Analytics Trends 2015
A below-the-surface look
If some of the hype around business analytics seems to have diminished, it’s not because fewer companies are embracing the discipline. On the contrary, analytics momentum continues to grow, moving squarely into the mainstream of business decision-making worldwide. Put simply, analytics is becoming both the air that we breathe – and the ocean in which we swim.
Analytics innovators continue to push the edge, looking for new ways to gain advantage over slower-moving competitors. In some cases, that advantage comes through sweeping discoveries that can upend entire business models. In other cases, more modest insights may emerge that unleash cascading value. For 2015, leading companies are working on both fronts to strengthen their competitive positions. These significant trends are in play – and in 2015, one supertrend is the context for everything that follows.
Quadruple down on data security
With the volumes of data being captured and managed by organisations today, analytics is the first and last line of defence for data security. Getting it right requires the convergence of innovation,analytics, digital connectivity and technology – all integrated into a more seamless approach with fewer holes. It’s not good enough to be great at analytics if you can’t convert it into a strategic asset andtie it to digital execution. That applies to all the trends discussed in this report, not just the escalating importance of data security.
The Analytics of Things
The Internet of Things generates massive amounts of structured and unstructured data, requiring a new class of big data analytics to uncover and capture value. In the hands of talented analysts, these data can generate productivity improvements, uncover operational risks, signal anomalies, eliminate back-office cycles and even drive enhanced security protocols.
Growing numbers of analysts and researchers insist that data should not only be managed as an asset but should also be valued as one.They see a future where companies can routinely monetise their own data for financial gain. For example, when consumers shift to online and mobile applications for shopping, the digital exhaust they create can have significant potential. But sometimes their digital exhaust is simply that: information with little value. You have to understand which situation you’re facing
The convergence of machine and human intelligence is disrupting traditional decision-making by equipping people with knowledge that was almost unimaginable just a few years ago. The connections between people and machines are becoming both more natural and more familiar, creating better and faster decisions throughout the value chain.
The rise of open source
Once restricted to Silicon Valley, open source solutions such as Hadoop are finding their way into the enterprise and being used by mainstream firms around the world as data storage and processing engines. And it’s just one of many open source solutions that are finding their way into the enterprise. Others include Mahout for machine learning, Spark for complex event-processing and specialised tools that are being adopted alongside commercial software. And, of course, there’s R, the open source language and environment for statistical computing and graphics.
Tax analytics : Striking gold?
Despite being focused on numbers, tax leaders within companies have been slower to adopt analytics.1 The leading companies that are beginning to address the area focus primarily on tax planning, with the goal of reducing taxes and better understanding the financial implications of different tax decisions.
Universities step up
The marketplace is looking for a supply of true data scientists, not just button pushers. Many universities are working to serve this need. As analytics grows to pervade wide-ranging professions and business models, the stakes are getting higher.
The data brokerage business has only grown hotter as analytics capabilities have experienced exponential growth. That’s unlikely to change in the near future. But, as those who purchase and use this data grow more familiar with it, they’re applying more scrutiny to the product they’re being sold. Maybe that’s because the possibilities for what happens at the next level of data accuracy are so tantalising.