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Summary of ALL the articles for BIA 2020
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This is a summary of all the required articles for the course Business Intelligence & Analytics. It is 100 pages but it is a thorough summary!
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Articles Summary BIALecture 1
Woerner S.L., & Wixom B.H. (2015). Big data: extending the business strategy toolbox
Companies are becoming more digitized as they use sensors, mobile devices, video streams etc. to
predict needs, avert fraud and waste, understand relationships and connect with stakeholders both
internal and external to the firm. But digitization creates challenges because for most companies it is
unevenly distributed throughout the organization. This makes it difficult to consolidate and simplify
the increasing amount of data that is one of the outcomes of digitization.
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. Companies are using big data to resolve previously unanswerable burning
questions in order to refine and optimize business processes and decision making.
There are two cases where companies are not replacing their business strategy toolboxes, but rather
are using existing toolboxes more effectively which allows them to have access to essential data
needed to solve problems or gain insights that was not possible to collect before.
1. Improving the business model
New data to understand customer preferences and behavior, companies embed sensors
in their products to track actual product usage and they mine social media.
New insight new big data approaches and techniques ranging from high-end statistics
and models to colorful visualizations of the output.
New action companies are becoming well-armed with big data, they now act differently;
faster and more wisely.
The most effective improvements result from creating well-articulated strategies that are informed
by data and then honed and shaped accordingly.
2. Innovating the business model
Data monetization monetization; the act of exchanging information-based products and
services for legal tender or something of perceived equivalent value. Information-based
products and services e.g. capabilities and skills requirements; and potential for financial
returns: raw data, enhanced data, reporting and analytics, process design, and process
execution. We can monetize by:
Wrapping: wrapping information around other core products and services. 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.
Selling: when companies receive money or legal tender in exchange for information
offerings
Bartering: when companies choose to trade information in return for new tools, services
or special deals (in retail industry)
Digital transformation when companies leverage digitization to move into completely
new industries or create new ones. It has the potential to blur company boundaries so that it
becomes more difficult to tell where a partner organization engagement begins and ends.
1
, LaValle, S., Hopkins, M. Lesser, E., Shockley, R. & Kruschwitz, N. (2010). Analytics: The New Path to
Value
Executive summary
Among the top-line survey findings:
Top-performing companies are three times more likely than lower performance to be
sophisticated users of analytics.
The two top barriers of using “big data” are the lack of understanding of how to use
analytics to improve the business and lack of management bandwidth
New technologies are collecting more data than ever before, yet many organizations are still looking
for better ways to obtain value from their data and compete in the marketplace. Sources of pressure
to use big data are innovating to achieve competitive differentiation and when their organization
has more data than it can use effectively. Executives now want business scenarios and simulations
that provide immediate guidance on the best actions to take when disruptions occur, and they want
to understand optimal solutions based on complex business parameters. These expectations can be
met, but with a caveat for analytics-driven insights to be consumed (to trigger new actions across
the organizations) they must be closely linked to business strategy, easy for end users to understand
and embedded into organizational processes in order to take action at the right time.
Top Performers Say Analytics Is a Differentiator
Top-performers organizations who substantially outperform industry peers. Most top-performers
agreed that the use of business information and analytics differentiates them within their industry.
They are twice as likely to use analytics to guide future strategies and twice as likely to use insights
to guide day-to-day operations.
Three Levels of Capabilities Emerged, Each with Distinct Opportunities
Organizations that know where they are in terms of analytics adoption are better prepared to turn
challenges into opportunities. There are three levels of analytics capability:
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, 1. Aspirational the farthest from achieving their desired analytical goals, focusing on
efficiency or automation of existing processes, searching for ways to cut costs.
2. Experienced gained some analytical experience, looking to go beyond cost management,
developing ways to effectively collect, incorporate and act on analytics.
3. Transformed substantial experience using analytics across a broad range of functions,
analytics as a competitive differentiator, less focused on cutting costs. Most focused on
driving customer profitability and making targeted investments in niche analytics.
Data Is Not the Biggest Obstacle
Leading obstacle to widespread analytics adoption is lack of understanding of how to use
analytics to improve the business. Organizations that use analytics to tackle their biggest challenges
are able to overcome cultural challenges and refine their data.
Information Must Become Easier to Understand and Act Upon
New tools like data visualization, process simulation, voice and text analytics and social media
analytics can make insights easier to understand and to act on at every point in the organization.
They transform numbers into information.
What Leaders Can Do to Make Analytics Pay off – A New Methodology
Each of the following recommendations present different pieces of the information-and-analytics
puzzle, each one of them meets all of these three critical management needs:
Reduced time to value
Increased likelihood of transformation that’s both significant and enduring
Greater focus on achievable steps
Recommendation 1: “Focus on the Biggest and Highest Value Opportunities”
With a potential big reward in sight, a significant effort is easier to justify, and people across
functions and levels are better able to support it. A sharp focus on major opportunities can excite an
organization with new possibilities. But don’t start doing analytics without strategic business
direction, that will waste resources and risks creating widespread skepticism about the real value of
analytics. Moreover, the inability to understand how analytics can solve business challenges is the
most daunting obstacle to adoption. The
single greatest opportunity and challenge
to speed adoption of analytics is to embed
them into daily operations. The process-
application-data-insight-embed
technique (PADIE): a simple means by
which a company can operationalize
insights drawn from data. It’s a three step
process:
1. Document existing processes and
applications
2. Use analytics techniques
(descriptive, predictive,
prescriptive) to gain insights from
data
3
, 3. Select the most appropriate approaches to embed insights into your operations
Recommendation 2: “Within Each Opportunity, Start With Questions, Not Data”
Traditional flow gathering data before analysis;
collecting, cleansing and converting data. This creates too
much lag time before insights can be put into action.
Recommended flow implement analytics by first
defining the insights and questions needed to meet the
big business objective and then identify those pieces of
data. By defining the insights first, organizations can
target specific subject areas, and use readily available
data in the initial analytic model.
Transformed organizations are good at data capture and they are much more adept at data
management. The operational challenge is to understand where to apply insights in a particular
industry and organization. To keep the three gears moving together (data, insights and timely
actions) the overriding business purpose must always be in view. That way, as models, processes,
and data are tested, priorities for the next investigation become clear.
Recommendation 3: “Embed Insights to Drive Actions and Deliver Value”
New methods and tools to embed information
into business processes (cases, analytics
solutions, optimization, work flows and
simulations) are making insights more
understandable and actionable. Trend analysis,
forecasting and standardized reporting were the
most important tools companies used.
Organizations expect that the ability to visualize
data differently will be the most valuable
technique in two years. Other techniques and
activities that are currently delivering the most
value will still be done, but will be of less value.
New techniques to embed insights will gain in
value by generating results that can be readily
understood and acted upon, and they will make it possible for decision makers more fully to see
their customers’ purchases, payments and interactions. Making customers, as well as information,
come to life within complex organizational systems may become the biggest benefit of data driven
insights real to those who need to use them.
Recommendation 4: “Keep Existing Capabilities
While Adding New Ones”
Executives use analytics more frequently to inform
day-to-day decisions and actions this increasing demand
for insights keeps resources at each level engaged,
expanding analytic capabilities. The frequency with which
analytics is used to support decisions increases as
organizations transform from one level of analytic
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