Introduction to Business Data Analytics.

International Institute of Business Analysis, Toronto, Ontario, Canada.
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Table of Contents
Chapter 1: Introduction to Business Data Analytics: An Organizational View
1.1 What is Business Data Analytics? 1
1.2 Business Data Analytics Objectives 3
1.3 Business Analysis and Business Data Analytics 4
Chapter 2: The Business Data Analytics Cycle
Chapter 3: Team Structure and Required Skills
Chapter 4: Strategy for Business Data Analytics
4.1 Building a Business Data Analytics Team 9
4.2 Establishing Best Practices 10
4.3 Curating Data 10
4.4 Performing Data Management Functions 11
4.5 Developing a Data Strategy 11
4.6 Challenges for Business Data Analytics 13
4.7 Techniques 13
Contributors 15
i

1.1
1
Introduction to Business Data
Analytics: An Organizational View
The Introduction to Business Data Analytics: An Organizational View
introduces business analysis concepts, activities, tools, techniques, skills and
how they're applied when establishing business data analytics capabilities for
the organization.
Business data analytics has become an area of great interest for
organizations, as it has been recognized as a means by which organizations
can obtain valuable insights from data; supporting more informed business
decision-making. As a result, more organizations are investing in business
data analytics as a means to deliver on their strategic imperatives, innovate,
and obtain competitive advantages in the marketplace. Such investments are
driving the demand for more skilled professionals with business data
analytics knowledge and experience.
This Introduction to Business Data Analytics: An Organizational View explores
the relationship of business data analytics to business analysis, emphasizing
how organizations can leverage the business data analytics cycle, team
structures and required skills, and strategy for business data analytics to
increase effective decision-making.
What is Business Data Analytics?
As a broad definition, business data analytics is a practice by which a specific
set of techniques, competencies, and procedures are applied to perform
continuous exploration and investigation of past and current business data for
the purposes of obtaining insights about a business that can lead to improved
decision-making. Business data analytics can be defined more specifically
through several perspectives.
These perspectives include, but are not limited to business data analytics as
a:
• movement,
• capability,
• data-centric activity set,
• decision-making paradigm, and
• set of practices and technologies.
1

What is Business Data Analytics? Introduction to Business Data Analytics: An Organizational View
1.1.1 Business Data Analytics as a Movement
Business data analytics as a movement involves a management philosophy or
business culture of evidence-based problem identification and problem-
solving. In this perspective, evidence through data is the driver of business
decisions and improvement. When this philosophy is in place, evidence is not
chosen to support a preconception or point of view; instead, all available
applicable evidence is used to make informed business decisions.
1.1.2 Business Data Analytics as a Capability
As a capability, business data analytics includes the competencies possessed
by the organization and its employees. Business data analytics competency is
not solely limited to the ability of an organization to complete analytical
activities. It also includes capabilities such as innovation, culture creation,
and process design. The capability or lack thereof may define or constrict
what the organization is actually capable of achieving through business data
analytics.
1.1.3 Business Data Analytics as a Data-centric Activity Set
As an activity set, business data analytics includes the actions required for an
organization to use evidence-based problem identification and problem
solving. Business data analytics has been defined by expert practitioners as
involving six core data-centric activities:
• accessing, • analyzing,
• examining, • interpreting, and
• aggregating, • presenting results.
1.1.4 Business Data Analytics as a Decision-making Paradigm
As a decision-making paradigm, business data analytics is a means for
informed decision-making. Through this lens, business data analytics is
considered the tool of making decisions through the use of evidence-based
problem identification and problem-solving.
1.1.5 Business Data Analytics as a Set of Practices and
Technologies
Business data analytics is also considered a set of practices and technologies
required to perform the analytics work itself. These practices can be
discussed in the context of five business data analytics domains:
• Identify Research Questions,
• Source Data,
• Analyze Data,
• Interpret and Report Results, and
• Use Results to Influence Business Decision-Making.
2

