Modern Business Analytics

Chapter 1. The Role of Business Analyst and Analytics

If you are new to business analytics or considering a career in this space, this chapter will be particularly useful to get started or transition from a business analyst into analytics. If you are already familiar with the fundamentals or currently working in the industry, you may find it helpful to jump ahead to the next chapter, where we start with analytics application.

The role of a business analyst has been around for some time but has grown in importance as the need to strategically leverage data has increased. Organizations want to leverage their data as an asset and improve decision making at all levels. While better decision making can be defined in multiple ways, ultimately it means the right answer was chosen the first time, which results in improved efficiency, effectiveness, and a better bottom line. There are many roles that leverage data, but a business analyst is the one role that provides the necessary context to create business value.

If you research what knowledge and skills a business analyst has, you will likely come across what business analysts do but not the business value of the results achieved. This chapter defines what the business analyst’s role is in analytics and the critical part it plays in attaining value from analytics. It also addresses the business problems that analytics solve and the project life cycle that analytics projects progress through. Let’s get started with understanding the role of a business analyst and types of analysts.

What Is the Role of a Business Analyst?

As a business analyst, you leverage your analysis and industry skills to decompose, or break down, problems and find root causes resulting in continuous improvement and successful strategy execution. This isn’t always easy, because the business landscape of an organization can be complex. It may include multiple information systems, business processes, and business departments (both people and structure), resulting in the analyst working across all of these functional areas. Ultimately as a business analyst, you have subject matter expertise about your organization and its industry, and you are key to solving problems and providing solutions.

Due to the diversity of knowledge a business analyst has, one of the primary areas that business analysts focus on is requirements management. Requirements management is the process of defining, documenting, and analyzing requirements to address the scope of business needs. Requirements are determined by understanding the problem and identifying the best solutions. Analytical projects often start with understanding a problem’s symptoms. For instance, let’s say you work for a telecom company and it appears customer attrition (percentage of customers no longer using a company’s products or services) has increased. An analytical project might start with research to determine the root cause of the problem. Requirements analysis would continue based on how clear the problem is and where analytics might be used to solve the problem. Increased customer attrition is a problem, but it is not yet clear how analytics might help solve it. This is why things aren’t quite as clear cut as they may seem. Requirements and how analysts help solve problems will be covered more in a later chapter.

As a business analyst, you will always be addressing new requirements due to the constantly changing business environment. Because of this, the best way to understand your role is to explore the skills and responsibilities and to see examples of the different types of analyst roles that may exist in an organization.

Skills

Business analysts are quite versatile, drawing upon multiple skills to produce results. As a business analyst, you research new approaches to address current problems, which means searching for new processes, systems, and other options to be able to tackle challenges your organization may be facing. You use your industry knowledge and technical skills to evaluate the results of your research and then determine the applicability of each option. To do this, business analysts apply statistics to determine if new options will produce the desired results and communicate findings to peers and senior leadership. Central to each of these examples is leveraging your ability to analyze—which means examining details and applying these details to the big picture of problem solving. Table 1-1 highlights many of the skills a business analyst can be expected to have.

Business analysts can have many skills that are applicable in different work environments and scenarios as well as industries. It is quite common for business analysts to transfer skills across domains and industries throughout their careers. The skills you choose to focus on and develop will help shape your future projects, responsibilities, and day-to-day activities as a business analyst.

Table 1-1. Business analyst skills

Skill	Definition	Example
Research	Systematic identification of materials and sources to fact-find and establish a conclusion	Reviewing different vendor offerings to determine if these offerings meet requirements
Communication	Exchanging of information to leverage effective methods	Presenting to an executive audience possible solutions to a problem
Problem solving	The process of identifying solutions to complex issues	Completing a root cause analysis effort, identifying the cause, and applying resolution
Industry expertise	Knowledge and skills specific to an industry group and ability to apply this knowledge	Having knowledge of the manufacturing industry and using this knowledge to complete problem solving
Statistics	The practice of collecting and analyzing data to infer results	Collecting and analyzing data on a process with the intent of improvement
Technical	Specialized knowledge and expertise required to perform technical activities	Using a programming language to complete data analysis
Analysis	Examining complex components or processes to gain knowledge of the nature and features	Examining system interfaces to understand how points interact

