WHY WE'LL SOON ALL BE DATA EXPERTS

WHAT IT'S ALL ABOUT...


Gartner predicts that in 2019, normal business users will have greater analytics capabilities than that of the professional data scientist. This blog explores the technologies that are making this possible.


Data is like blood. It flows through an organisation, nourishing business functions so that they can function at optimum capacity.

In the same way that blood needs oxygen to nourish the cells, in order to enrich an organisation, data needs to be effectively analysed.

[In steps the data expert]

For years, the data guru (scientist / expert / genius / general superhero) has functioned as the lungs of the business, “oxygenating” data to make it accessible to the average user.   

The problem is, when the process of analysing data to produce useful insights takes weeks, even months, it can leave vital business functions (organs) starved of oxygen.

But things are changing.

Gartner predicts that by 2019, normal business users will have greater analytics capabilities than that of the professional data scientist, and it’s all because of a handful of technologies that are changing the game.

 


Self-service

For a long time, using data experts as the middleman between the data and the business user made sense. The experts were the ones with the knowledge and skills to translate the data into actionable insights.  

The issue is, this would often see a game of organisational “Chinese whispers” take place, with data bounced from person to person and often altered beyond recognition by the time it returned to its rightful owner.

In reality, who better understands the purpose of the data than the person who created it in the first place? Only they fully understand the questions that need to be answered, the information that is needed and how urgently insights need to be generated.

Self-service prides itself in delivering simple to use BI tools to the average user. This is empowering business users to cut through the complex software to experience straightforward access to insights.

Users can unlock their data in real-time to make decisions quickly and accurately without having to rely on IT, and this is equally helping to unburden IT from constant reporting requests.



Natural-language generation

Let’s face it, even the simplest of data can cause headaches when it comes to explaining what it actually means in real-life.

Natural language generation (NLG) uses artificial intelligence to unpack complex structured data, and transforms it into narrative that explains data in human terms.

For example, a sales manager would be able to use NLG to look at sales figures and identify clear trends like “33% of sales in May were generated from the Covent Garden branch”.

This is making it easier than ever to generate digestible (and actually useful) insights.

NLG is also helping to automate the process of writing data-driven reports like financial reports, board reports and product descriptions.

As NLG technology develops it’s becoming more and more sophisticated, going beyond simply summarising the data and actually explaining why the numbers are as they are.

This means that when presenting the monthly sales figures to the board of directors, the sales manager that we visited earlier would not only be able to state that 33% of sales in May were generated from the Covent Garden branch, but actually explain that these figures were due to a relaunch of a best-selling product which attributed to 7% of sales… and so on.



Search integrated in software

Imagine having your very own Alexa to answer all of your data queries.

Thanks to the power of search integrated in analytics software, discovering insights is now as easy as searching for the nearest Italian restaurant on Google.

Users can simply say “show me the sales for product A this month compared to the sales for product B this month, as a bar chart” and the data will be shown as a neatly presented bar chart.

This handy technology can even be combined with natural-language generation to provide even more ease when it comes to generating all-important insights.

This saves business users from the pain of having to spend days and weeks trying to make sense of a sea of complicated data, and is truly changing the game when it comes to handing back power to frontline users.



Cloud

The days of being chained to a laboratory of complex software to gain data insights are over.

Cloud is giving business users the ability to unlock insights no matter where they are.

Users can access their data by simply opening up an app on their iPhone or iPad, giving them the power to take action anytime and anywhere – whether they’re sat at their desktop or on the go.

On top of this, Cloud is helping to simplify the way we imagine BI software. Many applications allow users to plan and visualise in one simple application, which means that users don’t have to rely on ten different specialist IT systems to decode their data.

Another thing to mention is the fact that Cloud technology allows users to collaborate and share insights with their colleagues. This enhanced collaboration of data across an organisation is working to make data analysis a process that starts and ends with people rather than technology.

 

Although many of the technologies mentioned aren’t particularly new on the block, it’s clear that they’re changing the way we look at data analysis.

By providing users with the power to unlock the true potential of their data in a way that’s quick and easy, these technologies are making data analysis accessible to all. As these technologies continue to advance, we can expect the once niche skill of analysing data to soon become an inherent (and essential) part of any business role.

Want to take things to the next level with your own analytics capabilities? Take our free data maturity quiz to find out where you are on your data maturity journey and understand the exact steps you need to take to move forward.

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Oletta Stewart

Content Writer


Oletta Stewart is a Content Writer for MHR Analytics.

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