Data analytics has failed… So far.

Will the era of data-driven applications finally deliver on the data analytics promise?

The promise of data analytics is to capture, analyze and present data that generate insights for businesses so they can make data driven decisions and take action.  With timely and easily understood data, businesses can capture opportunities as well as mitigate risks and threats.

As with all opportunities, risks and threats, the window to respond effectively is narrow and in our fast paced world these windows get narrower every day.

So far, data analytics latency – the time it takes to capture data, process it, take a decision and act – is longer than the window of opportunity is open. This is mainly due to the time it takes human beings to gain insights and derive and execute an action plan. The result? Missed business opportunities. To better understand this, let’s look at the evolution of data analytics.

The evolution of data analytics

The “ business intelligence” era (aka Analytics 1.0)

Analytics 1.0 was primarily a store-and-retrieve paradigm for reporting purposes, enabling enterprises to begin to optimize location of goods and services, segment their customer base and enhance financial management of businesses. Implementing Analytics 1.0 usually cost millions of dollars, was designed for a few key users (mainly executives) and was done by specialized consulting companies, making it too costly to integrate the analytics software into the operational business lines of a company. The latency from capturing data to taking an action was usually months.

The “big data” era (aka Analytics 2.0)

If Analytics 1.0 was a store-and-retrieve paradigm, then 2.0 is driven by handling the 3 V’s of Big Data (Volume, Velocity and Variety) primarily through software that can massively scale, such as MapReduce for data sets and NoSQL for databases. The Analytics 2.0 stack is largely a technology replacement wave, where the stack remains the same and each technology component is replaced with a newer, cheaper, faster more powerful version of its former self that can handle the volume, velocity and variety of today’s generated data. The latency from capturing data to taking an action is usually weeks.

The “data-driven applications” era (aka Analytics 3.0)

Although many anticipate that we are on the verge of a shift to Analytics 3.0 there are different ideas on what exactly 3.0 might be. Some call it Operational Analytics, others call it Data-driven Applications or Embedded Analytics.

In all cases, the common denominator for describing Analytics 3.0 is the ability to sense and react in real-time to events that impact customers, machines and devices.

The future of data analytics

Analytics 3.0 is believed to finally realize what data analytics promised but has yet to deliver and that is to help humans and machines make data-driven decisions in real-time and therefore capture business opportunities in time.

There is evidence that this time around the promise will be kept. It is simple and affordable to build data-driven applications. Open source, infrastructure as a service and enormous achievements in data science are key trends to power the development of data-driven applications but the biggest factor is the democratization of data.

Not only is more data generated like never before in the history of humankind, this data, to a large extent, is being made available to everyone.

So as data analytics races towards finally fulfilling its promise, businesses large and small will be able to start evaluating and investing in data driven applications that will shed light on their most urgent business opportunities, risks and threats.

Joav has overall responsibility for business and product strategy as well as software development and…


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