Responsibilities

Responsibilities are activities a business analyst is expected to perform, and depending on the focus of the business analyst, these can vary. As a business analyst, you might be focused on marketing, or you could have a focus on information technology (IT) projects—both common areas of specialization. Here are some of the primary responsibilities of business analysts, but this is not an exhaustive list:

Managing requirements

Gathering, preparing, and validating requirements for projects

Identifying problems and opportunities

Performing analysis on potential causes, sorting through the noise, and identifying root contributors and new opportunities

Determining and proposing solutions

Building​ on identifying problems and opportunities, taking the analysis further to identify and recommend the next course of action

Budgeting and forecasting

Creating ​a budget for business expenses and revenue for short-term planning, then projecting business outcomes for the future

Planning and monitoring

Scheduling ​activities, allocating resources, identifying milestones, and determining progress made to the plan

Process modeling

Creating a graphical representation of business processes or workflows for analysis

Ongoing analysis

Continuous gathering of data and review to check results for a specific purpose

Testing

Measuring the overall quality, functionality, performance, and reliability before use

Given the breadth of the various responsibilities that a business analyst can have, analysts can reside in many different organizational departments and support different efforts. As an analyst, you can specialize in an industry or functional area. The next section explores different types of analysts and their areas of focus.

Types of Analysts

Because the business landscape is broad and multiple functions support each business model, the types of analyst roles can vary. Any team that uses data may be able to use a business analyst, and all of these roles leverage the skills and responsibilities we just discussed while focusing on the needs of their part of the business. Some common types of analysts include marketing, finance, functional, system, and data analyst. Again, this is not an exhaustive list as it is possible to specialize in several areas; however, the roles we’re about to cover provide a good foundation of the diversity that comes with being a business analyst. The different roles outlined can also collaborate on projects in the organization.

Marketing analyst

Marketing efforts are directly linked with organizational strategy such as increasing revenue or the number of customers. Marketing has grown due to the digitization of how businesses interact with their customers. Marketing has shifted into digital marketing, also known as online marketing, because most marketing occurs online. Communications via social media, websites, and mobile devices enable businesses to connect directly with customers and are the primary way of developing customer relationships. Being able to analyze the data points created through these touchpoints is critically important to gain insight about how to best engage customers. The marketing analyst role doesn’t stop there. Since digital marketing is measurable, the need to analyze the results is a priority.

Marketing analysts focus on gathering and cleaning data from sources such as surveys and campaign results. Campaigns that include offers and promotions need analysis such as measuring reach, engagement, and lift (revenue increase) to determine what is working and what needs to be improved. Marketing analysts take the results and determine how to adapt and drive the digital marketing strategy. They also leverage analytics to predict a customer’s choice or use segmentation to target and personalize marketing campaigns.

Marketing analysts can also focus on research such as understanding competitors, reviewing trends in markets, and price analysis. They support product development and other business teams as marketing is a support organization and each of these areas leverage different types of analytics.

Financial analyst

Finance is another support organization like marketing. All organizations have to have financial health to stay operational. Finance supports the organizational strategy by providing insight into financial investment decisions. A financial analyst will primarily focus on activities that support these decisions by reviewing economic trends, industry direction, and competitive analysis. Analytics is a common skill set leveraged by financial analysts.

One key area a financial analyst works on is analyzing historical results and completing forecasts and predictions. Financial analysts create different financial models that could involve optimization and simulation scenarios to determine risk or the optimal price for a product. Knowing Excel and predictive analytics tools is often necessary as they support the creation of different financial models. These models start with financial statements, but can include discounted cash flows, mergers and acquisition analysis, and the impact of decisions on the organization’s stock price. Financial analysts are involved in risk analysis based on investment decisions and produce written reports related to financial status.

Both financial and marketing analysts are examples of business-focused analysts. Analysts can also be specialized when working in IT, where they may be a functional, system, or data analyst.

Functional analyst

Functional analysts can specialize in areas such as manufacturing, supply chain, or specific applications. Functional analysts are subject matter experts in their areas and leverage this expertise to find improvement opportunities. The primary goal of functional analysts is to improve the productivity of an area by focusing on the functional requirements and reviewing the systems. An example of this is reviewing the supply chain process for a company and identifying opportunities to improve efficiencies and then recommending improvements in the system to the operations team. Functional analysts also focus on reviewing existing systems and coordinating updates to keep the technology current. This type of analyst tends to be part of the IT department, but it is possible this role may exist in the department that the functional analyst has expertise in. A functional analyst is often the liaison between the business and technical departments in an organization and can be a part of either department.

