So ok, people are getting visibly more selfish
and extreme in their views, so what?
In my previous blog (1) I used some numbers
around the elections of Mr Trump and Brexit, and showed that the data was
clearly pointing towards Mr Trump winning because of white people, and Brexit happening
thanks to older people. I also explained why it is likely that we will see more
and more of extreme views being taken and exhibited by people. In this blog, I
consider what, as analytics people, we should expect and how we could deal with
this new reality.
Data Driven is at risk
People in the analytics field have waited for
the aim of becoming data-driven for a long time. Using data to help
organisations make the ‘right’/’optimal’ decisions is what we do.
For many years we were confined to very
specific business units such as marketing/customer management – where we
increased product take up, decreased churn, increased the value of customers to
the organization while decreasing the costs, risk – where we help manage risk
and find optimal points to balance risk and return, red flagging potential
issues before they happen so that pre-emptive action can be taken, or as they
are taken to help relatively objectively re-evaluate cases, operations – where
we increase efficiency by making processes smoother, decreasing TAT, optimizing
resources and resource utilization, human resource management – where we help
select suitable candidates, reduce employee churn, flag potential issues ahead
of time and so on.
Finally, with all the buzz around “data science”,
ML. AI, DL, the concept of using data to make decisions became mainstream, and
the idea of expanding the use of analytics to the organization as a whole
started taking hold. So we has Chief Data Officers, Chief Analytics Officers…
with associated teams who actually walk the talk, including specialized analytics
consultancies such as experfy(2) and AlphaZetta (3). These organisations
provide expert support in very specialized roles across the analytics spectrum,
although less so on the dev-ops side where the market is very well stocked.
In fact, this emerging gap in the market caused
management consultancies to try and create, or buy their own analytics shops,
some more interestingly (4) than others (5).
But this new trend of visible extreme runs
counter to data driven.
I mentioned the gap in the market above; not
only have specialized organisations sprung up to address this gap using human
experts, but some organisations, taking advantage of the lack of suitably
qualified humans have gone the systems way and codified specific procedures in
to generic solutions that may be applied in different cases, for example data
robot (6). To me this just makes it easier to pick the answer you like; many
algorithms are thrown at the data and a user can get to pick the one that suits
them. I am not saying that a human wouldn’t do this – in fact running
algorithms is probably the easiest part of the analytics process – but automated
systems allow this to be done easier. It’s not the technology, it’s how it
is used (and by who).
The second coming of
the HiPPOs
In this murky environment, the HiPPOs are
making a return. HiPPOs are decisions made by going along with the Highest Paid Person’s Opinion. Despite the hype around “data
scientists” remuneration, usually they are not the HiPPOs.
Instead of being driven by data which means
that in different circumstances, different positions are optimal and should, in
true data-driven fashion, be taken, extreme and inflexible decision makers
look for evidence that suits them.
In an environment where it is becoming more
acceptable to express and push extreme opinions, where if you really want you
can find some interpretation of data that suits your views – usually in the
websites/newsfeeds you go regularly to/are recommended to you. After all, we
are living in the world of “fake news”(7) and “deep fakes”(8). The fact is that
very few people or organisations have the skills and resources to question the
data, and in such an environment, it becomes very easy to drive a personal
agenda.
A little knowledge
Another related issue is that everyone thinks
they are experts, a little knowledge is bad (9). I once was in a meeting when
someone said: “It’s easy to become a ‘data scientist’, you can just take a
couple of hours course online! I’ve done it myself!”
I think online courses are great. Democratisation
of knowledge is good because it allows for healthy informed debates, and
eventually less bias/ However, there are all sorts of courses online, and it is
very easy to spend a lot of time, effort and money on courses that are not that
useful.
Relying on wisdom of the crowds (rating
systems) is not necessarily the right thing, especially for introductory
courses which create the foundation upon which further knowledge will be added,
simply because the majority of people giving ratings would themselves be
newbies, hence the ratings would likely reflect how the learning took place
rather than advised opinions on the quality of the learning. There has been
some effort to build curate curricula out of the publicly available courses,
but not everyone can benefit from them.
Correlation
Co-Co-your-head
How many of us have heard the phrase “correlation
does not mean causation” in the office or even at social events? Or even worse:
how many of us have heard people use the word “correlation’ in everyday
conversation? Especially when people use “correlation” to mean a relationship?
(The most common measure of correlation in fact, only describes/measures linear
relationships(10)).
Hence paradoxically, while more people are believing
that being data driven is a good thing, we are getting less data-drive because:
1 It is getting easier to find “supporting
evidence” by choosing algorithms after the fact based on their results. The
emergence of ‘automated’ algorithm testing software plays a big part in that.
2 The proliferation of “data science” courses
combined with the lack of “quality”/”suitability” checks gives the impression
that “data science” is all about a couple of catchy algorithms let loose on the
data, any data.
3 “Fake news” is becoming more and more common;
in some cases, people repost and give credence to stuff without doing research
into whether it is true. Not checking is not new. For example, it took 3 years
and countless applications to realise that evidence in favour of feeding
patients 80% oxygen after operations was flawed, and so was the WHO’s advice
based on these paper (11)
So what can analytics
people who choose to walk the talk do about this?
Unless you are given
the mandate and the power, don’t even think of changing organisations
Most of us end up working for organisations,
whether we are actually freelancers or contractors or employees. Depending on
the level or role we are engaged in, there are cases when a nice carrot of being
given a chance of making an organization become truly data-centric, or at least
taking part in an effort to make an organization truly data-centric. Here are a
few things to look out for.
