Tag Archives: real time analytics

Four Myths About Real Time Analytics

Analytics-Now

Five years ago, businesses beyond the e-commerce world were only dipping their toes into real time analytics. Today, brick and mortar businesses have embraced real time analytics in a range of applications, including error detection, price adjustment, inventory tracking, customer experience management, and more.

So what has changed? Certainly, advances in both hardware and software have helped. But at the heart of this reassessment is the data industry’s ability to dispel a number of myths surrounding real time data processing and analytics.

Here are four of the most deeply entrenched:

  1. Real time intelligence is only a nice-to-have

 A common misunderstanding about real time analytics is that it is the same as batch analytics, just conducted over a shorter time frame. Therefore, as long as important business reports are meeting required deadlines, intelligence in real time is regarded as less than critical.

In fact, in contrast to the analytics conducted on batch (ie static) data, real time analytics is not designed to diagnose key business trends. Rather, real time analytics is performed continually on streamed data in order to detect unusual events as they happen, and to trigger an immediate or quick response.

As such, real time analytics is best suited for processes which stream large volumes of data. Where the analysed data is signalling something amiss, fast action – usually dictated by pre-set business rules – can prevent it from escalating into a full blown problem.

For this reason, real time analytics’ greatest value is its ability to avert losses, for example, by detecting financial fraud or manufacturing defects. There is also the potential to improve routine operational efficiencies, such as through retail inventory management or telecoms network optimisation. Real time analytics is therefore deemed to deliver “operational intelligence”, and is complementary to, rather than a substitute for, “business intelligence”.

  1. The prices of my products/services do not change very often

Businesses are increasingly using real time analytics for another innovation – real time pricing. E-commerce sites have long used real time factors and complex algorithms to drive active price adjustments. What is perhaps less well known is the magnitude and velocity of these adjustments. Amazon, for example, changes the prices of about 40 million products many times during a single day, according to Internet Retailer Magazine.

The confidence that e-commerce sites have in their pricing models is now reflected in many traditional businesses. A report in the Wall Street Journal noted that, for example, adult passes to Indiana Zoo can range between USD 8 and 30, by responding in real time to weather changes, school group bookings, surprise closure of an attraction, and a variety of other factors.

Many other facilities now adopt similar real time pricing mechanisms, including highway tolls, parking lots, taxi services, golf courses, ski resorts, theme parks, and entertainment events. In the case of highway tolls, prices can fluctuate by as much as 500% in a single week. This real time approach to pricing is said to be capable of lifting revenues by 10 to 20%.

However, real time pricing offer benefits beyond that of higher revenues. Bottom line consistency and customer experience are often as important, with dynamic price adjustments used to control demand peaks and troughs, and associated resourcing costs. The question traditional businesses need to ask themselves is therefore not ‘How often should we review our prices?’ but rather ‘How can we approach pricing differently?’

  1. Real time information is unsubstantiated and therefore less valuable 

The term “real time” gives the impression of fleeting observations that are not verifiable. However, in most cases, real time analytics is not simply based on what has just occurred. Rather, such analyses involves correlations between real time and historical datasets, thereby placing a real time event in its proper context.

Take for example how a GPS now re-routes vehicles in order to avoid traffic congestion. The analyses required to do this is based not just on real time traffic information, but on the correlation with previous congestion events, in order to determine the best alternative routes that vehicles should take.

It is the ability to combine streamed real time data with historical data and data from other sources that gives real time analytics its greatest potency.

  1. Real time analytics is too expensive to implement

 Real time analytics is not without its complexities, but does not necessarily require a large business investment. This is because the evolution of open-source data technology has resulted in significantly more cost-efficient real time software. Spark, for example, is an open source project that is set to underpin much of the industry’s real time analytics capabilities.

In addition, rather counter-intuitively, the data architecture stack required to support the integration of real time feeds with existing batch based data processes can actually be a cheaper alternative to current storage technologies.

This is because the architecture needed to run real time analytics eliminates existing storage inefficiencies. Adoption of these stacks, which is entirely reliant on open source platforms, enables analytics to be performed incrementally and in memory, while also avoiding the need for large hardware investments.

Today, there are few excuses left for businesses not to adopt real time analytical capabilities. While the insights generated by analysing historical data can help businesses arrive at strategic decisions, real time analytics produce a different kind of insight. They are the triggers that enable business to tweak processes and refine experiences.