As the previous examples show, functional analysts have a deeper technical skill set than other analyst types. They have a broad understanding of technology including networking, databases, and applications, and they leverage process and technical knowledge to identify opportunities for improvement and recommend technical solutions. Technical analysts, like functional analysts, leverage software development methodologies including agile or Scrum and may model data, prepare design diagrams, complete testing, and train users. This role can be visible within the organization and requires strong communication and leadership skills. A functional analyst may lead a portion of a technical project and need project management skills as well.

Functional analysts contribute to the requirements for analytical projects by identifying measures, metrics, indicators, and other data needed for improvement opportunities. Process improvement relies on analysis and measurement of goal progress. Analysis also focuses on checking position on progress and making decisions about next steps. Many of the skills needed to be a functional analyst is used by a system analyst. We will review the system analyst next.

System analyst

System analysts are considered technology professionals who define requirements, assist in design, and support the deployment of information systems in an organization. There are some parallels with a functional analyst, but a systems analyst is more technical. Like a functional analyst, a system analyst can research problems, recommend solutions, and work with stakeholders on developing requirements. The difference between the functional analyst and the system analyst is that the system analyst is familiar with operating systems, application configurations, hardware platforms, cloud platforms, and programming languages. System analysts are often involved from the analysis stage of a project to post implementation to ensure system stability.

System analysts are often in a liaison between the business stakeholders and the technology teams to translate requirements into technical design. System analysts also focus on integration of technologies to solve business problems linking different platforms, protocols, networking, and software together. System and functional analysts can collaborate on solutions. System analysts are typically part of the IT department.

System analysts can also contribute to the requirements for analytical projects, by identifying data points required for decision making. Data analysts, which we will review next, work as team members on analytical projects.

Data analyst

Data analysts work with data to find insights that can be leveraged for business value. Business value can be a good decision, the discovery of a problem, the solution to a problem, or new trends and patterns that can be leveraged for new opportunities. Data analysts take insights and formulate data stories to communicate to leadership.

Data analysts can be in a technology or business department and share some of the skills of a functional analyst where knowledge of a business industry is important. Data analysts need to have a strong foundation in descriptive and inferential statistics and are often part of project teams that support larger analytical goals. One area where data analysts differ from the other types of analysts mentioned is the hands-on involvement working with the data.

As a data analyst, you will leverage technical skills for data collection and analysis, and report insights using data storytelling. A data analyst might acquire data from the source system, merge and blend the data with other sources, and use different analytical techniques to search for insights. For example, a data analyst might look at customers to determine if different clusters or segments exist so they can be treated differently. Data analysts can also cleanse and transform data to apply business rules or increase data quality.

While working with data, data analysts can partner with other analysts such as the system analyst. Since a system analyst has knowledge of the business applications, a data analyst may partner with them to better understand how to acquire the data or understand the structure of data in an application. In contrast, while system analysts focus on business applications to close business gaps, data analysts leverage analysis skills on diverse datasets to support improved decision making versus improving processes.

There are fine lines between a business analyst and a data analyst, with the primary difference being data analysts focus more on the technical aspect of wrangling and analyzing the data. Due to the overlap of multiple types of analysts, it is impossible to highlight precisely what a business analyst will focus on in an organization. Practically, a business analyst could leverage skill sets to work in multiple areas of an organization. If you review each type of analyst, you will see that data plays an important role in each, which begins to outline why business analysts need to understand analytics, which we will address in the next section.

Why Does a Business Analyst Need to Know Analytics?

Organizations are continuing to invest in analytics as it focuses on managing data and leveraging it to improve decision making, business processes, shaping strategy, and driving strategy. Analytics is used to discover and manage business risks and opportunities. As outlined in the prior section, a business analyst, regardless of the primary role played, works with data, focuses on problem solving, and recommends solutions. This is the sweet spot where analytics is used.