1.2
Introduction to Business Data Analytics: An Organizational View Business Data Analytics Objectives
Business Data Analytics Objectives
Organizational leaders frequently make business decisions based on personal
expertise and instinct. Business data analytics removes cognitive and
personal biases from the decision-making process by using data as the
primary input for decision-making. When performed well, business data
analytics can create a competitive advantage for the organization.
For example, algorithms based on weather, soil, and other conditions have
been found to be more accurate in predicting the price and quality of red wine
after it has been aged compared to the wine experts who influence the
decision-making based on their own cognitive biases as to what they enjoy
and do not enjoy in a wine.
In a broad sense, the objective of business data analytics is to explore and
investigate business problems or opportunities through a course of scientific
inquiry. The specific objectives of business data analytics are dependent on
the type of analysis that is being performed.
There are four types of analytics methods:
• Descriptive: provides insight into the past by describing or summarizing
data. Descriptive analytics aims to answer the question “What has
happened?”
• Diagnostic: explores why an outcome occurred. Diagnostic analytics is
used to answer the question “Why did a certain event occur?”
• Predictive: analyzes past trends in data to provide future insights.
Predictive analytics is used to answer the question “What is likely to
happen?”
• Prescriptive: utilizes the findings from different forms of analytics to
quantify the anticipated effects and outcomes of decisions under
consideration. Prescriptive analytics aims to answer the question “What
should happen if we do …?”
TIME / QUESTION TYPE WHAT WHY
PAST DESCRIPTIVE
What happened?
DIAGNOSTIC
Why did it
happen?
PAST/FUTURE
FUTURE
PREDICTIVE
What is likely to happen based on past trends?
PRESCRIPTIVE
What should happen if we take a certain path?
What is the best outcome given the uncertainty?
3

1.3
Business Analysis and Business Data Analytics Introduction to Business Data Analytics: An Organizational View
Business Analysis and Business Data Analytics
The terms business data analytics and business analysis are frequently used
interchangeably. However, there are significant differences between the two
terms. Business analysis is the practice of enabling change in an enterprise by
defining needs and recommending solutions that deliver value to
stakeholders. Comparatively, business data analytics is focused on the
process of data analysis.
Business analysis provides the business context for business data analytics.
Business analysis defines the focus for the research questions being asked
and sets the scope before data is collected. Business analysis also aids in the
collection of data and the implementation of the data collection processes.
Business data analytics is used to sort, process, and analyze the data once
assembled.
Once the analysis of the collected data is complete, business analysis
activities are performed to interpret the results obtained from analytics and
transform information into business decisions. Business analysis activities
are performed to communicate the results of business data analytics and
facilitate the implementation of informed business decisions made as a result
of what is learned from analyzing the data collected.
Some consider business data analytics as a specialty or subset of business
analysis; one that is focused on data analysis. This viewpoint is taken since
many skills and competencies often discussed when defining business
analysis are equally important when performing business data analytics work.
4
MAIN
FOCUS
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2 The Business Data Analytics Cycle
The business data analytics cycle represents the research aspects of
business data analytics. It is an iterative cycle initiated through the
development of a well-formed research question and then explored through
targeted, but thorough data analysis.
The cycle is based on the scientific method. The scientific method is a process
for research that is used to explore observations and answer questions. The
process starts by asking a question that scopes the research and is phrased
as who, what, when, where, which, why or how. Based on these questions,
background research is completed to inform the research and create a
smaller scoped question. A question is then raised using the following
possible format:
If ________ happens then will ________ happen, or
Is __________ different to ___________, or
Does __________ affect ___________ etc.
The question is then tested using a method or procedure, and the results are
analyzed to draw conclusions based on the smaller scoped question.
Business data analytics focuses on the data collection and data analysis part
of the scientific method while the processes before and after this are
informed by business analysis. Business data analytics requires business
analysis to ensure the data analysis is focused on raising questions that are of
importance to answer and that the data drives valuable insights for resolving
important business situations (problem or opportunity).
5

The Business Data Analytics Cycle
The scientific method paired with the business data analytics cycle looks as
follows:
Communicate Results
/Use as Knowledge
for Future Research
Pose a
Question
Research
Create a
Hypothesis
Test
Hypothesis
YES NO
Is testing effectively
capturing data?
Analyze Data/
Draw Conclusions
Troubleshoot
Problems/Consider
Another Method
Despite its similarities to the scientific method, the business data analytics
process has some slight differences. For one, the business data analytics
process may differ depending on the type of analysis taking place. Testing
may not always include an experiment to collect data, as the data might
simply be downloaded from a server using existing data sources. In business
data analytics, it is necessary to perform data validation and verification on
the data collected. In the scientific method, data validation is not required
because the data collected as part of a scientific experiment is obtained in a
controlled lab environment.
When the objective of the analytics effort is continuous improvement or some
other metric of improvement over time, the business data analytics cycle is
on-going and iterative.
In the context of projects, with defined endpoints, the conclusions drawn
from a project may be used to form new research questions in-turn
perpetuating another execution of the entire business data analytics cycle.
6

