,Introduction
Imagine a company. It could be an American manufacturer of home computers. Try to imagine,
too, all the things such a company must be able to do: purchasing from suppliers, assembling and
packaging the parts, preparing manuals and marketing plans, selling the products. The company
also has a large number of support functions. Someone must look after the well‐being of its
employees, new staff must be hired, people must be paid, the place must be cleaned, and a canteen
must work to feed everyone. There is an entire financial function, ensuring that the crediting and
debiting of banks, suppliers, owners, and customers runs smoothly. Finally, there are all the
planning processes related to product lines and customer groups on which the company has chosen
to focus.
Now imagine how much of this the company could outsource. Without too much effort, all
production could be moved to East Asia. That could probably bring huge advantages since
assembling computers is typically salary‐heavy and standardized production work. Others could
handle the logistic side of things. The company could get professionals to write and translate the
manuals. In addition, the company wouldn't need its own outlets; its products could be sold through
some of the major retail chains. Alternatively, a Web shop could be commissioned to create an
Internet site where customers could order the products they want. There is no real need for the
company to have its own warehouse for parts and computers, from their arrival to their delivery to
the customers. A lot of the support functions could be outsourced, too. Many companies outsource
the process of recruiting the right people. Routine tasks such as paying salaries, developing training
plans, and executing them in external courses could be outsourced, once the company has put the
routines in place. Cleaning, the running of the canteen, refilling vending machines, and mowing
grass are functions that are already, as a rule, outsourced by large IT companies.
By now, there is not much left of our company. We have removed all the functions that others can
do almost as well or, in some cases, even better. What we have left is what we call the company's
core competencies. These competencies are the things that the company is especially good at and
that can secure its survival in the future, provided it is capable of developing these so that they
continue to meet the requirements in the marketplace.
As shown in our example, core competencies have little to do with the physical world. Machinery,
warehouses, and distribution can be outsourced. A company's core competencies lie in knowing
how to handle internal processes, and knowing what customers want now and in the future. In
other words, the key is to have the right knowledge in the company. More specifically, what the
company needs is for the right people to have the right data and information at the right time. When
that happens, we have rational decision making that meets strategic, operational, and market
conditions. And this is exactly the first half of this book's business analytics (BA) definition:
Definition 1: Delivering the right decision support to the right people at the right time.
In this definition, we have chosen the term decision support, because BA gives you, the business
user, data, information, or knowledge, that you can choose to act upon or not. Here's a familiar
example: An analysis of check‐out receipts can inform the manager of a 7‐Eleven store which
,products are often purchased together, thus providing the necessary decision support to guide the
placement of goods on the shelves to increase cross‐selling.
There is a saying that “people don't buy drills; they buy holes,” and this definition of BA points
out that “people don't buy servers, pivot tables, and algorithms; they buy the ability to execute,
monitor and control their business processes, along with insights about how to improve them.”
Regardless of whether predictive models or forecasting is used, it's the historical information that
can give 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.
Imagine a company. It could be an American manufacturer of home computers. Try to imagine,
too, all the things such a company must be able to do: purchasing from suppliers, assembling and
packaging the parts, preparing manuals and marketing plans, selling the products. The company
also has a large number of support functions. Someone must look after the well‐being of its
employees, new staff must be hired, people must be paid, the place must be cleaned, and a canteen
must work to feed everyone. There is an entire financial function, ensuring that the crediting and
debiting of banks, suppliers, owners, and customers runs smoothly. Finally, there are all the
planning processes related to product lines and customer groups on which the company has chosen
to focus.
Now imagine how much of this the company could outsource. Without too much effort, all
production could be moved to East Asia. That could probably bring huge advantages since
assembling computers is typically salary‐heavy and standardized production work. Others could
handle the logistic side of things. The company could get professionals to write and translate the
manuals. In addition, the company wouldn't need its own outlets; its products could be sold through
some of the major retail chains. Alternatively, a Web shop could be commissioned to create an
Internet site where customers could order the products they want. There is no real need for the
company to have its own warehouse for parts and computers, from their arrival to their delivery to
the customers. A lot of the support functions could be outsourced, too. Many companies outsource
the process of recruiting the right people. Routine tasks such as paying salaries, developing training
plans, and executing them in external courses could be outsourced, once the company has put the
routines in place. Cleaning, the running of the canteen, refilling vending machines, and mowing
grass are functions that are already, as a rule, outsourced by large IT companies.
By now, there is not much left of our company. We have removed all the functions that others can
do almost as well or, in some cases, even better. What we have left is what we call the company's
core competencies. These competencies are the things that the company is especially good at and
that can secure its survival in the future, provided it is capable of developing these so that they
continue to meet the requirements in the marketplace.
As shown in our example, core competencies have little to do with the physical world. Machinery,
warehouses, and distribution can be outsourced. A company's core competencies lie in knowing
how to handle internal processes, and knowing what customers want now and in the future. In
other words, the key is to have the right knowledge in the company. More specifically, what the
company needs is for the right people to have the right data and information at the right time. When
that happens, we have rational decision making that meets strategic, operational, and market
conditions. And this is exactly the first half of this book's business analytics (BA) definition:
Definition 1: Delivering the right decision support to the right people at the right time.
In this definition, we have chosen the term decision support, because BA gives you, the business
user, data, information, or knowledge, that you can choose to act upon or not. Here's a familiar
example: An analysis of check‐out receipts can inform the manager of a 7‐Eleven store which
,products are often purchased together, thus providing the necessary decision support to guide the
placement of goods on the shelves to increase cross‐selling.
There is a saying that “people don't buy drills; they buy holes,” and this definition of BA points
out that “people don't buy servers, pivot tables, and algorithms; they buy the ability to execute,
monitor and control their business processes, along with insights about how to improve them.”
Regardless of whether predictive models or forecasting is used, it's the historical information that
can give 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.
