The landscape of analytics is evolving and to effectively leverage analytics to drive business value, the decision-oriented executive and budding analyst alike must be attuned to the latest developments. Here are three trends in analytics to watch in the coming year.

1. “Self-service” analytics: democratising learning for all

The idea that analytics requires a highly-specialised skill set is fading – it is no longer limited to statistics PhDs or laboratory settings.

Now, more companies are investing in straightforward analytics platforms with user-friendly interfaces, enabling staff of all levels to better understand business performance.

These self-service analytics platforms empower non-technical users to track sales, product inventory, and more.

With this democratisation of data and information, the desire and ability to conduct analytics is expanding to consumers as well.

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Online real estate marketplace Zillow is one example of a consumer-facing self-service analytics platform. Rather than turning to real estate professionals to analyse different markets’ property values, homebuyers and sellers can easily log onto Zillow to directly access the data needed to make a competitive offer or reasonably price their homes.

In the consumer healthcare space, Fitbit, which provides users with a simple dashboard to review and evaluate their own health metrics, is yet another example of analytics available to all.

With the wealth of data now available at consumers’ fingertips, it is key that both they and commercial organisations realise the true value of analytics lies not just in accumulating information, but assessing it to identify trends and inform decision making.

New sources of data: From visualisation to action

From your car to your home and even your toaster, the universe of connected “things” generates enormous amounts of data. In the consumer sphere, companies across industries are moving from data collection to action.

For example, in-store beacons monitoring traffic inform retailers’ staffing decisions and telematics now play a bigger role in insurance policy and premium pricing.

Expect companies to not only capture more data from the Internet of Things and to visualise it for exploratory purposes, but also to utilise this data to inform operational decisions and truly add business value.

As brick-and-mortar retailers innovate to compete with their e-commerce competitors, one way they can derive further benefit from physical locations is by drawing on sensors, beacons, and cellular networks to offer loyalty program customers exclusive promotions when they are in or near certain stores.

Leveraging data from connected devices to send timely offers straight to consumers’ mobile phones provides traditional retailers with a competitive edge in conducting customised outreach.

Traffic management is another area where organisations can maximise the use of their available data, through smart cameras, beacons, and microphones.

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For instance, some entertainment venues seek to harness actionable insights by using traffic heat maps to inform product and advertisement placement decisions.

Gillette Stadium analyses fans’ behavioural patterns within the venue to optimise the arena’s layout and offer new promotions and products accordingly. And potential flow management applications for traffic pattern data span industries.

For example, as banks rethink their physical footprints in the face of an increasing shift to mobile and online banking, such monitoring information could similarly refine their strategy for determining optimal Smart ATM locations.

Automatically, intentionally introducing variation to learn quickly

Over the past decade, many data-driven leaders have adopted machine learning, leveraging technology to automate data analysis. An even more powerful concept is what APT refers to as Machine Experimentation™.

While machine learning allows computers to improve performance or predictive ability when analysing natural variation,Machine Experimentation™ dramatically speeds up the learning process by intentionally introducing variation, allowing an algorithm to self-adjust its parameters and expand its findings.

Automating the process of generating testable ideas streamlines the experimentation cycle and enables organisations to more quickly implement new initiatives.

Gartner analyst Robert Hetu, for example, has investigated how analytics can inform retailers’ innovation efforts. In his research, Hetu found that smart machines evaluating different algorithms will have the ability to make complex commercial decisions, such as merchandise assortment and distribution – meaning that future fashion trends could be not just recognised, but introduced by machines.

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Another potential application of Machine Experimentation™ is in the realm of airline pricing – for example, airlines can utilise this new method of analytics to intentionally vary flight prices in “challenge” pricing versus their current “champion” algorithms to test the impact of different algorithms at different times.

Most users know that high demand for airfare on blackout dates and peak travel days leads to surging costs.

However, by purposefully initiating changes in pricing for different flights in different locations at varying dates and times, companies may discover new patterns in demand that allow them to lock in on where, when, and with whom increased prices are most viable.

Expect analytics across industries to shift to this more proactive and relatively automated model of learning.

As more organisations move to enhance their analytics capabilities, those that are able to keep pace with the burgeoning trends and developments in this area will be best positioned to make critical, data-backed business decisions and boost their bottom lines.

Sourced by Anthony Bruce, CEO, APT