Analytics will be explained in detail throughout this book, but to give it a definition, it is a capability organizations develop to support the analysis of data using statistics, math, and algorithmic models to improve decision making. Analytics can be descriptive, diagnostic, predictive, or prescriptive, according to Gartner. These primary analytical types have been adopted industry-wide and categorize the different types of analysis that a business analyst can be involved in. To get a better understanding of why a business analyst needs to not only understand but apply analytics, the next few sections cover the explosion of data, the need to have business context for every analytical problem, and the role analytics plays in generating business value.

Data Explosion

The concept of digitization is the key to understanding the data explosion. Simply, the data explosion is the automated capturing of data points through technology. The term “big data” has been in use since the 1990s when John Mashey, a chief scientist from SGI, identified the start of digitalization with the growth of the internet. With data being captured that was not available before, the focus on using data combined with computing power, statistics, and math, the value of data started to evolve. Big data is mostly the norm now, but, the concept of the data explosion has continued with more data points (data captured as part of an event, transaction, or any interaction) being collected every second of the day.

Think about search engines, cookies on websites, global positioning systems, and each mobile application on your phone: these are all data collection points. Multiply the data collection points by the number of users and interactions; you get the picture. This is happening consistently and globally. Take an example of the automated toll collection systems that different states use for state highways. States on the east coast of the United States use EZPass, where the number of transactions for 19 different states comes to approximately 10 million transactions a day. Likely this information is already outdated, and the number is much more than quoted. This is an example of one organization capturing data for automation but also analysis. Traffic patterns, point-to-point travel, analysis of logistics, and road wear are examples of the types of analysis that can be completed with the data collected. YouTube, baseball, and analyzing the surface of the earth are all examples contributing to data generation and growth.

All organizations are investigating the use of data for cost reduction, improving efficiencies, and measuring results. But the challenge is the amount of data and understanding what to do with it.

So does this data have a use or value? This is one of the catalysts that is fueling the growth of analytics. The primary goal is to find insight that can be useful to an organization. This is where the role of the business analyst comes in. Business analysts are key to determining the value of data to an organization, and this comes with understanding the business.

Business Context

Data is not valuable without context. If you were handed a spreadsheet with columns and rows of numbers, but no column headers, could you do anything with it? You could argue that someone with experience could look at the numbers and guess the data contents, but not much more could be done with that data. Business context is the key to turning data into information.

Business context is about understanding where an organization fits in the business world. An example is understanding what industry an organization is in. Industries such as finance, manufacturing, high tech, telecommunications, shipping, or entertainment (not an exhaustive list) are examples of where a business may operate. An industry has a model for business operations that defines what a business does every day to service its customers. There are concepts about products and services in an organization that a business analyst knows. For example, in telecommunication a customer is called a “subscriber,” and the average revenue per user, referred to as ARPU (pronounced R-PU), would be known to a business analyst working in this industry.

Data is generated by technology that can be segmented into systems and applications. Business analysts understand the primary business systems and applications in the organization they work for. Which systems are used in which business process? Which systems are the source of customer information? Which applications does a customer interface with when ordering a product or paying a bill? Knowing this information provides context to business data and insight into what the data represents and how it can be used.

Patterns and trends in data cannot be interpreted without context. It is not possible to understand root causes or provide valid recommendations to problems without context. It is impossible to determine if patterns or trends discovered are valuable without business context. Business analysts bring the experience and expertise needed to determine if data insights exist. This is why business analysts are a key part of the analytics process.

Analytics

Many terms exist that can be merged under the umbrella term of analytics. It has become an umbrella for business intelligence (BI) and in some cases has been identified as a specialized capability such as predictive analytics or marketing analytics. Many vendors use analytics to differentiate their products. Data and business analytics are often called out as different aspects of analytics, where data analytics focuses on the technical, statistical, and math aspects, and business focuses on the application of business expertise to the findings. Basically, the term analytics describes all activities and capabilities that an organization uses to exploit large datasets for insight.

Gartner has determined the primary analytical techniques that organizations use, and these were mentioned previously: descriptive, diagnostic, predictive, and prescriptive. Often another term that is thrown in is discovery analytics. Analytics can be segmented into five different techniques, which we explore next. Each technique can also be viewed as a step toward greater capability and maturity in analytics.

