Delivering Value Through Analytics
Organizations across
industries are in the process of increasing digital engagement,
closely integrated with
analytics,
for
their candidates, employees and customers. When we meet organisations
attempting to do bigger and better things, we often turn to the maturity model
concept, both as a means of diagnosing the level of the firm’s capabilities in
a given area, and of explaining how that capability can be enhanced to ever
greater heights.
Maturity models describe the characteristics of
maturity across a multi-point scale, from zero or one (typically a chaotic
state), through to say five, where the capability is at the highest level of
maturity and creates a genuine point of difference for the firm. For analytics,
the
journey has many layers and it pushes from transactional reporting of lag
metrics to proactive reporting of lead measures to prediction and prescriptive
analytics/insights in various business scenarios.
{Circle denotes what % of
organizations are at what stage of analytics}
Source: Bersin by Deloitte | DUPress.com
The Myth of Maturity Models
In the domain of advanced analytics and big
data, large analytics consulting providers or core analytic firms use a variety
of factors to pin-point a firm on the analytics maturity scale:
Let us first break
the myths of traditional maturity models and share learnings from
not so successful digital transformation & analytics journeys of mid to
large size organisations.
Let me share with you a couple of
examples from
my research, observations and interactions amongst large IT services firms in India*.
Example 1:
A top multinational IT services firm with large
offshore presence took
a bold
step a few
years
ago to automate
descriptive
business and HR reporting completely through Qlik, assuming it would be able to expedite decision
making to positively impact business & HR metrics. In the third year of its
journey,
full-scale execution
of all business and HR reports is still on with very little impact on decision
making. Full credit to the organisation for
reducing reporting TAT (Click of button vs. reactive) and reporting & analytics
team optimisation
saving
dollars. However,
this
is a
notional saving
given the investment
made into
vendor evaluation time, leadership time & cost and
internal team deployment time
& cost which
would easily take another 2 to 3 years to recover.
Challenges/learnings from the long execution cycle:
•Technology
integration capabilities (Synergies of buyer & vendor systems)
•Vendor
domain knowledge for faster transition/automation
•100%
reliance on reporting automation can stall short term benefits of
semi-automated descriptive reports
Example 2:
Another large multinational IT & consulting
services firm looked to optimise
talent acquisition
cost & time through advanced algorithms and analytics three years back. The
firm started the
journey by automating candidate screening process through NLP & artificial
intelligence technology platform,
followed
by integrating predictive analytics to score candidates on
employability/fitment, forecast probability of offer accept to even candidate
performance/stickiness through Big Data.
The myth with which the organisation
went
ahead was the fact that being a PCMM level 5 company, their hiring processes, candidate data,
job descriptions, hiring manager behaviour and historical employee data for like-to-like
profiles
was structured, clean and fully reliable. The pilot and actual launch showcased that while
candidate data was rich,
the internal data
to derive key insights from predictive analytics was extremely weak.
As a result, benefits were reaped only on auto screening.
Challenges and learnings from the
pilot &
execution experience:
•Capabilities
– Running a pilot to test predictive analytics capabilities on small scale
(Capabilities of not only vendor but buyer data quality, data environment &
availability. Any variables with less than 30% data availability cannot be used
for any predictive or prescriptive analytics)
•The
vendor failed
to assess descriptive reporting in buyer organisation (Organisations
without
matured descriptive reporting/analytics capabilities find it difficult to move
to predictive analytics)
•Cutting
edge AI technology with weak data yields little or no impact
It is necessary to issue a caveat
here:
The maturity
model
of your analytics practice needs to marry the organisational
maturity level. Key
questions to consider here are:
•Is
there leadership buy-in for prescriptive actions from predictive analytics?
•Are
there sufficient processes in place to capture clean data?
•Does
organization have a central data warehouse?
•Do
we have technology to support advanced data analytics?
•Does
organization have data required to accurately predict?
Leadership buy-in is comparatively easier
in financial analytics where we deal with numbers and predict numbers; whereas,
it’s an uphill task when the management problem revolves around human behaviour
and the attitude ranges from completely predictable to absolutely
undecipherable!
Also, it is important to understand that Statistics is
only a means to an end which prevents a helter-skelter approach to service
delivery. Without doing predictive modelling and creating causation frameworks,
there’s still much value that can be delivered to business. A
leap in the analytics value chain without the same lever in the organizational
maturity would render the outcome useless.
