Business Analytics for Managers. Thorlund Jesper
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companies a status on the situation they are in right now. Maybe the company's analysts and their scenario models can present different alternatives, but ultimately it's the responsibility of the decision makers to choose which business processes they want to alter or initiate based on decision support. BA is about improving the business's basis for decision making and its operational processes, as well as achieving the competitiveness possible when a business is in possession of relevant facts and knows how to use them. In our work as consultants, we have too often experienced BA as purely an IT discipline, primarily driven by the organization's technical environment, which results in BA initiatives floating aimlessly. Successful BA initiatives are always closely interlinked with the organization's strategy (mission, vision, and goals) and are put in place to strengthen the ability of business processes to move in the right direction toward business objectives. Unfortunately, these points are often overlooked, which is one of the reasons for this book.
Over the last ten years, BA has, however, undergone some major developments, which means the definition of BA must be redefined. One big change has been labeled big data. This term is coined to describe the phenomenon of increasing amounts and variability of data – including formats like images, videos, and audio files. But the fact that the volume, variance, and velocity of available data have gone up is still covered by the above definition. Neither do new technologies, such as in‐memory prestored calculations or the increasing use of clouding solutions (where software and data are not hosted at the user location), call for a new definition of BA.
What does call for a new definition of BA is not really the huge volume of data and the new software to store and process it, but the intensified use of analytical models to control operational processes in an intelligent way. We might say that artificial intelligence is beginning to make decisions in the digital area. Here are some examples:
• Pure digital processes like omnichannel marketing, where customer communication is send directly to the customers based on what the customer most likely wants from a specific electronic channel. Think of last‐minute‐offers from Booking.com. Also the automated investment programs based on algorithms that day trade shares and currencies automatically. Off course, the most successful investor will be the one using the best algorithm.
• Semiphysical digitalized business processes, such as when analytics are used to predict future market demand and automatically reorder new stock for inventories based on, for example, season, campaigns, market growth, or price levels. Again, in this case, the market winner will be the company that runs its digital processes based on the most well‐configured algorithms. The Internet of Things is another new term, describing how physical assets can coordinate their actions based on more or less complex algorithms. For example, there are milking cattle farms where the cows are almost entirely served by robots; humans are only called upon when needed to do services such as make decisions about replacing cows, treat detected illness among cows, repair or maintain the machines, or fill and empty inventories.
• Fully physical digitalized processes, like robots in the forms of automated cars and vacuum cleaners that respond to external physical input based on algorithms. Soon, these robots must be able to respond based on algorithms that handle voice, face and tone recognition, next to understanding the nonhuman physical environment they are acting in.
Over the last ten years, a huge amount of processes have been automated and digitalized, and the manual decisions that come with these processes have vanished. In many ways, what we see now is what people expected to see during the dot‐com era, which was all about the opportunities of new automated digitalized business process that allowed organizations to compete globally based on extremely scalable business models. Back in these early days, market disrupters like Amazon.com redefined how books were sold on the Internet. Later on, Apple and Kindle started to produce physical devices to increase people's experience of consuming books, music, and movies via the Internet. Now we are at a point where market disrupters can operate across all platforms based solely on apps. Some of the most spoken‐about market disrupters in 2016 are social media providers or the taxi service provider Uber.
Less noticed by the public, it is evident that physical production processes are being increasingly digitalized and intelligent. However, we are still waiting for the robots that can serve us intelligently in our private homes to have their breakthrough.
During the last ten years an increasing amount of business processes have been digitalized to the degree that the next competitor only is an app away. The market‐winning app is often the one that provides the best user experience based on intuitive user‐centric design, customer‐made data feeds, advanced analytics providing relevant suggestions, and the ability to store the relevant user history. Examples could be suggested friends on LinkedIn or Facebook, good offers and purchase tracking in virtual stores, banks, airline companies, or other service providers.
Because BA is increasingly applied and automated in digital processes, BA today is also much more than providing decision supports to humans within an organization, it is also about the provisioning of data to drive digitalized processes in an intelligent way.
This gives us this updated and final definition of BA:
Definition 2: Delivering the right decision support to the right people and digital processes at the right time.
This current intensified digitalization of business processes also means that although ten years ago we had to argue for the relevance of analytics, today we only discuss where analytics can be used efficiently as market challengers are constantly moving forward causing the extinction of “infosauric” companies – organizations that fail to see the direct linkage between analytical ability and competitive position.
It's quite easy to imagine a bank that runs all its customer processes and dialogue programs entirely without using IT – and what really hard work that would be. The point is, of course, that we can have BA without deploying software and IT solutions; at a basic level, that has been done for centuries. However, today it just wouldn't stack up. In this book, we look at BA as information systems consisting of three elements:
1. The information systems contain a technological element, which will typically be IT‐based, but which in principle could be anything from papyrus scrolls and yellow sticky notes to clever heads with good memories. A characteristic of the technological element is that it can be used to collect, store, and deliver information. In the real world, we're almost always talking about electronic data, which can be collected, merged, and stored for analysts or the so‐called front‐end systems that will deliver information to end users. A front end is the visual presentation of information and data to a user. This can be a sales report in HTML format or graphs in a spreadsheet. A front‐end system is thus a whole system of visual presentations and data.
2. Human competencies form part of the information systems, too. Someone must be able to retrieve data and deliver it as information in, for instance, a front‐end system, and analysts must know how to generate knowledge targeted toward specific decision processes. Even more important is human decision support: those who make these decisions and those who potentially should change their behavior or the configuration of processes based on the decision support are people who must be able to grasp the decision support handed to them.
3. Finally, the information systems must contain specific business processes that make use of the information or the new knowledge. A business process could be the way inventory is optimized or products are priced. After all, if the organization is not going to make use of the created information, there's no reason to invest in a data warehouse, a central storage facility that combines and optimizes the organization's data for business use.
The considerable investment required to establish a data warehouse must render a positive return for the organization through improved organization‐wide decision making and enabling of digital processes. If this doesn't happen, a data warehouse is nothing but a cost that should never have been incurred. An information system is therefore both a facility (for instance a data warehouse, which can store information) as well as a set of competencies that can retrieve and place this information in the right procedural context.
When working with BA, it