Measuring the levels of data maturity to capitalise on data accuracy – THE ICONIC

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
• Veracity
• Access

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.