Know-it-alls from business - how big data changes the face of companies

Remember, at school there were always such “know-it-alls”? Somehow, regardless of the subject, they managed to link disparate blocks of information in their heads and come to an understanding of the issue.

I cited this example because, in my opinion, it reflects the future of companies well: they have to become “know-it-alls” from business. Now, thanks to Hadoop and other technologies of the so-called Big Data, companies can, until recently, view disparate information as a whole. Imagine what this can mean. Airlines will know when their valuable customer was in trouble at the time of departure, and therefore will try to improve service during the return flight. Physicians will be able to link disparate types of information, such as MRI results, blood pressure, and atrial fibrillation data to predict the possibility of a heart attack or stroke.

This is not just about the amount of data - it 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, information from the call center, data on website use 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 gigantic amounts of information. This brainwashing reminds me of my childhood in Czechoslovakia, where we, as in Orwell’s Animal Farm, are used to thinking that “four legs is good, two are bad.” I want to tell you that two legs is just fine [ considering the plot of the Animal Farm, the translator is not sure if there is hidden sarcasm in this passage - approx. perev.], and with respect to big data, that the term "large" in this case is not always appropriate. Much more significant is the ability to evaluate the data - whether it is information flow directly from the Internet or part of it that leaked through the firewall, sensor data or information from public sources - and then link them into a single whole picture (as if as a result of the game “draw a picture using figures ”turned out to be a masterpiece of painting). It is equally important that then companies can embed this knowledge obtained on the basis of data in their processes, products and services.

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

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

We already have several examples of such companies:

• LinkedIn Using public and private information about who knows who, who likes and who works with whom, LinkedIn has become the dominant means of finding job, client and candidate information for job seekers and employers.
• AirBnB The company’s website will tell you who owns the housing that you are considering renting, whether the owner is a friend of your Facebook friends and who also liked this accommodation. As a result, users feel the transparency of the service work scheme and trust in 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 that appeared as a result of monitoring all available data types and embedded them in their business. This is the whole difference in working with data. Instead of passing only the main essence of insights to several analysts (in the spirit of big data), these companies constantly analyze the entire amount of information they have in order to continue to make business decisions in real time.

Although most companies do not have such capabilities, I believe that any business can become a “company that uses data in everything”, as long as its management is actively focused on the use of 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 the data economy.”

UPS, for example, uses digital map data and telematics systems built into trucks to plan the best route for each of its 55,000 drivers. Progressive Insurance combines its customer credit rating information with internal data to predict the likelihood of insured events. The property and equipment management company I know is now analyzing relevant public and private data for the past 12 years. Its purpose is to predict the duration of periods of extreme heat before air conditioners begin to fail.

Note that each company correlates data from previously unrelated types. And they all incorporate insights based on information received into their activities, services, or products to predict behavior or direction. As Davenport writes, we 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” are 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 available information - data from internal systems, cloud applications, social environment, public sources and mechanically collected data.
• Integrate analytical results, 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 We would like to ask the users of the site if you had to use Hadoop or other technologies, and if so, in what situations, did you really benefit? We will be glad to hear stories in the comments and in private messages.

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

Related links:

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