Descriptive

Most organizations have descriptive analytics capabilities as this area answers the question, What has happened? This capability stage uses ​ business intelligence tools, dashboards, and scorecards to monitor and manage the business. Business questions include: What product category produced the most profit? How did customer care perform on average time supporting customers? What was the average production cycle for product X last month?

Diagnostic

Diagnostic analytics builds on descriptive analytics to answer the question, Why did it happen? This is the capability stage used to understand root cause analysis and find patterns and trends that lead to recommendations. Business questions include: Why did the profit margin decrease on product X? Why are sales dropping in the fourth quarter for product Y? Why did the manufacturing cycle double last month? Both descriptive and diagnostic analytics focus on hindsight.

Discovery

Discovery analytics focuses on answering the question, What else should I know? This is the stage where new capabilities start to emerge. Building on diagnostic analytics, discovery analytics is where a new direction is formed for analysis. This stage can involve incorporating new data sources and finding additional contributing factors to the root cause. Discovery analytics provides insight, which leads to foresight.

Predictive

Predictive analytics is the capability stage where most organizations want to be, as this is where the business value of analytics emerges. Predictive analytics focuses on answering the question, What is happening next? More specifically, predictive analytics focuses on answering these questions: What is the likelihood this customer will click on this ad? What is the probability of this customer churning? What is the likelihood that this marketing campaign will result in a certain lift in sales? Predictive analytics helps shape and drive the strategy of an organization. Predictive analytics relies on techniques such as classification, regression, and other machine learning approaches.

Prescriptive

Prescriptive analytics is the nirvana of analytics as it is used to drive outcomes. Prescriptive analytics focuses on automation and optimization. Rule-based approaches and operations management techniques are used to determine how best to automate decision making. Often combined with predictive analytics, prescriptive analytics includes the use of recommendation engines and automated decisioning such as insurance quotes and mortgage approvals. Prescriptive analytics starts to merge with AI once it matures.

At the heart of each of these analytical stages is the need to understand data and apply the results of the analytics to solving business problems. Investing in analytical technology and building large data repositories does not provide an organization with analytical capability or value. It is the people with the business expertise that are required to solve business problems.

Business Analyst Contributing to Analytics Value

Business analysts have the knowledge of their organization and focus on a primary function, which is to identify and validate the needs of the business. As a project becomes clear, the business analyst will develop an understanding and determine the requirements. Analytical projects are no different. There are many roles that are involved in analytical projects, including data engineers, data scientists, and other analysts, but none of these roles can validate the results of analytics or ensure that the results can be used correctly.

Business analysts are necessary to ensure analytical projects run efficiently. The technical resources can determine the technical direction and start to build software, but the business analyst provides information, answers questions, and assists in removing barriers to ensure the project moves forward as expected. Business analysts are involved in understanding the business problem to be solved (the most important step in the analytics life cycle) and how business needs can be met.

Business analysts also provide clarity if multiple stakeholders are involved in the business understanding of the project. Facilitating discussion, gathering facts, reviewing outcomes, and reconciling the feedback from the stakeholders is needed. Essentially, the business analyst is the business representative, advocating for stakeholders and ensuring issues are addressed throughout the analytics project.

Assisting with testing the analytics model is another aspect where business analysts become involved. Through testing, the business analyst determines if the model will support the requirements and if it addresses the original business problem. Testing of analytics is not specific to software testing but includes the testing of the outcomes and the results of decisions made from predictions.

Business analysts are really the key to analytics business value. Business analysts assist with change management as they are experts on how analytics will be used in the organization. If analytical models are created and they do not assist in decision making or problem solving, the model is not providing business value. To get a better understanding of business value, the next section looks at different business problems solved by analytics.

Business Problems Solved by Analytics

As mentioned, analytics without results would not provide business value to an organization. To provide more clarity on the value of analytics, we will explore some of the different business problems addressed by analytics. The following examples include better decision making, campaign optimization, and other examples from Marriott and UPS.

The goal of many organizations includes becoming data-driven and making better decisions. For example, a leader of a fintech organization wanted to increase the use of data by making business data available to departments. Historically, the IT department had control over the data, and this prevented collaboration between departments. Dashboards were created to make data consistent and shareable, which resulted in removing hours in report preparation by IT and data being available in real time for decision making. Prior to this, data was available only after preparation by the IT department.

An online business wanted to have insight into customer activity and the life cycle to optimize marketing campaigns and make quicker decisions on how to increase customer satisfaction and brand loyalty.

