You don’t need to be a genius to do Data Analytics. Data Integration is the key!

You Don’t Need a Stats Ph.D. to Do Data Analytics

Data is bigger than ever. But that doesn’t mean it’s harder to analyze. In fact, the reverse is true: Today, you don’t need to be a data analytics expert in order to work with Big Data.

You don't need an advanced degree to perform data analytics

There’s no denying that the field of data analytics has become more complex over the last decade. The digitization of everything means that companies now generate and store much more information than in the past.

Plus, much of that data is only useful if you can analyze it in real time. This requirement places more pressure on data management teams to deliver analytics results virtually instantaneously.

Meanwhile, a slew of new data management and analytics tools have arisen over the past several years, from Hadoop and Spark to Elasticsearch. Gone are the days when knowing the difference between your medians and your means was enough for crunching data.

Big Data Is Not a Big Hassle

Yet, these changes do not mean that working with Big Data today requires years of experience and lots of credentials.

On the contrary, the beauty of the Big Data world today is that the proliferation of so many data management and analytics tools makes it easy for anyone to turn data into value without having a Ph.D. in statistics.

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Well, OK. That might be a bit of an exaggeration. Your grandmother is probably not cut out to analyze data, unless she happens to have a background in Hadoop and the like. You do need some skills to work with the current generation of Big Data tools.

At a minimum, you need a basic understanding of data structures. It helps to have the ability to write SQL queries, too, and some Java chops (although there are plenty of tools out there to help you work with major platforms without having to do much programming yourself).

But the point is that you don’t need to be a data analytics expert in order to do Big Data today. Anyone with basic programming or sysadmin skills will be able to pick up modern data analytics frameworks easily enough.

Professional data analysts still have important roles to play in dealing with the most complex data challenges.

Don’t Knock the Data Experts

This is not to say that a stats Ph.D. is no longer relevant, of course. Professional data analysts still have important roles to play in dealing with the most complex data challenges.

They’re the ones who can make sense of the really complicated data sets. They have the advanced programming skills with languages like R that can help handle Big Data workloads that are not a good fit for tools like Hadoop and Spark.

But again, these types of advanced skills are not necessary for many of the typical data analytics workloads that companies face today.

Related: Simplifying Big Data Integration with a Modern Data Architecture

Data Integration is Key

In order to assure that today’s data analytics tools are accessible to anyone with just a basic IT skillset, there is one big challenge you have to overcome: Data integration.

While tools like Hadoop and Spark are easy enough to use, they need data to work. If the data you want to analyze is stored on a different system or in formats that are not directly compatible with modern analytics tools – which is likely to be the case if the systems that collect and store your data include mainframes – you need to integrate it with your analytics platform before you can derive value from it.

Performing data integration manually requires a lot of specialized skills. That’s not a job for the average Joe.

Fortunately, Syncsort solves that challenge. By using Syncsort’s Big Data solutions, you can make data integration with platforms like Hadoop and Spark seamless – and ensure that everyone on your team is ready to work with data whenever necessary.

Christopher Tozzi

Authored by Christopher Tozzi

Christopher Tozzi has written about emerging technologies for a decade. His latest book, For Fun and Profit: A History of the Free and Open Source Software Revolution, is forthcoming with MIT Press in July 2017.

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