gems should come up but they should not be
the number one driver. If that is happening in a dominant way, people won’t believe it.
They need the analytics to say the obvious things first and then add a second and third
level coming up with those insights they have never thought of before. Getting people to understand and to believe in data that adds value to them. And one of
the first things, it sounds super stupid and very simple actually, is what we called data
meetings. So every meeting that you have that is more important you add data to the first
ten minutes. So in sort of diving right into the discussion you get everyone on the same page by just saying, hey, let’s look at the data first. We look at the facts, not the opinions. And we have an analyst in the room that if a hypothesis within the meeting that comes
up that can be looked into right away. And that usually leads to two things: First, people’s
analytical mindset or analytical skills are improved. And it makes them want to be more
analytical. And the other one – and this is one of the fantastic ones that also then puts
number one into a much better perspective – is you actual get much more decisions, actions done, within this meeting because you can take action right away. And that usually leads to a higher satisfaction with that meeting. You actual have an outcome of that meeting and
people are like: Hey, data led to that. And if I use this, I get from talking to action much quicker and actually get things done. The key thing of the analytics mindset is
actually the conclusion and going for the insights. Because when you would just analyze
for analyzing’s sake then we are not moving anything. We need to have this, it is not
a nice term, but saying profit driven approach as well. Saying, why do we have the data?
We need the data to be able to move the business because the business pays our salary. So,
at the end of the day it is about the profit and not necessary just about the data. Web analyst is a horrible name because it is very passive. It does not carry any responsibility.
So, what we should do instead of calling yourself web analyst we should be data driven business
developers. That is kind of where the future is and that is where we create value and kind
of accept our role in the organization. We do not just do reports. We build organizations and create value.

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