Big Data Doesn't Have to Be a Big Deal: Here's How to Handle it Effectively

Businesses today depend on data more than ever. But as the amount and variety of information firms have available continues to grow exponentially, the question of how to manage big data effectively needs to be at the forefront of any enterprise's planning.

For instance, some estimates suggest that in 2021, as much as 1.134 trillion MB of new data was created every single day, while some 278 petabytes of data were sent and received every month.

Within this, greater use of mobile and social media mean there are more opportunities than ever to learn about your customers and spot emerging trends. Meanwhile, the emergence of technologies such as the Internet of Things lets companies gain much more insight into what's going on inside their business - provided they have the tools to effectively collect and analyze this.

The big challenges facing big data strategies

One of the biggest issues with big data is simply getting this vast array of information under control. Traditionally, big data challenges were defined in terms of the 'three Vs' - volume, variety and velocity. In other words, how do you gain insight from huge amounts of information, from many different sources and formats, at speed?

However, in recent years, several more Vs have been added to this. They include value - can you find the right data at the right time to extract useful insight - and veracity - how can you be certain that the data you're basing your decisions on is accurate?

Big data comes in many forms, from many sources. In some cases, this can even make it difficult to ascertain what data you actually hold, let alone where it might be found and who will have access to it.

This can have a wide range of consequences. It might mean you miss out on emerging trends or fail to spot customers who are unhappy because you don't have visibility into the right data. In other cases, it could pose a range of security issues that make your firm vulnerable to data breaches.

How to get your big data management under control

To tackle these issues, you need an effective big data management strategy. This should encompass everything from how you gather data in the first place, how it's cleansed and stored, and the way you turn the insights you gain from the process into action. Here are a few things to keep in mind to make this a success.

1. Know your goals

An essential first step is to have a clear idea in mind for what you want to get out of a big data analysis. This will help ensure that you're using the right data for the right purposes and, more importantly, that you're asking the right questions when you run an analytics process.

As part of this, you need to conduct a full audit of your entire data environment to find out what you have and where it is. Knowing this will help you understand what possibilities are open to you or whether you have to go out and gather more raw data.

2. Keep your data clean

Taking the time to ensure the data you're ingesting into your big data analytics processes is as high quality as possible is vital. This is especially the case when you're dealing with a wide range of structured and unstructured data that comes in many formats.

Data cleansing techniques should standardize this formatting as much as possible, as well as filter out duplicated or incomplete information. This will give you the peace of mind you need that the conclusions drawn will be accurate.

3. Embrace automation

While data cleansing is a vital activity, it can also be very tedious and time consuming. For instance, it's been estimated that data scientists spend between 50% and 80% of their time cleaning their data sets. As well as leaving very little time left for the important job of analyzing it, this also opens you up to greater risk of human error.

Therefore, tools that can automate these processes help ensure your big data team isn’t overwhelmed by mundane activities and can focus their attention on the things that really matter - the insight.

4. Use the right tools

There are a wide range of free and open-source tools available to help businesses take control of their big data and turn it into value. For example, one of the most popular is Apache Hadoop, which is a framework for the distributed storage and processing of large data. Within this, there are several essential mobiles you need, including Hadoop Common, HDFS, Hadoop YARN and Hadoop MapReduce.

One advantage of tools such as this is, because of their open-source nature, they have a large and passionate support community. Therefore, businesses should always be able to find the answers to any queries they have to help them make the most of these tools.

5. Visualize the results

Once analytics processes have been completed, it's important these are able to be turned into actionable insights. This won’t be possible if the outcomes are presented in forms that are only comprehensible to those with expertise in data science.

Therefore, using powerful visualization tools to provide results in a format that can be easily understood by decision-makers in business units. Knowing what format will be best-suited to each dataset to aid interpretation is essential in making this successful.