Woerner. Big Data: extending the business strategy toolbox
Business world is rapidly digitizing. Digitization creates challenges because for most companies it is
unevenly distributed throughout the organization: This uneven, disconnected investment makes it
difficult to consolidate and simplify the increasing amount of data that is one of the outcomes of
digitization. This in turn makes it more difficult to derive insight – and then proceed based on that
insight.
Much of big data, while seemingly valuable, does not fit into the recording, measurement, and
assessment systems that enterprises have built up to aid in enterprise decision making. And
constantly modified and volatile data doesn’t easily form into stable interpretable patterns,
confounding prediction. A focus on real-time data ‘undermines long-term planning, and reframes
the trade-offs between short-term and long-term decisions
However, big data will stay so companies need to find ways to deal with these challenges.
Improve the business model
Companies are using big data to resolve previously unanswerable burning questions in order to
refine and optimize business processes and decision making. In these cases, big data supports the
measurement and monitoring of strategy in novel ways by offering new data, insight, and action.
New data. companies mine data from sensors, social media etc to generate new insights
New insights. The program’s (example) success stemmed not from new big data, but from
new big data approaches and techniques that ranged from high-end statistics and models to
colourful visualizations of the output
New actions. As companies become well-armed with big data and proficient at making
insights based on that data, they act differently – often faster and more wisely
big data facilitates improvements to business models across industries. A commonality across the
best practice efforts we have studied is a clear strategic driver underlying the big data initiatives.
The most effective improvements result from creating well-articulated strategies that are informed
by data and then honed and shaped accordingly.
innovate the business model
Companies are using big data to find different ways to generate revenues. Two contemporary
approaches include data monetization and digital transformation. Digital transformation occurs
when companies leverage digitization to move into completely new industries or to create new
ones.
1. Data monetization. The act of exchanging information-based products and services for
legal tender or something of perceived equivalent value. Information-based products and
services can include a range of offerings that increase in complexity; capabilities and skills
requirements; and potential for financial returns: raw data, enhanced data, reporting and
analytics, process design, and process execution. Regardless of company type, data
monetization requires an organizational group or business unit dedicated to building the
requisite technical and business competencies appropriate for the scope of its monetization
business. A dedicated unit helps manage and govern the data monetization business model,
which is dissimilar from other organizational ventures.
a. companies are monetizing by selling, bartering, and wrapping. Wrapping refers to
wrapping information around other core products and services. Selling occurs when
companies receive money or some form of legal tender in exchange for information
offerings. Bartering occurs when companies choose to trade information in return
for new tools, services, or special deals. Companies are wrapping to differentiate a
core product or service offering by fulfilling some informational need, which in turn
makes the product or service more attractive to customers and generates greater
value.
, 2. Digital transformation. Digitization creates not just increased amounts of new and different
types of data, it also has the potential to blur company boundaries so that it becomes more
difficult to tell where a partner organization engagement begins and ends. We think the
result of increased digitization is the development of business ecosystems, coordinated
networks of companies, devices, and consumers that create value. Ecosystems offer more
choice, information, and value to consumers, and how much deep knowledge of the end
consumer a company currently has or in which it is willing to invest becomes a key
dimension for company strategy. Big data can enable companies to transform in ways that
propel them into new industries or ecosystems and alters traditional competitive
landscapes.
we do not believe that big data is necessarily an impediment to strategy. Instead, it offers rich,
exciting opportunities to leverage and extend a company’s business strategy toolbox. Like most
new technology opportunities, big data itself does not create strategic issues; generating business
value from big data is reliant upon the strategy makers and surrounding structures and processes.