3 Team Structure and Required Skills
For any data-driven engagement to succeed, there needs to be a partnership
between those providing the business experience (business stakeholders and
business analyst) and those with the technical skills: the data analysts and
scientists. These roles work collaboratively to ensure the business context is
properly translated to guide the analytics activities appropriately and to find
the best ways to obtain value from available data.
There is no optimal team structure that works for every business data
analytics initiative. Business data analytics teams require resources having a
mix of business and technical skills. Teams who excel technically augment
the team with members who possess strong business knowledge or acquire
the knowledge themselves. Teams that are comprised of resources with
strong business knowledge seek out those with technical skills to help with
the data acquisition, cleansing, and analysis work. This latter scenario is
evolving due to the advancements being made in the tool market simplifying
the steps for creating models and performing analytics. The role of the 'citizen
data scientist' has emerged where business resources can now play an active
role in conducting analytics research without having to possess the advanced
skills of a data science expert.
For a large organization the team structure may consist of any or all of the
following roles:
• Subject Matter Experts (SMEs): provides specific knowledge of the
business sector or specified business domain.
• Data architect: develops data systems to capture and store data.
Generally, does not program systems as that is the job of the data
engineer.
• Data engineer: develops and maintains data systems.
• Data scientist: applies advanced technical skills to create and run
analytics models to obtain insights from data.
7

Team Structure and Required Skills
• Data analyst: interprets and analyzes data. May work under the direction
of the data scientist.
• Data journalist: turns results into something that can be communicated
to anyone within the organization.
• Business analyst: establishes the scope for the analytics work and
utilizes results to support business decision-making and implementation
of the resulting decisions.
The structure of a business data analytics team can be dependent on a
number of factors, including:
• the size of the organization,
• industry,
• current capabilities, and
• tools.
For small organizations, it may be possible to hire a single person to perform
all the roles. In a smaller organization, or one that is new to business data
analytics, a data scientist with the help of SMEs and IT professionals, may be
sufficient. For each of these roles, there are different levels of experience,
skills, and educational accomplishments required.
8

4.1
4 Strategy for Business Data Analytics
Strategy for business data analytics includes the activities to build the
capacity and capability for data analytics within an organization. This includes
building a business data analytics team, establishing best practices, curating
data, performing ongoing data management functions, and developing a data
strategy.
Strategy for Business Data Analytics includes:
• Building a Business Data Analytics Team
• Establishing Best Practices
• Curating Data
• Performing Data Management Functions
• Developing a Data Strategy
• Techniques
Building a Business Data Analytics Team
Due to the growing interest in business data analytics, the demand for skilled
practitioners in data science and analysis is on the rise. Resources are in high
demand, resulting in an increase in salaries. Organizations interested in using
business data analytics as a strategic advantage or applying it enterprise-
wide will be dependent on selecting and retaining top talent to build out their
practice. While retention may be based heavily on salary level, there are
several other factors that can influence the ability of an organization to build
and retain a talented data team.
9

Establishing Best Practices Strategy for Business Data Analytics
For example: having the
• opportunity to work on engaging and exciting initiatives,
• ability to work directly with key decision-makers,
• opportunity to learn the business,
• ability to solve complex business problems, and
• access to sufficient tools to perform the job.
4.2 Establishing Best Practices
Best practices in business data analytics are established through the
accumulation of experience, lessons obtained from completed work, and
being up to date on industry trends, including advancements being made in
technology.
Establishing best practices in business data analytics involves identifying a
standard set of tools and techniques that work well for the organization, for
the types of problems being solved, and for the skill set and capabilities
available. Best practices may be used as suggestions or formed to provide a
set of standards that are required to be followed by members of the analytics
teams. For example, in establishing best practices, an organization may
develop policy to ensure that sampling methods between different analytics
projects are shared across teams. Another application of best practice may be
maintaining subsets of analytics requirements for re-use or establishing a
procedure for securing approval for data access. Whatever the practice is, the
motivation for identifying, stating, and developing policies around best
practices is to mature the analytics work in a way that fosters improved
performance of the work and moves the organization forward to obtain more
value from the investments being made in business data analytics.
4.3 Curating Data
Data curation involves the collection, aggregation, and integration of data
from different sources. Early data curation processes focused heavily on
obtaining data from transactional systems, but today's big data environments
require more sophisticated tools and approaches to obtain data from a wide
variety of sources.
Data curation is more than blending data together. One of the main objectives
is to transform data from disparate sources in a manner where the new whole
is worth more than the individual parts; meaning, once transformed, the value
obtained from the curated data is more valuable than the value obtained from
each individual data source.
When establishing a company-level practice for data analytics, data curation
is used to build the repositories and source the data that analytics teams rely
on, streamlining the processes to obtain, clean, and preserve data, and
establishes a mindset of utilizing data as a corporate asset.
10

