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BRUNO AZIZA: Welcome to another episode of "Data Journeys."
This is the place we come to learn
from data leaders who've experienced amazing growth,
amazing momentum.
Today, I have a special guest, Lucas from BCG.
BCG stands for Boston Consulting Group.
They work with hundreds of organizations
to learn best practices.
And today, Luke is going to share
best practices around data strategy and data
governance with us.
Lucas, welcome to this show.
Tell us a little bit about yourself and who BCG is.
LUCAS QUARTA: Thank you very much.
Indeed I am from BCG.
And I'm a partner and associate director
there from the Paris office.
I'm working only on data analytics topics
indeed and working with all my clients
to define their data strategy to find the best use cases
and to put in place all the enablers' data governance,
advanced analytics, data science team and architecture in order
to deploy this use case at scale and to get great impact.
BRUNO AZIZA: That's excellent.
So let's start and dive right into data strategy.
Why does a company need to have a data strategy to start with?
LUCAS QUARTA: Yeah, good question.
So first perhaps simply because without a good strategy,
it's difficult to achieve, ambitious goals, right?
So since 2016 at BCG, we are sharing the market ambition
and the ability to leverage data.
We ask companies to assess themselves
along seven key capabilities needed to conduct a data
transformation.
We have almost 600 companies in our survey covering all
industries and all geographies.
We performed already the survey twice in 2016 and 2019.
And we are currently running the new version of it, actually.
So we ask the companies to assess their data capabilities
at the moment they take the survey,
and to express their ambition for the next three years.
We are then able to compare the ambition they expressed
at the beginning of the given period with what they actually
achieved at the end.
And we have also a good view on how
they have corrected their ambitions for the next period.
So globally, companies are making progress
in their effort to mature their data capabilities.
But the transformation to data-driven organization
is proving to be arduous for many of them.
And it's not happening as rapidly as they
would have hoped actually.
So while they initially hoped to raise their Data Maturity Index
Score by more than 50% in 2019, they
fell short of their ambition, improving their score
by only a small 20%.
And during that period, actually, only 10% of companies
do reach their ambition.
And we noticed that all of them have a strong and clear data
strategy.
And this one, convinced by their success, so these data
analytics leaders, are continuing to invest even more.
And they will continue to improve, and most probably
by 10% during the next three years.
So at the same time, of course, the risk
for the others, widening the gap is real.
I mean, developing a [INAUDIBLE] company already
need to make a jump of respectively of almost 30%
to 60% to stay close to the data champions.
And these figures were assessed even before the COVID crisis.
As I told you before, we are running the new survey,
job of Q1 this year.
The trends are similar.
And actually, it would be my pleasure, Bruno,
to have another chat with you to share these results.
BRUNO AZIZA: Absolutely.
That's really interesting.
So you said only 10% of companies
reach their data ambition.
What's happening there?
What are the common pitfalls that you're able to observe?
LUCAS QUARTA: There are some common pitfalls indeed
we encounter regularly at our clients.
And actually, many times, they face many, many of them.
I mean, it can be because of an unclear value proposition
or because only aiming at some incremental steps
instead of considering a blank slate usually.
It's often because of limited creativity around monetization
opportunities, an inability to identify the real advanced data
sets and the related high-impact business outcomes or use
cases as we are used to seeing.
Not surprisingly, it can be also because of a lack
of foundational architecture and absence of modularity
that leads to a situation where the data is really stuck
into the organizational silos.
Very often, they miss the implementation
of an effective data governance to activate and to scale
the business outcomes.
And last but not least, actually, we almost always
notice a very limited adoption from top and lower-level
employees.
Actually, if you really want to scale your data initiatives,
in fact, you need to manage the necessary changes
in the ways of working, the ways of operating your business,
and the ways of taking decisions.
BRUNO AZIZA: So we've talked a little bit about what
gets in the way, the pitfalls.
Now let's flip down to how do you successfully
build to this strategy?
What are the building blocks to building a successful data
strategy?
LUCAS QUARTA: That's my bread and butter here.
We believe at BCG that there are five key elements to building
a robust data strategy.
And while certain elements may need higher focus depending
on the starting situation or the ambition,
it's critical for the strategy to cover these five elements.
So our data strategy approach starts
with the clear articulation of the why and of the what.
So the why, it's easy.
It's the expression of a bold vision of the business outcomes
you want to achieve and the selection of the value
levers creating your sustainable and competitive advantage.
So easy to say, but perhaps more difficult to define.
So the what is the identification, the sourcing,
and the monetization of the advantage data
set creating that competitive advantage.
So the advantage data set are these 20%
of internal and external data sets
that are making 80% of the impact you are looking for.