,Gann, Cohn, Parmar. The new patterns of innovation
Search for new business ideas and models is hit or miss for most corporations. Are various reasons
for this failure. Managers who are skilled at executing clearly defined strategies are ill equipped for
out-of-the-box thinking. In addition, when good ideas do emerge, they’re often doomed because the
company is organized to support one way of doing business and doesn’t have the processes or
metrics to support a new one. When you tackle business innovation systematically, you improve
the odds of success. Are different ways to search for ideas competency based, customer
focused, changes in the business environment & complements the existing frameworks but
focuses on opportunities generated by the explosion in digital information and tools.
Explosion in digital data= more data available due to digitization,
better tools for data= Our capacity to integrate, analyse, and exploit structured data continues to
improve, and our ability to understand and learn from data has been transformed.
poses this question: How can we create value for customers using data and analytic tools we own
or could have access to?
Advances in IT facilitate the hunt for new business value in five distinct—but often overlapping—
patterns. Those patterns form the basis of our framework. None of the patterns depends on
bleeding-edge technology. Patterns
using data that physical objects now generate (or could generate) to improve a product or
service or create new business value.
o Because of advances in sensors, wireless communications, and big data, it’s now feasible
to gather and crunch enormous amounts of data in a variety of context. Those data can be
used to improve the design, operation, maintenance, and repair of assets or to enhance
how an activity is carried out. Such capabilities, in turn, can become the basis of new
services or new business models.
Digitizing physical assets
o As mobile technologies continue to fuel this trend, more creative businesses are tapping
into it and generating their own enhanced services or new business models. Digitized
versions of physical assets are transforming the way people operate in other industries as
well. improved design in many manufacturing industries by 3D printing. The management
of digitization itself could be a new business. As more assets become digitized, we expect
competitive advantage to shift. Digitization typically slashes distribution costs and makes
the ability to move physical inventory efficiently or secure favourable store locations less
critical. But you can expect that offering customers more choices and more tailored
service will become increasingly important. Going forward, we will see more players
explore ways to use the digital nature of the purchase process itself to strengthen
customer intimacy and transform the industry yet again.
Combining data within and across industries (realm of big data).
o The science of big data, along with new IT standards that allow enhanced data integration,
makes it possible to coordinate information across industries or sectors in new ways
o Similar opportunities can be found in the private sector. While some firms, such as
Walmart and Dell, have successfully integrated data across their supply chains, most
supply networks are relatively uncoordinated. Advances in IT could help address that
problem.
Trading data.
o company whose information is valuable to another company sells it. The ability to
combine disparate data sets allows companies to develop a variety of new offerings for
adjacent businesses.
Codifying a capability.
, o allows a company to take any process in which it is best. Ever since their invention, IT
systems have helped automate business processes. Now companies have a practical way
to take the processes they’ve perfected, standardize them, and sell them to other
parties. Any process that is best-in-class—but not central to a company’s competitive
advantage—can thus be turned into a profitable business. . Cloud computing has put such
opportunities within even closer reach, because it allows companies to easily distribute
software, simplify version control, and offer customers “pay as you go” pricing.
Combining the patterns
actual initiatives often encompass two or three of the patterns. In addition, what begins as a
relatively simple extension of an existing business often grows into a whole new business.
Getting started.
When we work with clients to uncover new business opportunities, we begin by describing the five
patterns, using one or two detailed examples, and then move right to questions designed to
inventory the raw material out of which new business value can be carved. The questions seem
simple, but answering them requires considerable thought in most cases. • What data do we have?
• What data can we access that we are not capturing? • What data could we create from our
products or operations? • What helpful data could we get from others? • What data do others have
that we could use in a joint initiative? Armed with the answers, the team cycles back through each
pattern to explore whether it, or perhaps a modification or combination of patterns, could be
applicable in the company’s business context.
Once we’ve worked our way through the second set of questions, the process looks pretty much as
you’d expect it to: The various ideas are collated and prioritized; generally one or two are tapped for
further investigation; subgroups are charged with fleshing out the ideas in more detail. They’re
asked to develop a scenario in which an idea creates significant new business value and to identify
the key assumptions that would need to hold true for that to happen.
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