Business "know-alls" - how big data changes the face of companies

Remember that school has always been a sort of "know-it-all"? Somehow, regardless of the subject, they were able to link the separate pieces of information in their heads and come to an understanding of the issue.

I gave this example because, in my opinion, it reflects well the future of companies: they have to become “know-it-alls” of business. Now, thanks to Hadoop and other technologies of the so-called Big Data, companies can, until recently, consider scattered information as a whole. Imagine what that might mean. Airlines will know when a customer, valuable to them, has encountered trouble at the time of departure, and, thanks to this, will try to improve service during the return flight. Physicians will be able to link disparate types of information, such as MRI results, pressure indicators, atrial fibrillation data to predict the possibility of a heart attack or stroke.
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It is not only about data volumes - and this is what most people think of when they mention Big Data. On the contrary, the main thing is that between these data - regardless of their type and source - extremely important relationships are hidden, such as, for example, between information from a call center, data on website usage and sales figures. For me, the difference in these approaches is significant. Simply put, size doesn't matter here.

And yet, after so many years of enthusiastic talk about Big Data, our focus has shifted to the fact that their main value is the ability to collect huge amounts of information. This kind of brainwashing reminds me of my childhood in Czechoslovakia, where, like in the Animal Farm, Orwell, we used to think that “four legs are good, two are bad.” I want to tell you that two legs are just fine [ given the plot of the “Stockyard”, the translator is not sure if there is hidden sarcasm in this passage - approx. trans. ], and in relation to large data, the term “large” is not always appropriate in this case. Much more significant is the ability to evaluate the data - whether it is the flow of information directly from the Internet or part of it that leaked through the firewall, sensor data or information from public sources - and then link them together into a single whole picture (as if the game “draw figures "was a masterpiece of painting). No less important is the fact that companies can then embed these knowledge-based knowledge into their processes, products and services.

In his book "The Rise of Analytics 3.0: How to Compete in the Data Economy," Tom Davenport ( Tom Davenport ) describes how companies begin to integrate analytics "into fully automated systems based on ranking algorithms or rules based on analytics. Others embed analytics in consumer-oriented products and features. ”

This is what it means to be who I call “a business that uses data in everything” - an enterprise where they know everything that is necessary and use this knowledge in their work.

We already have several examples of such companies:

• LinkedIn. Using public and private information about who knows whom, who likes whom and who works with whom, LinkedIn has become the dominant means of finding information about jobs, customers and candidates for job seekers and employers.
• AirBnB. The company's website will tell you who owns the living space that you are considering renting, whether the owner is a friend of someone you know on Facebook and who also liked this accommodation. As a result, users feel the transparency of the service operation scheme and the credibility of the company.
• Netflix Using data on user views, the company developed an algorithm that made movie recommendations 10% more accurate. Later, the company used the data to create its own content, which now competes in popularity with the best cable or network TV products.

In each of these cases, companies used insights, which appeared as a result of monitoring all available data types, and implemented them in their business. That's the whole difference in working with data. Instead of transferring only the main essence of insights to several analysts (in the spirit of big data), these companies constantly analyze the whole amount of information that they have in order to continue to make business decisions in real time.

Although most companies do not have such capacities, I believe that any business can become “a company that uses data in everything”, as long as its management is actively focused on using information and analytics as a sustainable competitive advantage. As Davenport writes in his book: “The most important feature of the era of Analytics 3.0 is that not only online companies, but literally any firms in any field of activity can be involved in data economics.”

UPS, for example, uses digital map data and telematics embedded in trucks to plan the best route for each of its 55,000 drivers. Progressive Insurance combines information about the credit rating of its customers with internal data to predict the likelihood of insured events. The real estate and equipment management company known to me, is now analyzing relevant public and private data for the past 12 years. Her goal is to predict the duration of periods of intense heat before air conditioners begin to fail.

Please note that each company correlates data from previously unrelated types. And they all include insights, based on the information received, in their activities, services, or products to predict behavior or direction. As Davenport writes, we have always had three types of analytics: descriptive, which characterizes the past, normative, which tells us what to do, and predictive, which uses data about the past to predict the future. "Analytics 3.0 includes all three types, but the focus is primarily on predictive analytics," he writes.

I can not disagree with him. I believe that the benefits of becoming a “business that uses data in everything” is both frightening and attractive. I also believe that companies that do not analyze all the information they have will cease to exist.

Where to begin?

• Decide to use information and analytics as a competitive advantage.
• Use in your work all the available information - data of internal systems, cloud applications, social environment, public sources and mechanically collected data.
• Integrate the results of analytical work, both normative and predictive, into your products and processes.
• Use SaaS applications and PaaS architecture as a means of controlling costs and increasing IT complexity.

"Know-it-alls" rule.

PS Would you like to ask the habrausers if you had to use Hadoop or other technologies, and if so, in what situations, did it bring real benefits? We will be glad to stories in the comments and in personal messages.

PPS If you notice a typo, mistake or inaccuracy of the translation - write a personal message, and we will quickly fix it.

Related Links:

• Virtualization: history and development trends
• By 2015, the Big Data area will create 1.9 million jobs in the US IT sector alone [Forbes]
• Windows and Linux virtual servers from 1cloud