6 Steps Towards Better Data Management for Startups – InData Labs

Businesses nowadays accumulate tons of data, whether it is information collected through 3rd party tools like Google Analytics, or the data that is being stored within a site’s backend, like a MySQL database.

What many of these companies don’t realize is the importance of data. In today’s world, data is a valuable business asset and enterprises that haven’t reached this conclusion yet are in for a rude awakening.

Data is of particular importance for startup businesses that need all of the advantages that they can muster. Instead of relying on vague industry benchmarks for various KPIs, startup businesses need to realize the full potential of their data and use it as the source of their benchmarks.

Each business is unique and relying on outside sources to deliver the proper business goals for your company is often pretty ineffective. Here’s how startups can exploit their data to improve decision-making, operations and even targeted marketing campaigns.

The global data management market is expanding at anannual growth rate of over 14%. Data Management offers businesses flexibility, accessibility, and advanced analytics capabilities. That’s why structuring data should be one of the priorities for any company. By ‘structuring data’ we mean setting up a data management system that allows the capturing, storing and processing of said data.

Of course, it all depends on your business and the types of interactions that you have with customers, but you always need to aim for the highest level of transparency in data management.

A simple example would be an email complaint from a client. You can log its details into the database, but it’s going to be hard to analyze in the context of business goals. You could just take that email, assign it a value, for example, ‘negative’ for a complaint and log it, adding text as additional information.

While there’s machine learning, natural language processing, and all sorts of other cool buzzwords within advanced data analytics, it’s still important to know your data. Even if you want to apply some of these cool techniques to your data in the future, you need to be able to understand the outcomes that you want to predict and what data is relevant to your business goals.