Here are a few examples to demonstrate
the value delivered at different maturity levels without the constrains of
sticking to gradual movement up analytics value chain:
AirBnB
and Indian bed-linen industry – Nearly a decade back, a handful of
financial analysts supporting HNI’s ran simple descriptive & correlation
analytics (graphs and dashboards) on growth of bed-linen industry in India and
merged with causation analytics to identify possible indirect sources of
demands for listed bed-linen companies. This sort of data can be a goldmine for
decisions about community growth, product development, and resource
prioritization. This led to a conclusion that growth in the number of rooms listed on AirBnB by home owners and the push towards
cheaper good alternatives to the established hotel industry would push the demand for bed-linens from manufacturers
in India.
Five years down the line, both listed and unlisted players have seen a huge
surge in both top line and bottom line and decade down the line few players
become multibaggers.
Financial
Analytics for end HNI customer – The financial services industry in
developed nations are truly leveraging the power of prescriptive analytics,
providing prescriptive actions to customers on banking and investment
decisions, based on factors such as personal health, financial status, weather
conditions in cities they live in, political or ecological changes and many
more such direct derived data with financial goal set.
Prescriptive
Analytics and Consulting for Governments – Most matured countries who have
connected systems, high end technology and citizen data captured over years
are, in fact, in a better position to leverage prescriptive analytics in
policy, political and economy related decisions. Virtual Singapore is one of
the best examples where policy makers, statesmen and citizens can build or test
decisions through use of IoT and
prescriptive analytics. The US government has a team of data scientists testing
various decisions to outcome of various policies implemented around the world
to help enhance the decision making process. This is a classic example of
starting with descriptive reporting to descriptive insights generation to
slowly move towards predictive which is extremely mature & requires huge
investments.
How to Derive Value?
Value comes from things that matter at
that point in time and for the overall organisational purpose. Almost
80%
of the reporting done in most organizations is transactional with metrics that
are of little value —that’s where we should start.
The right metrics provide a context
around which performance can be analysed. With the wrong metrics, an executive
summary gets reduced to an anecdotal commentary. One of the classic examples of
business metric in HR:
Employee Attrition
Reporting, Prediction to Prescription.
Reporting employee
attrition by various bands/locations/skills etc. renders little or no value
unless key employee clusters/groups to be retained are identified, reported,
predicted and prescriptive actions taken on this cluster to impact employee
attrition as a metric.
Such outcomes presented in a compelling
fashion win over the management’s trust and get them to rethink processes and
workflows, invest in technology and infrastructure, and adapt to the
organisational changes that arise out of your analytical frameworks. And
when the organisational and business needs are in synergy, you move up the
analytical value chain, leaving the management to answer just one question, Are
we aware of the
next
big thing?
Analytics team needs to delivery
descriptive & predictive models with Intelligence, Insights and Prescriptive
actions to
drive value and not just good looking dashboards or statistical models. This is
where a combination of statisticians, data scientists and domain experts can
together add value to answer the question of next
big thing?
Important for Analytics teams to
focus on Results but imperative for leadership to be ready to convert
prescriptive insights to actions
Significant untapped value lies in data
that already exists in most organizations, and analytics team needs to assess
required capabilities that can effectively exploit this data. However both
leadership team and analytics team needs to establish with a clear focus on
tangible business value.
Based on research one of the key reasons
for failure of internal analytics team in large organizations across various
sectors starting from Pharma,
Banking, Financial Services to IT has been lack of synergies and underlying
intent of setting up analytics team between leadership & analytics team.
Analytics team needs to establishing an
Agile Analytics data architecture and methodology to
addresses ever-changing business requirements and opportunities in a way that
can evolve along with the business to become a source of genuine strategic
value (Link it back to Balance Scorecard or organization goals).
Business users
and leadership needs to be prepared to act on insights from descriptive to
predictive analytic models to see true value of investments made.
About the Author: Gaurav
Vasu
Gaurav Vasu is Global HR Market Intelligence
& Analytics lead at a
leading global IT services company . He has worked with CEO office,
CHRO’s
and senior
HR leadership to shape the business strategy, identify human capital
implications and design people practices to enhance performance and
productivity. He is one of the top Industry experts in the Research, Consulting
and Analytics domain.
He also specializes in Growth Consulting
(IT/ITES), Market
Entry
Strategy, Industry
Analysis & Assessment (India, China & Philippines), Talent Supply
Mapping, Vendor Analysis, Peer Group Benchmarking, Financial Analysis
(Discounted Cash Flows, Relative Valuations, Simulation, etc.) and Wargaming.
During his 12+ years of experience,
Gaurav has helped delivered
consulting by growing the research and analytics value chain
in companies such as HCL,
Accenture,
Zinnov, and Knowledge Faber & Nirvana.
