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Defining and Scoping a Data
Science Strategy
To understand the constituent parts of a data science strategy as well the strate-
gy’s current and future significance, it’s worthwhile to look at some of the major
components on a high level. I then address each of these different parts in detail
throughout this book. But before that I need to make a short clarification of the
difference between a data science strategy and a data strategy.
On a high-level a data science strategy refers to the strategy you define with regards
to the entire data science investment in our company. It includes areas such as
overall data science objectives and strategic choices, regulatory strategies, data
need, competences and skillsets, data architecture, as well as how to measure the
outcome. The data strategy on the other hand, constitutes a subset of the data sci-
ence strategy, and is focused on outlining the strategic direction directly related to
the data. This includes areas such as data scope, data consent, legal, regulatory and
ethical considerations, storage collection frequency, data storage retention periods,
data management process and principles, and last, but not least; data governance.
Both strategies are needed in order to succeed with your data science investment
and should complement each other in order to work. The details of how to define
the data strategy is captured in part 2 of this book.
Objectives
If I ask about the objectives of a data science strategy, I’m asking whether there
are clear company objectives set and agreed on for any of the investments made in
data science. Are the objectives formulated in a way that makes them possible to
execute and measure success by? If not, then the objectives need to be reformu-
lated; this is a critically important starting point that must be completed properly
in order to succeed down the line.
Data science is a new field that holds amazing opportunities for companies to
drive a fundamental transformation, but it is complex and often not fully under-
stood by top management. You should consider whether the executive team’s
understanding of data science is sufficient to set the right targets or whether they
need to be educated and then guided in setting their target.
Whether you’re a manager or an employee in a small or large company, if you
want your company to succeed with its data science investment, don’t sit and
hope that the leadership of your company will understand what needs to be done.
If you’re knowledgeable in the area, make your voice heard or, if you aren’t, don’t
hesitate to accept help from those who have experience in the field.
26
PART 1 Optimizing Your Data Science InvestmentIf you decide to bring in external experts to assist you in your data science
strategizing, be sure to read up on the area yourself first, so that you can judge
the relevance of their recommendations for your business — the place where you
are the expert.
Approach
Taking the right initial approach is a fundamental part of your data science
strategy — it will determine whether your company takes the appropriate imple-
mentation and transformation approach for the data science investment. For
example, is the approach ambitious enough — or is it too ambitious, considering
time estimates related to available competence? Is there a clear business strategy
and expected value that the data science strategy can relate to? Taking the time to
think through the approach is sure to pay off because, if you don’t know where
you’re going, you are most unlikely to end up there.
Choices
The term choices here refers to the strategic choices necessary to drive the data
science transformation forward.
The strategy you create cannot be about doing everything. It’s equally important
to make strategic choices about what to do as it is to make decisions about what
not to do.
Decisions can also be distributed differently over time, because the choices can be
about starting with a particular business area or set of customers, learning from
that experience, and then continuing to include other areas or customers. The
same strategy applies to choices of data categories or types to focus on early rather
than later on as the company matures and capabilities expand.
Data
Defining a data strategy is a cornerstone of the data science strategy — it includes
all aspects related to the data, such as whether or not you understand the various
types of data you need to access in order to achieve your business objectives. Is the
data available? How will you approach data management and data storage? Have
you set priorities on the data? Have you identified and set data quality targets?
Another important aspect of data relates to data governance and security. Data
will be one of your most valuable assets going forward; how you treat it is funda-
mental to your company’s success.
CHAPTER 1 Framing Data Science Strategy
27Legal
Understanding the legal implications for the data you need in terms of access
rights, ownership, and usage models is vital. If you aren’t on top of this aspect
early on, you might find yourself in a situation where you cannot get hold of the
data you need for your business without breaking the law, or, even if you can get
hold of the data, you may realize that you cannot use it in the way you need in
order to fulfil your business objectives.
Laws and regulations related to data privacy stretch further than many people
think, and they keep changing in order to protect people’s data integrity. This is
good from a privacy perspective, but doesn’t always work well with data innova-
tion. Therefore, as a good investment, you should always stay informed about
laws and regulations related to the data needed for your business.
Ethics
Ethics, an area of growing importance, refers to the creation of clear ethical guide-
lines for how data science is approached in the company. Internally, this term
refers to securing a responsible approach to data usage and management when it
comes to preserving the data privacy of your customers or other stakeholders. One
way of protecting privacy is through anonymizing personal information in the
data sets.