1 Industry
The industry you are in makes a huge
difference. It is arguable that the degree of exposure to digitization in an
industry is a good gauge how likely more people in management would be on board
the data-driven train.
A group of people from McKinsey wrote a very
interesting article in HBR (12) that uses data and provides possible explanations
for the findings. I have reproduced the diagram at the heart of their paper
below:
One of the interesting things about such
surveys is that the financial services industry is one of the most digitized. I
think it is necessary to split banking and insurance.
I have worked in both banking and insurance,
and from my experience, insurance has far more dinosaurs alive than banking
does. For example, I have met a CIO/CTO of an insurance company who was
convinced that the best way to deal with migration of dirty data is to hire
students as temps and re-key in the whole database (and I am not talking about
only hundreds of records).
But this also means that the rewards for
becoming data-driven in the insurance space are huge. And there are start-ups
that are trying to bring modernity to insurance. However, there is a long way
to go.
In sum, you can use the study by McKinsey as a
guide but remember, insurance is much lower down the order.
From a business perspective, it is much easier
to ‘sell’ analytics in an industry where it is already quite accepted; in fact
many organisations would be looking around for partners; not being left behind
is a powerful motivator.
2 strong leadership
If the adoption of digital in an industry,
then, if there is to be data driven, the decision and support has to come from
the top. Changing people’s mindsets is not easy, and we may disagree on what
makes effective leadership, but I am quite sure we would agree that the ability
to stay the course is critical in a leader who wants to implement change
towards data driven.
You may argue that I am shooting myself in the
foot, because I have been arguing that HiPPOs would be winning, and you are
very unlikely to have a bigger HiPPO than the CEO.
However, a HiPPO can easily win any battle, but
change is more of a war than a battle. Furthermore, if there are opposing (or
even just old-school status quo) voices in the organization – or people who
want to become the new HiPPO – then any change will be in a very
stop-start/one-step-forwards-two-back fashion.
Basically without strong leadership, an organization
cannot change, or will change at the pace of its slowest/least-willing/biggest bully executive.
In the case of analytics/”data science”, there
is simply no point trying to effect change without strong leadership. It will
just lead to frustration.
For consultancies on the other hand, doing
tactical projects can be a decent source of income, as long as success metrics
are defined very clearly at the outset; however any project where delivery
cannot be measured objectively should be avoided.
3 Results based culture
It may
sound obvious to analytics/”data science’ people, but not all organisations are
results based.
You do not need analytics to be results based, not only in sales
but across the organization, such as in operations and even departments like
marketing, PR and comms…
An organization
who has the mindset of measuring outcomes, setting KPIs and rewarding their
people according to these KPIs will quickly adapt to the use of analytics/”data
science”.
But if an organization
still rewards people subjectively, focuses on ‘effort’ (or worse relationship)
rather than outcome, then the first battle for use of analytics will be over
the need to measure outcomes.
That’d be a
lot of change to be done, and implementing analytics is hard enough without
having to fight that battle.
Furthermore,
an organization that does not have a results-based culture is most likely to be
personality driven, HiPPOs will fight for their corner of the murky waters…
In sum, it
would not be a good organization for someone into analytics to join
immediately.
As for
consulting, it may be useful, but focus on short term projects that minimize bruising
from HiPPOs, that have at least clear measurable outcomes. These may or may not
grow into longer more transformational projects, or even longer term contracts.
But as long as the culture does not change, it might be better not to prioritise
such opportunities.
Summary
Nowadays
people can find like-minded people easier, and this has led to more visible and
unbending extreme stances
Furthermore,
the craze around ML/DL/AI/”data science” has increased demand for analysts/”data
scientists” but also allowed some not so accurate beliefs to seep into the
collective mind.
However,
the increased demand, has also increased the supply of training and courses
that supposedly prepare one for a career in analytics. But the quality of these
is hard to ascertain.
This
sometimes creates people with a little knowledge that may be dangerous. This is
worse when that knowledge belongs to executives, or worse HiPPOs.
Therefore,
as practitioners, while the goal of “data-driven” organization is very
enticing, most of us end-up being disillusioned and frustrated.
In order to
minimize chances of this, I have proposed 3 aspects one should be very mindful
of before getting involved in a gig/contract/job.
1 Industry –
different industries have different maturity I use of analytics/digitization.
In a more mature industry, it is easier to find people to support the efforts
to be data driven. In others, you may find more blockers. As an employee that
can be real frustrating.
2
Leadership – sometimes a good leader can drive change. Finding a strong leader
is critical if an organization has to become data-driven. Change has to take
place across the organization, and mini HiPPOs, status-quo people will drag the
process down, deflect issues and focus on their comfort zone BAU. A leader who
cannot cut through this is not someone you should follow as an employee. As a
contractor, focus on measurable outcomes, to minimize risk, and report directly
to the project sponsor who has to be right at the top.
3 Culture –
any organization that wishes to become data-driven has to measure results and
reward people accordingly. If an organization is not already doing that, the
battle to become data driven will be long and painful. So someone from the
analytics field should really reconsider joining such an organization. However,
this is good hunting ground for gigs/short contracts as long as you can ensure
metrics are used as acceptance criteria; this would help minimize chances of
being crushed by HiPPOs.
Conclusion
If you find
an opportunity to make an organization data-driven, and this organization is in
an industry not traditionally associated with analytics, whose leader is more
of a consensus/approval seeking person and where the organization doesn’t focus
on results, then it is probably better to look elsewhere if you are looking for
a longish work stint.
On the
other hand, an organization that is in an industry that has been toying with
analytics for a while, has a results based culture and has strong supportive
leadership, then lucky you.
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