Big Data In Startups - How Do You Start?

How can Big Data be properly implemented in startups today?


Does Big Data only work for big companies?

This is a question that many have asked in the past and one that is clearly important today. The startup scene is bigger than ever and with the influx of money and talent into new companies, it is only likely to continue.

In terms of utilizing Big Data within this scene, it is less clear cut. Is it worthwhile for a company with only 10 people to invest in a new data system?

Many companies believe so. It is clear from hundreds of examples that working with your data to create actionable insights is the ultimate goal of any data programme, so what should startups do in order to begin?

Start Early

Starting a Data Programme does not begin when you create your first algorithm or pull your first report, it begins in the way that data is being collected.

Collecting as much data as possible is always key, but the way that it is collected and stored is equally important. A well maintained database is important for having strong actionable insights from your data. This is making sure that the correct fields are present, accurately input and correctly categorised.

This begins almost before any data collection has occurred and creates a firm foundation for when a Big Data system is implemented. The popular saying for data systems is garbage in, garbage out, making sure your data is gold before it enters the systems will bring the best results.

Do You Need It Yet?

Jumping into a Big Data implementation before you are ready can be as damaging as not jumping in at all. With the outlay that is required for the systems or subscriptions, if it is started too early and doesn’t get the desired results then companies are unlikely to invest when they are in a better position to do so.

It is often a good idea to scale the systems that are already being used. This could mean buying add-ons to existing systems or simply using it in a different way.

However, the other side of this is that companies are often reluctant to begin data programmes because they think they do not have enough data.

When many of the fortune 100 companies who have made huge strides in their data programmes began them, they had only a few gigabytes before gathering more to coincide with the growing need for data within their business. Starting off with a smaller amount of data does not mean that it lacks the same value, it just means more may be required in the future to make further gains.

Can You Commit?

When you invest in a data system, the most important aspect to consider is that it is not a system that will last for years then be replaced when it becomes too slow. Data science is an evolving business area that requires new investments and work all the time in order to maximize its potential.

A startup needs to be willing to make this investment and have faith in the systems that are being created and updated. The updates and work that goes into the upkeep of systems will not always make considerable differences to performance, but will be necessary for future growth.

The investments will need to be ongoing and this is something that needs to be considered when the pros and cons of budgeting are planned.

Data Based Changes

In order to make the most of the data that is produced, startups need to be able to make changes that are based on the data they are shown.

Often within the startup environment, the way that employees work is based on gut feeling and exploration, so being told to do something in a certain way because of an analysis goes against the way that they want to work. In order to make the most of any big data programme it is important for any company to be able to make changes to what is being done quickly and efficiently.

Therefore, getting full buy in from employees is as important as the amount of investment from the budget or the amount of data that is collected. Without a workforce that is willing to implement changes based on the results of any analysis undertaken, any data system, regardless of how good will ultimately be a failure.


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