And actually, over the last five years, a lot of attention
has being given to analytics, AI, machine learning
with companies across the board trying
to build these capabilities.
And as these are getting more widespread,
the ability to gain competitive advantage only
from algorithms and from basic data sets will diminish.
So companies should really wake up to the fact
that the data itself is the foundation for everything else.
So the true differentiation will stem from the ability
to identify, build, acquire, and monetize advantaged data
sets at critical mass.
So with this why and that what clearly articulated,
the next step is, of course, to define the how.
So we will teach you about all the enablers
to be mastered to implement and deploy the data
transformation at scale.
So I have already stressed the importance
to take care of the culture, the change management, and things
like that, and top management, and on the workforce level.
Of course, you also need to reflect
on what are the AI and advanced analytics capabilities required
to generate the given insights and to deploy your use case
solutions.
So you also need to reflect on how to organize your data
science teams, the way to let them collaborate
with the business and with the IT teams
to be efficient and agile, and then
to have a good time to market.
BRUNO AZIZA: So when you're talking about business and IT,
I know you place a premium on data governance
and building a data and digital platform at the heart
of the data strategy.
I've read you write about that.
Tell me more about what's entailed here.
LUCAS QUARTA: Data governance and actually
what we call data digital platforms are key pillars,
in a sense, to build a data fabric
or a data factory that you need to put
in place to industrialize your data-driven use cases.
Actually, without these two concepts,
no hope to get some impact at scale, so no
hope to reach big ambition with data and AI.
So let's start with data governance.
I mean, when you have identified your advanced data sets,
you need to be able to integrate them, to aggregate them,
to manage their quality.
So you need to govern your data.
And actually, it's well beyond administrative practices.
It's about ensuring the right information
to the right people at the right moment
with the required level of quality.
And actually to define theoretically, to be honest,
it's easy.
There are good books on that topic.
So you even don't need to be [INAUDIBLE] for that.
But how to define it in a way it fits your company
governance, your ways of working,
and to implement it in a way that
creates tangible and sustainable impacts, that is much harder.
And actually in the Data Capabilities Maturity Survey
I mentioned before, more than 60% of the respondents
rated their data governance capabilities at various level
of underdevelopment.
Actually for many companies, data governance
is the business equivalent of flossing.
They know it's good for them, but they would rather
do something or maybe anything else.
So when, of course, some good foundation
are requested to start, the key for data governance
implementation is a show don't tell approach.
So please, start to deploy very concretely
your business-driven data governance
on the perimeter of your most valuable use cases,
covering your advantage data sets to show,
to really show that data quality matters.
That is your key enabler to create the expected value.
I mean, even data governance can be deployed here
in a use case-driven approach.
And it's a great way to bring the change
into the organization.
And I could speak about that for hours.
But let me give you a word about what
we call a BCG, the data and digital platform
approach, the so-called DDP.
So actually, DDP starts from the strong conviction
that technology does not need to be
the bottleneck of your data and digital transformation.
So you actually need to decouple the data and digital business
transformation from the core IT transformation,
ideally to allow them to move at different speeds--
I mean, to create new stuff, data and digital stuff
fast, while getting time to upgrade the core system
at the right pace.
But to do that, you need another approach
than the conventional IT one.
I mean, with the conventional IT approach,
it's based on large programs taking many years
for replatforming, complex technology replacement.
And we all know that it goes along
with a lot of associated risk.
So actually here the key is to liberate the data.
I mean, you have to unlock them from the silos
where they sit into the legacy.
So fundamental to this setup is a DDP reference architecture.
So in a nutshell, in this architecture,
you create a data layer to separate the data
from the core transactional system, like ERP and CRM.
You create more modular interfaces between systems.
You adopt cloud infrastructure for speed and agility.
And it also creates a digital content
that is more adaptable so that you
can evolve with new customers, suppliers,
or employee and employee needs.
And then powered by AI and leveraging open source software
and cloud services, DDP then combines
internal and external data in a new way,
provide the data as a service to the omnichannel smart business
layer.
And this creates the speed to deliver
new data and digital services to the front lines,
I'd say, every few weeks instead of every 12 to 18 months.
And this is, of course not easy.
But if you do it, it pays off.
When you achieve it, in our experience with our clients
across industries, it allows to deliver
twice the value for half of the price and twice faster
than with the classical method.
BRUNO AZIZA: Lucas, thank you so much for what
you shared with us today.
I learned about the show don't tell,
I learned about the Data Capability Maturity
Survey, and then, of course, how to approach data governance
at scale.
I hope people are going to follow you, and read
your research, and reach out to you to get more insight.
In fact, if they want to find out more,
we've got all the links to your references down here.
I hope you can follow this show as well.
Until next time, I'm Bruno Aziza.
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