In my previous blog, I argued
that few organisations should have full-time “data scientists” on their books.
In this blog, inspired further by comments I received, I explore who the “data
science” functions should be outsourced to.
To simplify things, I will start with 4 options.
Large Management Consultancies
with “Data Science” arm
The first, most obvious, is to go
with the big names; whether you look at the big management consultancies (Bain,
BCG, McKinsey) to which you may add say Accenture or the big accountancy firms
(Deloitte, EY and to a lesser degree PWC and KPMG) are all trying to establish
a practice to gorge of the “data science” pie in the sky. So if you are from a
huge organisation, then chances are, someone likely high up in your
organisation has been approached by one or more of these.
I am definitely not against
management consultancies getting into “data science”. In fact I believe that an
organisation’s “Analytical Maturity” is
key to its ability to make use of data and become “data driven”. Therefore, the
journey is much more than just applying some models/algorithms here and there,
but the ability to consume and exploit them is critical. (In my previous blog
(1), I kind of proxied that by “variety of ‘data science’ projects”).
The question is, can you afford
these, is the RoI (Return on Investment) worth it...
Individual “data scientist”
On the other extreme, you can
choose to pick an individual and either kick-start your analytical journey by
showing quick-wins and good RoI, or start building an analytical culture from
scratch – in which case the individual should be well rounded (it’s more akin
to getting a sort of CDO).
A mid-way solution would be to
engage niche consultancies specialised in “data science”. This is kind of best
of both worlds (or worse); you get a group of people who may have complementary
skills/specialisations, without the extreme overheads of layers of management
and partners.
Technology Luminaries?
How about technology luminaries
such as Cloudera, HortonWorks, MapR, Google, AliCloud... you may ask.
Personally I think technology is a very important component of “data science”
(afterall many algorithms have been available for years but compute capabilities
required weren’t ready), however, technology is a tool. So, while I believe
collaboration with these behemoths is a good way to go to equip your
organisation, they should not be leading the efforts; carts and horses...
Positioning vis-a-vis Data Science Venn Diagram
If you look at the Drew Conway
data science Venn Diagram (2), it becomes easier to see. The diagram below is
inspired by the Drew Conway version, updated if you want.
Large Management or Accounting
consultancies come from the area of Substantive Expertise, and in order to
reach the data science have hired people with IT and Maths/Stats skills to form
“Data Science” capable teams.
Individuals have specific skills,
or a combination and can be coming from any area of the Venn Diagram (although,
given the depth of knowledge and effort required to be in the centre of the
diagram, they are less likely to be there)
Large Technology companies have
an abundance of IT/Hacking Skills, and are picking up Maths/Stats, very often
ignoring the “Substantive Expertise”, relying on “Machine Learning”/”AI”/”Deep
Learning”. While it is debatable whether proponents/practitioners of machine
learning today have enough maths/stats understanding, I think most would agree
than there is a lack of business knowledge.
Some ML/AI/DL practitioners see
this as a benefit, believing that all you need is data; I do not agree.
Small consultancies, like
individuals can be anywhere in the diagram, but mostly are a combination of 2
aspects and most I am aware of are in the Machine Learning space, being staffed
or started by people with strong Computer Science backgrounds. I am not saying
that niche consultancies have no domain knowledge, they may have but either it
comes from the IT side (as someone recently reminded me: working in the IT side
of a bank doesn’t mean you understand how a bank works, but you understand how
the IT in a bank works) or is quite specialised since the number of such
experts is likely to be limited. They are after all niche.
What does this mean to your organisation?
If you are large enough and the
kind of transformation you are willing to undertake is big enough, by all means
engage a large management consultancy with a good “data science”/Analytics
practice.
If your organisation is not that
large, or prefers to spend more conservatively (could be the same amount but stretched
over a longer period), then you could choose to find a niche consultancy and
supplement them with your own substantive expertise (adding burden to your
staff since the person should more or less be embedded with the niche vendor),
the problem is that the talent pool is quite limited.
An alternative would be to
constitute a team from individuals with various skill sets, you may even get an
external expert with substantive expertise. The issue with this is that not
everyone has the knowledge to hire specialists skills, and secondly screening,
choosing people can be quite costly.
I am not sure why any
organisation would look to technology companies to provide “data science”; it
is just not their forte, and you would be better off pairing them with one of
the above options to form a slightly more rounded team, more likely to reach
the centre of the Venn Diagram.
Is there another option?
Well I expect Sesh (hi Sesh!) to
say he knew it was coming, but frankly I didn’t . My blogs are usually written
as I think about something, so they are quite raw (and the diagrams worse...
The only blog that took me a lot of effort to write was the one about Hindu
temple builders being data scientists). When I started thinking about this
question, I genuinely expected to end up with conditions where each of the
“data science” providers would have a place (except the large technology
companies who really can only play an important supporting role, not a driving
role). However ...
The long prescription
If your organisation is really
large and serious about really transforming, then you should look at a large
Management Consultancy with an analytics/”data science” arm. They have the scale and capability to help you
without adding much extra workload on your organisation, compared to cases when
you would have to manage the process if you would engage niche consultancies or
even individuals.
