Most business decisions involve some degree of uncertainty and the decision-makers seldom know the exact outcome of their actions. Data plays a crucial and transformational role in how decision-makers view business uncertainties.

Data is a collection of unorganized facts or observations that can be processed to obtain valuable information. Analytics is the science of examining raw data and information in order to draw insights.

The volume of available data and the technical ability to quickly interpret insights from data are primary factors in reducing uncertainties in business decisions. Organizations are using data to improve their business processes and forecast typical business metrics, as well as support strategic decisions that shape their future.

Understanding and using business-relevant data is a means to obtain valuable insights to support more informed business decision-making. Organizations are investing in analytics initiatives to deliver on their strategic imperatives, innovate, and obtain competitive advantages in the marketplace. Such investments are driving the demand for skilled professionals with analysis and analytics knowledge and experience.

Data analysis impacts how businesses make decisions by:

enabling new products and services and by creating new markets,

disrupting existing markets and unseating secure businesses,

driving increased efficiency (for example, for retailers to enable them to tailor products for customers),

identifying growth opportunities,

driving innovation,

operating more efficiently,

and improving risk management.

Business data analytics is an area of study that targets effective business decision-making as opposed to using the rigorous technical know-hows through which data is analyzed. Several business analysis tools, techniques, and competencies are used in business data analytics to direct analytics initiatives at many touch-points within the life cycle of an analytics initiative. This IIBA® Guide to Business Data Analytics emphasizes some of the significant business analysis and analytics concepts to build a foundational understanding that will guide practitioners through analytics initiatives.

Use of business data analytics for business decision-making is accomplished in the following ways:

Asking foundational questions to shape strategic imperatives:

What will analytics initiatives and business data be used for?

How will insights from data drive business outcomes and value for the enterprise?

What type of business data is most likely to generate the insights needed?

What business problems are being addressed using business data analytics?

What is the hypothesis that will be tested?

What do the identified patterns from data inform us about the future?

Highlighting how enterprise data is organized and managed:

What type of data is collected and captured?

What are the primary data sources for the enterprise (for example, customer, supplier, or product data)?

How are we managing data quality?

What is the enterprise data strategy and architecture: legacy, data warehouse, data lakes and vaults, big data capable, and so forth?

Understanding and communicating analytics results:

How can analytics results be best explained (for example, data coherence versus storytelling)?

How are analytics results presented to stakeholders visually?

What business inferences can be drawn out of the data?

Integrating insights into initiatives:

Enterprise business processes

What business processes and workflows are impacted?

If the analytics results drive change, how will that change be managed?

How does an organization become more data sophisticated?

Technology

What IT systems need to be improved to capitalize on the insights?

Are any additional technology/systems required?

People

Is additional training needed in order to improve employee capabilities?

1.1What is Business Data Analytics?

Business data analytics is a specific set of techniques, competencies, and practices applied to perform continuous exploration, investigation, and visualization of business data. The desired outcome of a business data analytics initiative is to obtain insights that can lead to improved decision-making. Business data analytics can be applied to investigate a proposed business decision, action, or a hypothesis, or to discover new insights from business data that may result in improved decision-making.

The business data analytics cycle is the iterative research process that seeks to answer a well-formed research question. Data analysis then explores the results of this research.

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.

1.1.1Business 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. Evidence through data is the driver of business decisions and change. Rapid technological advances in the digitization of data and improved analytics methods are prompting businesses to adopt a data-driven management philosophy.

Example of Evidence-Based Problem Analysis in Insurance

For the insurance industry, generating better customer value has always meant getting a clearer picture of individual risk. By paying closer attention to the data people create daily, insurance companies can better anticipate needs, personalize offers, and tailor the customer experience. It is a shift from the practice of using demographics data to customize insurance products. Data such as telematics, social media, and lifestyle data can accurately reveal individual risk patterns through advanced analytics. The availability of such data has prompted insurers to change the way products are marketed and priced, and to better manage claims.

1.1.2Business Data Analytics as a Capability

Business data analytics as a capability includes the competencies possessed by both the organization and its employees. Business data analytic competencies extend beyond those required to complete analytical activities, they include capabilities such as innovation, culture creation, and process design. This capability, or lack thereof, may define or constrict what the organization is capable of achieving through business data analytics.

Building Competencies for a Data-Driven Enterprise

Spotify is the largest on-demand streaming music provider in the world, with millions of users globally. As an experiment, Spotify wanted to send out a large number of emails that would tell customers if their friends have subscribed to the streaming service and the playlist they are listening to. The idea was to improve user engagement through by promoting it as a social experience. The initiative was a success. However, behind the scenes Spotify must have decided:

what the data infrastructure for their organization should look like,

how to source the relevant data about customers,

how to design a solution that should be capable of sending out relevant email content,

how to measure improved user engagement, and (above all)

how to create a business case to justify the entire initiative.

