As we head into a world where organisations are becoming
more and more dependent on data and analytics, the Head of Analytics and Data
Science from The ICONIC divulges
into where the industry is going and key steps to transform into a data-driven
organisation.
Kshira Saagar:
At a very high level, there are two big challenges for a Data and Analytics
team at any organisation:
- Credibility
- Time to insight.
Credibility refers to the aspect of the end user’s willingness to
believe, agree and action on the top of our data-driven insights - and to get
any logical thinking person across the spectrum of belief to action using data
as your only tool is quite a challenge.
Following on from the first challenge is the natural second one of
deriving fast and useful actionable insights from the humongous volume of data
rapidly. As the famous adage goes, decisions
don’t wait for data - so if the data and insights are not ready in time for
decisions to be made, they most likely end up being exercises in vanity on a
nice little PowerPoint buried within someone’s unread mails.
The future for any data-related initiative must be able to answer and
accommodate three major components:
Scale - the one thing that
we can predict confidently about the future is that the size of the data and
the input sources for data collection will definitely increase exponentially.
This means a lot of current approaches and technologies to data warehousing
from 15-20 years ago will no longer help us with the data needs of the future -
leading us to rethink a data architecture based on the new age data solutions -
for the next 5 years, if not more.
Veracity - with such a big
volume and velocity of data, comes the problem of veracity - i.e. data
credibility. Collecting and processing millions and millions of
rows/tuples/columns of data in a day is all fine but it will all mean nothing
if, at the end of it all, the data is deemed unreliable and incorrect. A lot of
older data solutions encourage data collection over data verification.
Performing real-time event-driven algorithmic data integrity checks sits
outside these solutions or need to be over engineered, which highlights the
need for a new architecture that can not only collect process and clean data
but also provide the options to check for integrity and provide more confidence
over the data collected.
Access - the biggest
roadblock to a future-looking solution is that given the lack of maturity of
these solutions in the market, making data collected and processed on these
platforms accessible to a wider section of the company becomes a challenge -
not only for the wider business folk, but also to the common analyst who’s not
too happy about having to write complicated Scala code to be able to even start
interacting with data. This makes it absolutely imperative to come up with an
accessible-for-all last mile that integrates well with the future data
platform.
One crucial suggestion for companies investing in going deeper would
be:
·
To set up a less fragmented and
more cohesive foundation for their data and analytics teams.
That involves thinking about the data and analytics team as a
horizontal, and not like a vertical tied to individual departments. Think of
Data and Analytics as you would think of your Finance or Human Resources team -
horizontal for the whole company.
Rather than having a separate Marketing Analytics department that
churns out amazing insights into customer behaviour and another separate
Product Analytics department that in turn generate their own deeper insights
running counter to the aforementioned first team which is quite consuming in
terms of time and energy - it pays to have all the Analysts and Data Scientists
under one common umbrella but still deputed to work for individual teams. This
is where cross functional thinking plays a big role and can not only unify data
initiatives but also help in scaling them efficiently.
The data-action loop is getting closed faster than widely expected and
I expect this momentum to keep growing upwards. For quite a long time data and
analytics was pigeon-holed mostly within certain groups like Strategy and
Marketing teams, and the output of most of this work ended up in the limbo land
of PowerPoints and inboxes. The data-action loop was mostly never closed and
analysts/data scientists could “predict” stuff and get away with incorrect and
sometimes invalid findings - because the actions were never taken on the back of
this data and analyses. This was and is not particularly motivating to data
teams that want to make a serious impact and causes a vicious cycle of
ineffectiveness.
But now that’s changing. Be it an in-house data team or an external
consultancy, everyone is asked to ‘prove’ the value of their data-driven
insights, offerings and strategies - and this can be seen reflected in how
senior leadership and executive teams have started approaching the data angle.
The landscape has evolved to an extent where it has become easy to “measure”
effectiveness of a strategy, insight and/or the solutions powered by data.
Data-driven insights can no longer hide behind the veil of
“you-didn’t-implement-it”
When this healthy trend keeps growing, it will ensure that data is no
longer a ‘good-to-have-eye-candy’ on a document but will be the centerpiece of
all decision making. In turn, that will lead to people working in data and
analytics to feel more vindicated about their work and feel the output from
their teams are valued. Which finally will ensure that the these happy data
team members produce amazing and more boundary-pushing outputs which will
benefit the end customers’ overall experience with an organisation.
Still interested? Come and see me speak about
Measuring
the levels of data maturity to capitalise on data accuracy at the
Australian Data Summit 2018 Sydney from the 19-21 November.
Still interested? Stay tuned for information on upcoming conferences and summits by following us on Facebook @ Akolade Aust
Written by: Kshira Saagar, Head of Analytics and Data Science, THE ICONIC
Kshira Saagar (Shee-Ruh Sa-Ga) has been with the Analytics/Decision Sciences industry for almost a decade now having worked across Americas, Asia, Europe and more importantly Australia. The bulk of his work has been focussed on developing solutions for the Analytics problem spaces of the Retail, Telecom and Insurance marketing departments at some of the leading Fortune 100 clients. In his other roles, he has enabled decision making through data for clients from the Media, Healthcare, Aviation, Logistics and FMCG organisations.
In his current role at The Iconic, as the Head of Analytics and Data Sciences, he's responsible for understanding and enabling data driven decision making. Previously at Datalicious & prior to that at Fairfax, he was responsible for institutionalising data-driven analytics across the company’s core competencies and building new-age analytical products for the organisation.