Tom Davenport of Harvard Business School cowrote a book called Competing on Analytics in 2006 and highlighted multiple examples of how analytics provides business value. In an abridged version, Davenport highlights several organizations that have gained value from incorporating analytics. Two examples that stand out are that of Marriott International and UPS.

Marriott International has leveraged analytics to determine the optimal price for rooms using an analytics process called revenue management. Marriott has leveraged analytics for total optimization. Marriott has used analytics to develop systems that optimize offerings to customers and determine customer churn, or what number of customers switch to their competitors. The analytics are in the hands of revenue managers to make the best decisions on pricing, which directly determines the profit margin for the company.

Logistics cannot be successful without the application of operations management and analytics. UPS uses analytics to track packages in real time and predict customer churn as well as root-cause analysis of problems. For example, UPS can accurately predict customer churn by reviewing patterns and complaints. If a customer is predicted to churn, a salesperson contacts the customer to resolve the problem before churn can happen.

Imagine being able to protect the profit margin and prevent customer churn. What is the value of both of these analytics-enabled capabilities to these organizations? Also consider how a business analyst would be needed for process and other business context to make these capabilities possible.

Collaboration with Other Teams

Organizations tend to have siloed data, which means data is not centralized or integrated to the point where it is possible to see an enterprise view. Additionally, business expertise can focus on a department or a particular area. Analytics is rarely focused on one data source or part of a process. Business analytics are necessary to bridge the different areas of expertise and understand the impact of analytics.

An example of this would be the use of a predictive model. A model can be created for predicting customer churn, for example, but there could be many different factors that impact a customer’s experience. Marketing, sales, customer care, and product warranty could all be touching points experienced by a customer. How would an analytics project consider how these different viewpoints and processes impact churn? Business analysts help bridge these different areas and provide a horizontal view of the business versus a vertical one.

Skill Sets Used in Analytics

Math and statistics are some of the different skill sets mentioned in analytics. To expand on these further, business analysts become skilled at using different analytical techniques leveraged in the different analytical stages (descriptive, diagnostic, discovery, predictive, and prescriptive). Many of these techniques will be covered in chapters that follow, but analysts can be expected to understand descriptive and inferential statistics, applied math, some software development, and the analytical life cycle—all of which will be covered in the following sections.

Python and R

The current trend in analytics is to use open source tools: the leading tools are Python and R. Python is a full programming language that covers the programming capabilities to complete the data engineering as well as the machine learning algorithms to create models. R is used for statistics and analysis with strong visualization capabilities. Both tools are fueled by the different packages and libraries available.

Statisticians and academics developed R, and it currently has more than 12,000 packages available in its open source repository. Each package (synonymously used with library) contains many statistical capabilities needed by a business analyst. R surpasses Python with its output through visualization and the ability to publish findings in a document. The largest drawback to R is the ability to use the code in a production environment. While possible to do, R is hard to automate and use for operational processing.

Python has the same capabilities as R but excels at deploying and implementing large-scale analytics. Python code is easier to maintain and support. It was first a programming language before it was used in analytics. Today, most machine learning capabilities are available in Python first, then R. Python uses application programming interfaces (APIs) easily, and if you want to productionalize your machine learning code, Python is the simplest approach.

Analytics Project Life Cycle

Analytics professionals have adopted a life cycle for project delivery that is based on a data-mining approach used for some years. The life cycle is not sequential and is iterative as analytics projects are often about discovery. The life cycle provides the phases and steps that analytics professionals follow. Several of the large tech companies such as Microsoft and Amazon have incorporated these same phases and steps into their own methodologies. There are six primary phases to the analytics project life cycle: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Chapter 2 will explore the analytics project life cycle in detail, and the value of each of the phases will become clear.

Summary

This chapter focused on introducing the business analyst and the role of the business analyst in the analytics process. A business analyst role can take on several forms, but at the heart of the role, a business analyst focuses on understanding the requirements to address a business problem, working with data and providing business context to the data, reviewing the results of testing, and ensuring the business stakeholders are represented correctly in problem resolution. We also got introduced to the concept of analytics and how a business analyst gets engaged in the different analytics techniques. Business analysts are critical to analytics success due to business expertise and ensuring business value.