Strategy for Business Data Analytics Performing Data Management Functions
4.4 Performing Data Management Functions
Data management consists of the practices performed to administer data
across an organization. Its major functions include:
• Data governance: is the rules and policies that manage the data assets of
an organization to ensure high-quality data.
• Data architecture: is the models and standards that govern how data is
collected, stored, and integrated across an enterprise.
• Data security: are the activities performed to protect data from a privacy
and confidentiality perspective.
• Meta data management: is the administration of information that is
maintained about the data assets an organization collects and manages
As organizations look to set an organization-level strategy for business data
analytics, more formal policies and processes are established to improve how
data is acquired, integrated, and accessed.
4.5 Developing a Data Strategy
Making an investment in business data analytics is no different from investing
in project management or business analysis; the organization must obtain
value for the money it is spending on the discipline. Business data analytics
needs to deliver value in order to obtain sponsor support and be sustainable
for the long-term. Just as an organization performs thorough up-front
analysis before initiating a new initiative, the same holds true for investments
made in business data analytics.
One way to ensure value from business data analytics efforts is to align the
data strategy to the business strategy. Doing so helps to ensure the data
analytics work focuses on addressing the right situations (problems or
opportunities) that truly help the organization deliver on its business strategy.
Without this alignment, the business data analytics team is collecting and
analyzing data that is of little value to the overall direction the business is
heading. This alignment is important at the onset when teams are identifying
the research questions to explore.
Facilitating discussions that enable the business data analytics team to
understand the business model and providing the results from the current
state analysis to guide the definition of the data strategy are activities best
performed by those who possess strong business analysis skills.
11

Developing a Data Strategy Strategy for Business Data Analytics
4.5.1 Business Strategy Supporting Data Strategy
A business strategy defines a roadmap or plan an organization uses to achieve
its strategic goals and objectives. Developing a business strategy is a function
of management, but the strategy itself should be leveraged by all areas of the
organization to guide lower-level plans, objectives, and the work itself.
Business data analytics initiatives use the business strategy to guide and
align analytic initiatives. The organization as a whole benefits from the
insights discovered through the successful execution of the data strategy
which in turn helps to deliver on the business strategy.
4.5.2 The Data Strategy
A data strategy defines a roadmap for using an organization's data to enable
better informed decision-making, obtain competitive advantages, and
generate business value. Data strategies are used to enable the organization
to achieve its strategic goals. Businesses who develop data strategies
understand the importance of using their data to glean insights by which to
guide future decision-making. They recognize that data is a valuable asset to
be managed and they leverage their data as a tool to deliver on their strategic
imperatives. A data strategy is used to define the types of data that will be
collected and cleaned, the understandings that are being sought, and the
ways data will be used to obtain competitive advantages. Innovation should
be an underpinning to all aspects of the data strategy.
Building a data strategy requires a firm understanding of the business. IT
frequently lacks sufficient understanding of the business to develop the data
strategy independently. On the business side, subject matter experts often
lack sufficient knowledge about technology to know how it can best be
applied to relate and analyze the data. This necessitates that building the
data strategy is a collaborative effort in which IT and business work together.
Business analysis skills such as analytical thinking and problem-solving,
business acumen, system knowledge, and interaction skills such as
facilitation, negotiation and conflict resolution help business data analytic
professionals build an effective data strategy.
Components of a Data Strategy
Many organizations develop their own templates for building and
communicating a data strategy. How formal the template and process is to
create a data strategy is dependent on the practices and methodologies used
within the organization. Much of the value achieved from specifying a data
strategy can be obtained through the collaboration and discussions held
between IT and the business stakeholders.
Whether the organization is using a predictive, adaptive, or a hybrid delivery
approach, a data strategy should address how an enterprise will identify,
store, manage, share, and use its data.
12