Externally, insisting on the ethics of data science is vital when it comes to gaining
your customers’ trust in how you handle data. When machine learning or artificial
intelligence is introduced — especially when automation of decisions and preven-
tive actions are involved — it touches on another ethical perspective: the “explain-
ability” of algorithms. It refers to the idea that it must be possible to explain a
decision or action taken by a machine. Machine learning or artificial intelligence
cannot become an automated, black box execution by a machine. Humans must
stay in control to secure the transparency of AI algorithms and ensure that ethical
boundaries are kept.
Competence
Based on the objectives that are set, choices that are made, and approach that is
chosen, you must ensure that you put the right competence in place to execute on
your targets. Putting together an experienced and competent data science team is
easier said than done. Why is that? Well, you really need three main categories of
competencies, and the availability of experienced data scientists in the market is
now very low, simply because few data scientists have the sufficient experience
and because the demand for these types of competencies is very high.
28
PART 1 Optimizing Your Data Science InvestmentYou can’t get by with simply hiring only data scientists. Data engineers with a
genuine understanding of the data in focus is fundamental. Without good data
management, data scientists cannot perform their algorithmic magic. It’s as
simple as that.
Finally, you need to secure domain expertise for the targeted area, whether it’s a
vast business understanding or an exceptional operational understanding. It’s
absolutely crucial to have the domain experts working closely with the data
engineers and the data scientists to achieve productive data science teams in your
organization.
Infrastructure
When talking about infrastructure, it’s all about understanding what is needed in
terms of data architecture and applications in order to enable a productive and
innovative environment for your data science teams. It includes considering both
a development environment (a workspace where you innovate, develop, train, and
test new capabilities) and a production environment (a runtime environment
where you deploy and run your solutions).
Infrastructure includes all aspects, from how you’ll set up your data collection/
data ingest, anonymization, data storage, data management, and application layer
with tools for the analytics and ML/AI development and production environment.
It is impossible to identify and set up the perfect environment, especially because
the technology evolution in this area is moving very fast. However, a vital part of
the infrastructure setup is to avoid getting locked into a situation where you
become entirely dependent on a certain infrastructure vendor (hardware, software,
or cloud, for example). I don’t mean that you should only go for open source prod-
ucts, but I do mean that you have to think carefully which building blocks you’re
using and then make sure that they’re exchangeable in the long run, if needed.
Governance and security
Working actively with data governance and security will make sure you stay in
control of data usage at all times. It isn’t important only in terms of gaining your
customers’ trust, but it is in many cases also a necessity for following the law.
Keeping track of which data is collected, stored, and used for which use cases is a
minimum requirement for most types of data.
Overworking the area of governance and security will have an impact on your data
science productivity and innovation. A common mistake is to be overprotective
with regard to data usage, keeping all data locked in to a degree that nobody can
access what they need in order to do their job. Therefore, you should approach the
CHAPTER 1 Framing Data Science Strategy
29setup of data governance and security with a mindset of openness when it comes
to sharing data amongst employees within the organization. Lock the gates to
outsiders, but strive for an open-data approach internally, boosting collaboration,
reuse, and innovation.
Commercial/business models
As part of your company’s data science strategy, you need to consider whether you
only want to focus your efforts internally as a means of improving operational
efficiency or whether you have ambitions to utilize data science to improve your
commercial business models. Improving your business using data science will
absolutely expand your possibilities, both in improving current business as well as
helping you find new opportunities.
Tread carefully when commercializing data. If you haven’t transformed internally
first by implementing data-driven operations, you’ll likely be unable to fully
leverage a data science approach externally in the business perspective.
That doesn’t mean you need to implement and run data-driven operations
throughout the company, but such operations will be needed for the areas con-
nected to the new data-science-based business models and commercial offerings
you’re aiming to realize.
Measurements
Without measuring your success, how will you ever know whether you have
actually achieved your objectives? Or be able to prove that. Still, many companies
fail to think of measurements early on.
Measurements are needed not only from an internal operational efficiency
perspective but also to measure whether you have managed to deliver on the
promises made to customers. This is important regardless of whether the agreed-
on customer targets have been contracted or not. It should always be a priority for
you to know how your business is performing against your objectives. The feed-
back will give you all the information you need to determine where the business
stands, what needs to improve, and what has perhaps already been achieved.
Yes, establishing measurements early on is fundamental when it comes to secur-
ing continuous learning in your company, but it also shows customers that you
care about reaching your targets. However, don’t forget to think through the met-
rics structure you plan to use. It isn’t an easy task to identify and define the correct
set of metrics from the start. This is also something that needs to be reevaluated
over time, based on which measurements actually give you the insights and
feedback needed on what is going well — and what isn’t going so well.

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Onnowpurbo: Created page with "Defining and Scoping a Data Science Strategy To understand the constituent parts of a data science strategy as well the strate- gy’s current and future significance, it’s..."