Choosing the right solutions suite for your enterprises’ Data Office.
By: Abhijeet Gaddagi
Tags: Artificial Intelligence, Customer Data Platform, Analytics, Data Science, Business Intelligence

As we entered the third decade of the 21st century, the world was hit by an unprecedented pandemic, forcing a tremendous amount of change to the way governments, organisations & even individuals function on a daily basis. The much anticipated digital revolution truly dawned upon us as (almost) everything from employee training to board meetings and sports to theatre — moved online overnight! People found themselves much more dependent on the world wide web than the pre pandemic days.

This resulted in many large enterprises witnessing a massive surge in the amount of consumer data being collected. With this, the need to judiciously and intelligently harness the data has become a key business problem for many enterprises.

“Information is the oil of the 21st century, and analytics is the combustion engine.” — Peter Sondergaard, SVP Gartner Research

We at TheDataTeam couldn’t agree more with Peter’s wisdom. Indeed, it is the choice of the combustion engine a.k.a. the Analytics Solution an enterprise on-boards which determines the long term winning prospects.

Today, it is not surprising that enterprises are considering doing away with established solutions such as Tableau , given that apart from robustness, they have little to offer in terms of what machine learning & artificial intelligence can deliver.

At the same time, a multitude of new age analytics solutions are implementing these technologies for their customer. Choosing the right one for your organisation can hence become quite a daunting task.

We attempt to demystify this process for you by listing a few key points to always consider before you make the key decision.

The system’s capabilities

In an era, when every solution claims to deploy machine learning & big data, it is imperative to distinguish the wheat from the chaff. A good starting point is to inquire about the size of the database the solution provider can ingest and quality of the models being deployed.

For an enterprise to churn out useful & actionable insights, it needs two key ingredients

a) Large quantities of good quality data

b) Advanced & robust mathematical models to process them.

“Data that sits unused are no different from data that were never collected in the first place.” — Doug Fisher, Professor, San Diego State University

With an abundance of data sources, the best solutions providers are always able to ingest all of your user attributes & entities , with the requisite amount of cleansing and validation. This ensures that when we start drawing insights, we haven’t missed out anything, no matter how trivial.

“Big data will spell the death of customer segmentation and force the marketer to understand each customer as an individual within 18 months or risk being left in the dust.” — Ginni Rometty, ex-President & CEO, IBM

Next are the algorithmic models themselves. Understanding one’s user base in the form of clusters is a thing of the past. Today’s AI & Machine learning models are capable of drawing a complete behavioural map of each individual customer. Any solution with these capabilities will definitely get extra points.

Real time is good, but is it Instant ?

In the connected world, getting real time feedback and insights is the norm. From how a given campaign performed to a customer’s experience with a product or service — measurements & communications are most effective when done on a real time basis. With reducing attention spans of the consumers in the social media-dominated environment, the need for instant gratification is even more accentuated.

However, traditional insight generation solutions are at best lagging by a few hours, if not in most cases by a day. System infrastructures which enable organisations to measure and communicate on an Instant basis have been proven to be game changers.

How easy is it for my team to adapt to the new solution ?

“The human side of analytics is the biggest challenge to implementing big data.” — Paul Gibbons, author of “The Science of Successful Organisational Change.”

Even with the most advanced capabilities, a solution is bound to fail if it doesn’t address the human element involved. Humans need simple, intuitive interfaces to work with daily. Sure, humans adapt fast and can be trained easily as well, but, nothing beats asking a simple question to the all knowing machine and seeing the answer.

A FAQ section and a training guide are always good to have, however, be on the lookout for features like natural language querying and minimalistic UI workflows. They go a long way in ensuring your team’s adoption rates and happiness quotient.

According to BARC, Tableau has a customer satisfaction rating of 4.5/10 & a customer experience rating of 5.7/10 !

This would also be a good point to inquire about the on-boarding and post-sales experience of the analytics solution. In our experience, the majority of self-serve products by large enterprises come with a very poor on-boarding and post-sales support. It often leads to decreased team morale, not to mention that we’ll still be far from extracting the real value from the data.

Is it fully governed, scalable & flexible ?

Every enterprise has its own sets of complexities and nuances which needs to be completely addressed. Add to it, the enormous number of users who will be gorging on the data pipelines to hit gold, the need for a robust security system to be in place cannot be ignored.

Be particularly focused on the accessibility & traceability aspects of your enterprises data. At the same time, the solution also needs to address your ever-growing ambitions and be fully functional at 10X the scale you’re operating in today.

Lastly, get the clarity on the number of customisations you can avail because you will definitely need them as you move ahead. While, we’d recommend a white-boxed solution, tailor made for your organisation’s need as an ideal solution, having a flexible analytics partner along in your growth journey can prove to be priceless.

Does it fit within my budget ?

“Data matures like wine, applications like fish.” –James Governor, ex-journalist and co-founder of RedMonk

And finally, the solution has to be economical, fit within your budget while addressing all of your use cases. An application which is fairly fresh with genius minds behind them is definitely recommended as it is most likely to be faring well on all of the above parameters including the affordability quotient.