Strategy for Business Data Analytics Challenges for Business Data Analytics
4.6 Challenges for Business Data Analytics
The following are some of the challenges that organizations may face with
business data analytics:
• Business alignment and priorities from a data perspective can be difficult
to define.
• Making decisions on topic, scale, or scope for a data initiative can be
tricky.
• Determining which data to measure and capture to achieve business
objectives can prove challenging.
• Finding data that creates value may be difficult; not all data helps make
better decisions.
• Even when the data source is identified, defining the specific subset of
data needed can be complex.
• Poor or unknown quality of data, especially historical.
• Data integration and accessibility. Data being placed in disparate
systems, and of varying format and quality.
• Business stakeholders not being comfortable with the rapid changes
occurring in the business data analytics space.
• Difficulty bringing business stakeholders to a shared understanding on
value when sharing data assets across business domains.
• Lack of experience or knowledge for those completing the analysis as well
as the managers receiving the results.
• Change in organizational culture required to trust insights gleaned from
data over experience and intuition.
• Business managers finding it challenging to structure data teams.
• Difficulty finding the right tools.
4.7 Techniques
There are a host of techniques used when building a data strategy. Many of
the techniques listed here can be used by analysts to drive valuable
discussions between the business stakeholders and the IT representatives.
Most of the techniques result in the creation of visual models that can be
leveraged on an ongoing basis to help the team, comprised of both business
and IT resources, understand the context surrounding and influencing the
business data analytics work being performed.
While this list is not exhaustive, it does highlight some of the major
techniques in use today by business data analytics teams when developing a
data strategy:
• Balanced Scorecard: a strategic planning and management tool used to
measure organizational performance. It is outcome focused and provides
a balanced view of an organization by implementing the strategic plan as
an active framework of objectives and performance measures. Within
13

Techniques Strategy for Business Data Analytics
business data analytics, this technique is used to measure both internal
and external elements in order to bring a shared understanding between
business stakeholders and IT about organizational performance and how
the organization is currently meeting its customer obligations.
• Benchmarking and Market Analysis: provides an understanding of
where there are opportunities for improvement in the current state.
Specific frameworks that may be useful include 5 Forces analysis, PEST,
STEEP, and CATWOE. The results from this type of analysis are useful to
provide an understanding of the external environment surrounding the
organization and how the performance of different aspects of the
business compare to the leaders in the industry.
• Business Model Canvas: provides an understanding of the value
proposition the organization satisfies for its customers, the critical factors
in delivering that value, and the resulting cost and revenue streams. It is a
helpful technique for understanding the context for any change and
identifying the problems and opportunities that may have the most
significant impact. On business data analytics initiatives, this technique
can be used to facilitate collaborative discussions with business
stakeholders and IT representatives enabling everyone involved to
achieve a comprehensive understanding of the critical aspects of the
business.
• 5 Forces analysis: a framework that can be used to provide an
understanding of where there are opportunities for improvement within
the current state of the organization. It involves analyzing aspects of the
competitive landscape and the powers held by suppliers and buyers. On a
business data analytics initiative, this technique helps to understand the
competitive forces impacting the organization.
• Metrics and Key Performance Indicators (KPIs): are used to measure
the performance of solutions, solution components, and other matters of
interest to stakeholders. On business data analytics initiatives, KPIs are
used to identify the key results expected from data analysis efforts.
• SWOT: is used to evaluate the strengths, weaknesses, opportunities, and
threats to the current state of the organization. On business data analytics
initiatives facilitating discussions to complete a SWOT analysis will
provide context about the internal and external environments of the
organization.
• Value chain analysis: is a model used to identify the key activities
performed within an organization and to analyze how each contributes to
the value provided by the products and services delivered to its
customers. Value chain analysis is a technique used in business data
analytics to provide a shared understanding of how the organization
provides or may provide value to its customers. It can be used to facilitate
collaborative discussions between business stakeholders and IT to drive
innovation in order to improve an organization's competitive advantage.
14

Contributors
Authors
• Anne Tixier
• Laura Paton, MBA, CBAP, IIBA-AAC, CSM (Chair)
• Leelyn Cruddas
• Dr. Mark Griffin
Reviewers
• Angela Weller, CSM, CSPO
• Anna Sloan
• Charlotte DeKeyrel
• Darcey Leischner, CSM
• Jodie Kane, CPBA, CSM, CSPO, CPBI
• JoJo John
• Kunal Joshi, PMP, CBAP
• Melanie Lee, MSc, CBAP, CSPO
• Melody Hicks
• Parvathi Ramesh, CA, CBAP, CISA
• Ramanpal Singh Anand
• Sruti Chandra, MBA, CBAP
• Swaroop Oggu
15

Introduction to Business Data Analytics.

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