Business analytics (BA) is not just moving fast; it's also in the process of developing from conventional BA to pervasive analytics, which equips everyone in the organization and in the private sphere at all levels with real‐time analyses, alerts, and feedback mechanisms. It's a paradigm shift with potentially huge advantages and far‐reaching cultural significance—and it's happening already.

Instead of just measuring business results after they've been achieved, which is the primary role of BA today, the next generation of pervasive BA will advise and drive the business forward with an arsenal of analyses and tools for real‐time decision making. These will be delivered with a view to improving earning power and efficiency, and they will be delivered to people in all corners of the organization and even outside it.

Pervasive business analytics can be explained as omnipresent information technology (IT). And that means that IT will circle, inform, and advise everyone at all times, wherever people are. And we won't always realize when it's happening.

One example is General Motors' OnStar system. In this package, the typical GPS navigation system for cars is extended with an information and convenience service in a “pervasive” way. The customer service center at General Motors knows the real‐time location of the car and can perform cross‐reference searches in an underlying database to interesting places along the way, such as hotels, restaurants, and so forth. “Would you like to be directed from your current position to the nearest cash point, airport, or a room at your preferred hotel chain?” OnStar's underlying data warehouse has the information and can deliver this in real‐time to our General Motors cars as a service. And this scenario isn't even the future. The only futuristic element is that soon, we will not drive the car anymore; it will be self‐driving. Also, the car is not likely to be ours, since the self‐driving units simply would be a service that we subscribe to.

In the Introduction, we defined BA as: Delivering the right decision support to the right people and digital processes at the right time.

We believe this definition will continue to be true into the future and that BA will continue to develop in all three dimensions that are part of the concept.

As far as the delivery of the right decision support is concerned, there's hardly any doubt that the quality of the decision support delivered by BA will become increasingly complex and precise. We anticipate, for instance, that BA solutions will not just identify which customers are going to leave, when, and why, but that these solutions will also be able to engage with the customer in a intelligent way through interactive speech, individualized value proposition development, and services. We expect that when a key performance indicator (KPI) is below its defined standard, the owner will not only be alerted to this fact, but will also receive recommendations on what to do about it—preferably as early as when the system is forecasting foreseeable problems. Similarly, we anticipate that employees will not merely be receiving e‐mails in the course of their working day, but that these e‐mails will be prioritized and structured in relation to the tasks that must be performed on a given day.

Another change in the near future is how the decision support will be brought to us. We are seeing new gadgets like glasses, mobile phones, and watches that deliver the information to us in ways so intuitive that the gadgets are becoming more and more an extension of our body. Soon hearables will also be a gadget to consider, since some information is easier to convey via sound that vision. Next steps would be to hide the hearables inside the ear or perhaps simply operate in a place where they can be docked.

We see that gadgets becomes smaller and smaller—so why not surgically implant sensors into the body that can tell us about the condition of our health? Who would not want to know right away when the first cancer cell mutates, or when to take insulin, or how to diet in order not to take insulin in the first place. The gadget may order the right food to be delivered to our refrigerator on the fly and track whether we consume it. However, in a potentially negative sense, this may push the responsibility of our health onto ourselves as individuals, as gadgets will continuously give us decision support on what to do. Consider: why would an employer pay for an employee's self‐inflicted illness caused by poor food habits and a lazy lifestyle? Certainly life insurance companies would give discounts to persons who can continuously document a healthy lifestyle, for starters.

With regard to the right people and digital processes, we'll be seeing some major changes in the near future, to some extent because BA solutions must include users' preferred way of making decisions when information is distributed. Is it, for instance, a team of specialists who make decentralized decisions, or is it a consensus‐driven decision culture surrounding the business process we're informing? An even more important trend will be that BA information will not only be supporting the optimization of business processes, but also be supporting the optimization of individual behavior in the organization. Employees thereby become business processes in themselves, since their behavior will now be a target for optimization. The previous examples about when to read which e‐mails and the use of health analytics illustrate this perfectly. If the local network registers that an employee arrives at his office with an important meeting in five minutes, the employee should be informed of important e‐mails only. The rest must wait until the 20‐minute break after the meeting. Similarly, a truck driver with an upcoming meal break may be advised about where to find a good place that serves well‐made and healthful food at a reasonable price, so that he can stick to both his budget and his diet. Finally, let's imagine a busy businessman who, regardless of when he emerges from his meeting, is informed of which flight or train connection is the fastest in relation to his preferred way of traveling—allowing for the time he needs to buy a wedding anniversary bouquet for his wife on his way home. The ordering of ticket and flowers happens automatically, of course.

The third element in our definition of BA is the right time. Here we anticipate that BA solutions will increasingly send information to users whenever it's relevant, rather than storing information for when users choose to read the reports. This means that BA solutions, in connection with the monitoring of business processes, will be sending alerts to the people who are responsible the minute these processes deviate from their defined standards. The advantage of this form of real‐time advice in terms of process deviations is that decision makers can focus on the processes that need correcting on short notice. This will reduce the waste represented by a process that has more or less run off the track; additionally, it delivers scope for possible savings in connection with the number of employees needed to monitor processes. They no longer need to spend time looking out for problems, as the needle in the haystack calls them when it has something of relevance to report. When problems need solving—and hopefully well before, based on forecasting—the employees will be notified that the issue exists or even that it already has been solved.

