Continuous improvements vital to agile and scalable data systems

13 Dec 2017

  • Analytics
  • Data-driven Marketing
  • Data

Kshira Saagar has been developing solutions to the analytics problems of the Retail, Telecom and Insurance industries at some of the leading Fortune 100 companies for most of this past decade.

Currently, at THE ICONIC as the head of analytics and data sciences, he's tasked with understanding and enabling data-driven decision making.

Kshira's presentation on 'Reimagining data processing and warehousing' at ADMA's Data Day in 2018 will explore unifying data, future proofing for agility and scaleability and insourcing data management solutions.

Kshira Saagar
Kshira Saagar

Here we get a glimpse of the challenges and solutions Kshira works with on a daily basis. 

1. What are the biggest challenges you face day-to-day and how do you overcome them?

At a very high level, there are two big challenges for a Data and Analytics team at any organisation - credibility and 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 emails.

2. How can you ensure that data systems are scalable and agile?

CICD - Continuous Integration and Continuous Delivery as used in the DevOps world is the best way to ensure data systems are scalable and agile. Instead of waiting for a trigger like a breakdown of existing infrastructure or undertaking an overall paradigm shift of data processing, the best way is to work on continuous improvements. Continuous improvements, for example,  independent microservices that can handle the ever-growing operational load and translating infrastructure to be purely on code on an ongoing basis - is vital to scale data systems.

And the ideal next step is to deploy these improvements continuously without too much focus on having to wait for official celebrations or ceremonies heralding these new improvements.

3. Having the right skills within your team is vital to successful analytics functions. How do you get executive buy-in to get the right resources?

Fortunately, THE ICONIC is truly a visionary company in that we have our vision of liberating our customer actually translated into actionable strategies for each department. This, in turn, gets translated to specific problems that need answers which are thankfully not too straightforward or quotidian. Each new hire or reorganisation of an existing team is therefore tied to these critical business problems - enabling a more scientific way to gain executive buy-in. The skillset we look for in a Data Scientist at THE ICONIC is that of a full stack one where the person can perform the business, tech and math components of this key decision enabling role.

4. What are your top tips on how companies can unify their data for better outcomes?

The most critical aspect of unifying the data is to unify the teams that use data to drive decisions. That involves thinking about the data and analytics team as a horizontal, and not like a vertical tied to individual departments.

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 - it pays to have all the Analysts and Data Scientists under one common umbrella but still working for individual teams. This is where cross-functional thinking plays a big role and cannot only unify data initiatives but also help in scaling them efficiently.

Hear more from Kshira Saagar at Data Day 2018
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