Organisation size is important –
some of the large Management Consultancies would not even consider engaging
with small organisations – but so is the analytical maturity of the
organisation. Large Management Consultancies really come to the fore when there
is a organisational transformation since they can leverage not only the
analytics/”data science” arm, but also the traditional change management and
associated skills where their traditional expertise lies.
If you are not large enough
and/or you are not looking for organisational transformation, then you would be
better off looking elsewhere. That is the whole point of analytics/”data
science”; you run small experiments and keep what is good, chuck what is not.
You do not need an army for that (as I mentioned in my earlier blog).
The advantage of working with
individual “data scientists” is that you get dedicated people who you know or
will get to know and who can fit well with your organisation (else you can
always get someone else). The fit with the organisation can be experience in
the specific subject area, ability to fit culturally with the organisation...
and should not be under estimated.
However, no one individual will
have the breadth of skills that you may need. Of course every data scientist
can be adequate at a whole range of skills and subject areas, but no one can be
a specialist at everything. Furthermore I believe analytics/”data science” is a
team sport, you need more than just one person once you reach a certain level
of maturity, especially when you are operationalising.
Hence niche
consultancies look good.
The main advantage of niche
consultancies is that they have a decent breadth of skill-sets under one roof,
and you may be able to access these skill-sets as and when you need them.
Proper niche “data science” consultancies would at least a team of people
covering the three circles of the Drew Conway diagram.
Looking at a “data science”
project as a flow:
It takes a lot of different skill
sets to have a successful “data science” project. For example solution
architects to design data flows, database experts and data engineers to manage
the data and especially for operationalisation, domain experts to interprete
and craft the data, “data scientists” to build models/algorithms, visualisation
experts to help take action among others... Not that these roles are a full
list, nor that these are all specialised people, nor all are external to an
organisation, but it just gives an idea of what is needed to operationalise
“data science” and the advantage a niche consultancy would have over an
individual, or a group of individuals brought together for a specific project.
However, when you get into
consultancies, you enter the realm of overheads. While an individual has very
little overheads, the larger the niche consultancy the more overheads:
management, sales, adjustments for time on the bench, office space,
administration expenses... Furthermore you may lose the fact that individual
consultants, free from official partnerships/administrative work... are able to
learn and grow in their areas of interest, as opposed to what the consultancy
requires/prefers.
Furthermore, niche consultancies
are niche because of some specialisation, be it by industry verticals or by
function, or by a combination. Therefore
you need to find the right partner. One of the factors you may lose out is that
one emerging trend is “data science” is cross-pollination where ideas and
techniques from a different field/industry is modified and used.
So what is the solution?
The solution is to find networks
of people who work together, of course preferably with an emphasis on analytics/”data
science”. There are quite a few organisations that offer this “new” approach to
analytics/”data science” and a few variations. My views are based on the ones I
am familiar with.
Such a analytics/”data science”
focused network basically combines the advantages of individual people with
that of niche consultancies adding the potential for cross-pollination, without
the disadvantages such as high overheads.
An organisation is free to choose
the right person while that person benefits from being part of a network for
learning and support, or a group of people with complementary skill sets to
deliver an analytics/”data science” project without the huge overheads. Furthermore,
all members have access to common resources and a support group of fellow
experts just like larger formally organised consultancies.
What do the individual
consultants get from joining such a network? Simple, the opportunity of working
on projects they did not uncover themselves, of working on bigger projects than
they could have by themselves, of learning from peers with similar mindset
whether via discussions, training, or participating in projects in different
roles.
Looks like I did end up with some
simple rules of thumb...
In sum:
- If your organisation is large and serious about transformation, go for a large management consultancy with a “data science” practice, run a comprehensive transformation programme.
- If you have very specific needs and know an individual or a niche consultancy with reasonable overheads that exactly suits your these needs, then go for either of these options bearing in mind where you will need to manage/supplement.
- For other cases, go for a network of analytics/”data science” experts that incorporates the advantages of the two options above without the disadvantages.
- In general, use large technology vendors as providing technology rather than “data science” services, horses for courses.
So what does this mean for independent “data scientists” and niche
consultancies?
Join an analytics/”data science”
focused Network! A large portion of demand generation is via networks anyway,
hence networking is not new to independent “data scientists” and niche
consultancies. But it is an advantage to join more formal networks and get the
benefits from there.
Individuals would benefit from
joining networks by broadening their knowledge and gaining the ability of
participating in larger projects. Note this does not have to mean loss of
independence, in fact as long as there are no fees or other commitments; there
is no downside for an individual to join a network.
Niche consultancies would still
have high overheads, that’s because of their structure, but they would at least
gain the ability to broaden the scope of projects they could take up by
collaborating with other members of the network, allowing to continue
specialisation while broadening the scope of projects that they can embark on.
Eventually, the choice of which
network to join will be the critical one. The right network has to bring value
to the individual or the niche consultancy. Value can be measured in many ways,
and not all are purely monetary. As Doc argued (3), the values of the leader (and of
the network) are critical.
P.S.
Personally I believe the labour
market is changing so much that we will soon be back to the times where most of
us would independently be selling our skills rather than being “full time
employed” especially with benefits; back to middle ages/very early
industrialisation.
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