How to Read The Future in Big Data and Business Analytics

How to Read The Future in Big Data and Business Analytics

Published by: Nathan Fulham
Published date: 9th Mar 2018

(Note: This news item is a summary of an article published in the Sunday Business post on March 4, 2018)

The adoption of real-time analytics to inform better decisions is gathering pace, aided by advancements in technology that are seeing a gradual shift from BI to AI.

The problem with the “the next big thing” in business technology is that you always have to wade through an ocean of hype to get a clear picture of what organisations are actually doing with it. In the case of analytics, data may well turn out to be “the new oil” or facilitate robots taking our jobs, but for now it’s an area of IT that some companies are using for business advantage while many more are struggling to unlock its potential.

There’s a widespread understanding that accessing the increasing amounts of data companies hold in their systems will yield valuable business insights and inform better decisions, but organisations find it hard to execute on strategies to optimise the infor­mation that’s available to them.

John Roddy, commercial director at Co­dec, put it another way. “Until recently there have been very low adoption rates. But there’s been a real change in the last 12 months and the cloud is responsible for that,” he claimed. “All the complicated things you had to do in the past around data mining, statistical analysis and predictive modelling are available now in the Microsoft cloud. It’s more readily available and easier to digest.”

Codec sees analytics as inseparable from digital transformation and the work it does for customers around Microsoft Azure and the Dynamics 365 platform. Roddy’s argument is that these technologies get around historical inhibitors. No longer will you be bamboozled by programming languages like Python and R or worry about the costs of recruiting a data scientist.

There is still learning curve, however, not least because business intelligence (BI) and analytics means different things to different people, according to Roddy.

“A lot of organisations think that an Excel spreadsheet is business intelligence. They’re right on some level, but the advent of the cloud has made more sophisticated solutions readily available,” he said.

Gone are the days of spending lots of money on racks of servers and consultants that de­liver too little information too late, he argued. Now organisations can get to see the value from a project in week one, particularly if they are a B2C (business-to-consumer) company where better customer service can deliver quick wins.

Roddy talks about one client with 2,000 customer service people in a contact centre. Codec deployed a machine learning solu­tion to analyse the peaks and troughs in the business and predict when it would need to hire more call agents. “You can very quickly realise the benefits of a project like that be­cause it’s about helping understand customer relationships,” he said.

Another big advancement has been self-service BI, putting analytics in the hands of people who need it, when they need it.

“Back in the day, vendors used to do very well building dashboards that are pretty much available today out of the box. With Microsoft it’s Power BI,” he said. “The real skill now is what we do at the backend, implementing the solutions and making the “glass” smart and intuitive to use — the tablets and phones that people use to access insights.”

Codec also introduces its customers to more innovative analytics. As part of a project for an airline, the team analysed five days’ worth of tweets where the company was mentioned and introduced them to the idea of gauging customer sentiment. “There’s been an explo­sion of unstructured data through things like social media that you can use for sentiment analysis — it’s become quite an important feature in Microsoft cloud products,” he said.

Roddy believes that the biggest win with cloud analytics, and the Microsoft portfolio in particular, is the way it makes analytics more accessible and transformational. “If you have diverse data sets and are able to interact and analyse them in a very easy way, you will make more decisions that are pre­dictive and holistic, rather than reactive and disconnected,” he said

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