In the future, we'll therefore see the information wheel used not only for business processes, but also for the individuals in the organization, too, as illustrated by Exhibit 9.1. We'll also see information wheels turning faster—that is, the time between a new information need presenting itself and the delivery of new information will be reduced. It's perfectly realistic to imagine that every time a user accepts an action suggested by a BA solution, the underlying information wheel will pick this up. Similarly, if a user dismisses information as irrelevant, this would mean the information is automatically downgraded accordingly in the information wheel. This scenario is actually not new; this is exactly the thinking behind the development of neural networks decades ago. Neural networks are self‐learning algorithms that continually adapt to the environment they're in, just like the human brain—thus the name. The new thing here is that the user of the network is not forced to sit next to a supercomputer, but can move about freely and interactively train his or her own information wheel on dimensions such as his or her preferred way of traveling, e‐mail behavior, meeting behavior, eating behavior, coffee behavior, leisure time behavior—dimensions that we imagine users to begin with might turn on and off, but that they will later have turned on all the time because the information wheel efficiently supports and creates the user's lifestyle. This progression in thinking is on par with our use of the mobile phone, which we no longer turn off at night or when we're off work, even if we all used to swear that that's what we would be doing. It is even on par, for that matter, with our use of the automobile, which stank and was noisy and which my grandmother used to refuse the right to overtake her when she walked down the street, arguing that “the drivers of automobiles did not have the authorization to run her over.” This is the same vehicle that we now look at as an opportunity that both creates and supports our contemporary lifestyle.

Just as the industrial era changed people's daily behavior, the information age will change ours. At first we will object, then hesitate, and then adopt the changes without noticing them. Don't believe it? Just remember that a majority opposed building the Eiffel Tower in Paris. In the same way, the information age will offer us a freedom that will make us feel uncomfortable at first, but which we will come to adopt and allow to shape our lives.

We can buy books and a wide assortment of other products online on Amazon.com. If we inquire about items that have been discontinued or are not in stock, the system will suggest other items that might be of interest to us. The “pervasive” element is present here in the shape of a semi‐intelligent search and guidance as we navigate the Web site.

In the near future, you may be on your way to the airport in a taxi. You will receive an alert on your mobile device saying that you will be late for your flight, but there is another departure at 8:20 ∼PM with an available seat on Economy Flex. Do you want to be booked in? Alternatively, a train departing at 7:30 ∼PM has one available seat on Business. Do you want to be booked in? The system recommends the train option at 7:30 ∼PM, because flying increases your stress level, as registered in your personal data bank. No doubt, some people will not like the idea of a future with IT information and guidance interfering with their lives all the time. Many might say that such a future is scary and will add stress to our everyday life. But is this true? Is it not less stressful to avoid arriving at the airport to find that you've missed your flight than to be advised in advance and have time to change your travel plans?

A classic example of pervasive BA, which we may experience in the near future, is the computer HAL 9000 in Stanley Kubrick's film 2001: A Space Odyssey. The intelligent computer sees everything, monitors everything, analyzes everything, controls everything on board the expedition, and is omnipresent. The astronauts are being given lead and lag information about potential problems, and are advised on big as well as small things.

One of the future challenges of analytics is also to align all the information wheels of the organization. Today, BA is typically deployed only on a per‐process level. This means that pricing analytics run independently of loyalty analytics and logistical analytics. In the future, business will have to become better at connecting the individual analytical disciplines. A concrete example could be that if a customer has low loyalty based on some negative customer experiences, the logistical system will prioritize the speed of the future deliveries to this customer and the customers network, while at the same time providing the customer with some good offers.

Even more to the point, BA will be important for large companies looking for scale advantages in asset‐heavy industries like, for example, containerized shipping, where millions of containers must be at the right location at the right time, and hundreds of ships must sail with the right speed in the correctly designed network. Additionally the containers must be packed correctly on the ships so they can, as quickly as possible, be landed at the correct ports while the ship constantly remains in balance. At the same time, the right prices must be offered per ship departure and destination for all types of containers and commodities the customers wish to ship. Next to this, we have customer service processes performing booking and documentation, which must be done before the containers can even be sent. On top of it all, a shipping company has to constantly make accommodations for disturbances caused by storms, strikes, piracy, engine and system failures, constant price changes, and late deliveries from customers and partners in a industry with a profit margin of only a few percent! Particularly in these cases, the ability to holistically optimize the entire business model could bring unique competitive advantages. But today, we are still struggling with being able to optimize on a per‐process level.

Speech analytics is also an area to watch in the near future. Today, however, even though voice commands are possible, we still typically prefer to control our phones and television via graphical user interfaces and remote controls. But robots are already taking over larger and larger parts of the customer service functions as analytics becomes better and better at making meaning out of written and spoken sentences and coming up with relevant replies. It is, for example, interesting to note that people in an experiment who chat with people and with robots have difficulty in deciding which conversation was with a person and which with a robot, if the robots throw in some jokes.

The way to train these robots is by giving them a large set of possible responses and training them in understanding when these responses should be provided—which is also how the Watson computer that won in Jeopardy was developed. Soon, however we will also, as private persons, be able to train our own alter egos to take over routine tasks like responding to simple requests or ordering food in the supermarket. One could imagine that every evening, instead of, for example, writing in our diaries, we will respond to questions prompted by our digital alter egos so they also can better act on our behalf when it comes to phone calls and more complex investment decisions.

Over time, we can perhaps even expect our digital alter egos to adapt to how we respond during various emotional states and learn our ways of being creative and approaching dialogue. This method of training digital alter egos could already be applied today at home, treating people with memory loss conditions such as Alzheimer's Disease; it might be able help these people cling to who they are.

Finally, try to imagine how our great‐grandchildren, one hundred years from now, will be able to have a dialogue with our digital alter egos based on all the digital traces we are currently leaving on social media. So at least for our digital alter egos, heaven will be a cloud solution on earth.