The ability to perform advanced analytics on the data customers generate is definitely a part of the shift in approach. However, to operationalize such an initiative, the organization needs to treat data as an extension of organizational culture which translates into creative ideation, process change, and the agility required to embrace the changes brought in by a data-driven enterprise.

https://labs.spotify.com/​2013/​05/​13/​analytics-​at-​spotify/​

1.1.3Business Data Analytics as a Data-Centric Activity Set

Business data analytics as a data-centric activity set includes the actions required for an organization to use evidence-based problem identification and problem-solving. Data analytics has been defined by expert practitioners as involving six core data-centric activities:

accessing,

examining,

aggregating,

analyzing,

interpreting, and

presenting results.

Business data analytics, in addition to the core data-centric activities, extends the activity set to analysis-oriented activities:

planning,

strategy analysis,

stakeholder collaboration and management,

solution designing,

recording and verifying analytics approaches, and

tracking and managing analytics recommendations.

These activities are executed in a more structured way to help organizations realize the business objectives behind analytics initiatives.

1.1.4Business Data Analytics as a Decision-Making Paradigm

Business data analytics as a decision-making paradigm involves making business data analytics a mechanism for informed decision-making across the organization. Business data analytics is the tool of making decisions using evidence-based problem identification and problem-solving. Evidence from data is an enabler for informed business decision-making that is more persuasive than instinctive decision-making which can be influenced by cognitive biases. Business data analysis strikes a balance between business experience and analytics results for effective business decisions through collaboration.

Examples of Collaborative Decision-Making

As deep analytics and artificial intelligence (AI) are becoming more prevalent in influencing decisions for enterprises, the underlying processes to arrive at a predictive or a prescriptive action are becoming more opaque. For example, the General Data Protection Regulation (GDPR) has provisions that give consumers the right to receive an explanation for any automated decision-making, such as the rate offered on a credit card or mortgage. The role of business data analytics becomes even more critical in this sense where evidence generated through data must be explained with the right business context to the decision-makers as well as end customers.

1.1.5Business Data Analytics as a Set of Practices and Technologies

Business data analytics as a set of practices and technologies establishes the framework required to successfully execute analytics initiatives. These practices can be discussed in the context of six business data analytics domains:

Identify the Research Questions,

Source Data,

Analyze Data,

Interpret and Report Results,

Use Results to Influence Business Decision-Making, and

Guide Organizational-Level Strategy for Business Data Analytics.

These six business data analytics domains define the set of data-centric activities, as well as the business analysis practices that enable successful analytics initiatives.

1.2The Business Data Analytics Cycle

The business data analytics cycle represents the research aspects of business 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 information is collected and smaller scoped questions are formed. A question may take the following 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 identifying questions that are of importance to answer and that the data produces valuable insights for resolving important business situations (problem or opportunity).

The scientific method paired with the business data analytics cycle:

Taking an example where the organization is looking for a solution to address its employee turnover problem, the analytics cycle begins by posing a question such as "How can we improve our staff retention rates?"

After conducting initial research, it may be discovered that turnover is effected by several factors resulting in the need to create several hypotheses, one of which might be "Does work overload affect turnover in our organization?" The organization may develop a survey to measure work overload and turnover and administer it to current and past employees. The results of the survey may be analyzed to understand any cause and effect relationships. It might be determined that work overload is high in parts of the organization that have or are experiencing a large amount of turnover. These results may lead the organization to put measures in place to re-balance work or decrease the workload of individual employees or roles.

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 accessed 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 may not be required because the data collected as part of a scientific experiment is obtained in a controlled environment.

When the objective of the analytics effort is continuous improvement or some other metric of improvement over time, the business analytics cycle is ongoing and iterative.

In the context of projects, with defined end points, 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.

1.3Business Data Analytics Objectives

Business decisions can easily be based on personal and individual experience, expertise, and instinct. Business data analytics reduces cognitive and personal biases 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, analytics models 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.

The objective of business data analytics is to explore and investigate business problems or opportunities through a course of scientific inquiry. The specific outcomes of business data analytics are dependent on the type of analysis and inquiry 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?

Example: Aggregation and summarization of sales data based on geographic regions.

Diagnostic: Explores why an outcome occurred. Diagnostic analytics is used to answer the question Why did a certain event occur?

Example: Investigation of dipping revenue in a particular quarter.

Predictive: Analyzes past trends in data to provide future insights. Predictive analytics is used to answer the question What is likely to happen?

Example: Predicting profit or loss that is likely to happen in the next financial year.

Prescriptive: Uses 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 …?

Example: What will happen to the total sales if the organization increases the marketing spend by 10%?

New modelling techniques are now available due to the advances in machine learning, deep learning, optimizations, and advanced data science. These techniques, coupled with the availability of disparate data and related data infrastructure, have increased the feasibility of deploying analytics solutions for business problems or opportunities.

Examples of Analytics Contexts and Business Use Cases