Data Management for Startups

Not all startups that accumulate data, end up leveraging it to its full strength. Despite a glut of data available through 3rd party tools like Google Analytics; most startups waste effort on collecting, organizing, storing it. In sum, start-ups could end up being information-rich, yet insights-poor.

Managing data from Day One can provide a competitive edge like no other. Deploying analytics fast, is indeed possible. Instead of chasing obscure industry benchmarks for business KPIs, Startups can set their own benchmarks, provided they fully leverage their own data.

Assuming that you have already zeroed in on outcomes you expect of your data, here’s how to get started on your data management journey.

The starting point: Structured Data

The data management market is growing at a CAGR of over 14%. Why? Simply because it enables scaling, market access and advanced analytics. That’s why startups need to start with putting a data management system in place that enables capture, storage and processing.

Intentionally or otherwise, businesses collect all kinds of data, of which Structured Data is the go-to source, to improve everyday functioning.

Structured Data is highly organized, hence easily understood by machine language; which in turn makes it easier to analyze for everyday view of business & customers.

Next come basic data science & analytics

So you’ve built a working pipeline for your structured data. To make it work hard for you, the next step is to apply basic Data Science techniques, to deliver the most time-saving outcome — Single view of business and customer. Imagine a typical working day starting with a 30-second, breezy review of how your business performed over the past few hours.

Upping your Analytics game — in small sprints!

As data volume grows, startups want to mine it better. By now, your business has already set the foundation for Machine Learning, Natural Language Processing and basic analytics. So how do you get more out of it — discover new use cases, store it better, optimize processing time etc.?

Advanced analytics capabilities are no longer the privilege of huge enterprises. Startups shouldn’t be overwhelmed about exploring advanced analytical capabilities via machine learning and AI. Cloud platforms like AWS, Azure, GCP have made ML far more affordable and accessible today. These innovations ought to drive startups to ask more of their data.

Are you a startup trying to decide how to get to the next stage of your Analytics journey?

Reach out today at, for a ‘no fine print’ conversation.

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