Modern Business Analytics

Chapter 1. The Role of the Business Analyst and Analytics

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If you are new to business analytics or considering a career in this space, this chapter will be particularly useful to get you started or transition you from a business analyst into analytics. If you are already familiar with the fundamentals or currently working as an analyst, 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 is chosen the first time which results in improved efficiency, effectiveness, and a better bottom line. There are many business roles that leverage data, but the business analyst is the one that provides the necessary context to create business value.

If you are researching what knowledge and skills a business analyst must have, 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 the role 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 . This isn’t always easy, because the business landscape of an organization is 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, 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. This 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. Often analytical projects start with understanding the symptoms of a problem, before the problem is clear. For instance, let’s say you work for a telecom company and it appears customer attrition or customer churn (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 well the problem is understood and to determine where analytics might be used to solve the problem. Increased customer attrition is a problem, but it is not yet apparent how analytics might help solve it. Thus, requirements analysis can be an iterative and discovery process. Requirements and how analysts help solve problems will be covered more in a later chapter.

As a business analyst, you are often involved in addressing new requirements due to the constantly changing business environment. Because of this, the best way to understand the role of the business analyst 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 are researching 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 are using 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 also 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.

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 leveraging effective methods	Presentation of possible solutions to a problem to an executive audience
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	Knowledge of the manufacturing industry and using this knowledge to complete problem solving
Statistics	The practice analyzing data to infer results	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 interface connections interact

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. Based on the different skills, you, as a business analyst, are involved in different projects, duties, and activities which you will be expected to perform, which determines your business analyst’s responsibilities. The skills you adopt will be broad and will help shape your future projects, responsibilities, and day to day activities as a business analyst.

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. Table 1-2 provides some of the primary responsibilities of business analysts and is not an exhaustive list.

Table 1-2. Business analyst responsibilities

Responsibility	Examples
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
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 to validate the overall quality, functionality, performance, and reliability before and during 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. In the next section, I explore 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 of the 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 a number of 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 together on projects in the organization.

Marketing analyst

Marketing efforts are directly linked with organizational strategy such as increasing revenue or number of customers. Marketing has shifted into digital marketing, also known as online marketing, because most marketing occurs via the internet. 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 for 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 what choices a customer might make 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 a 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 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 reports related to financial status.

Both financial and marketing analysts are examples of business-focused analysts. Analysts can also have a different focus when aligned with information technology such as a functional, system, and data analyst.

Functional Analyst

Functional analysts are specialized 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 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 examples above 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 leverage process and technical knowledge to identify opportunities for improvement and recommend technical solutions. Technical analysts, like a functional analyst, leverage software development methodologies and may model data, prepare design diagrams, complete testing, and train users. A software development methodology refers to a framework that is used to structure, plan, and control the process of developing an information system. Examples of software development methodologies include Agile or Scrum. These methodologies help manage, plan, and streamline the development process.

The functional analyst 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 use project management skills. Project management is the practice of initiating, planning, executing, controlling, and closing the work of a team to achieve specific goals and meet specific success criteria within a specified time. It involves coordinating tasks, resources, and stakeholders to deliver a project within scope, time, and budget constraints. Project management helps ensure that the goals of a project align with the strategic goals of the business.

Functional analysts contribute to the requirements for analytical projects by identifying measure, metrics, indicators, and other data needed for improvement opportunities. Process improvement requires performance management for measurement and monitoring of the improvement to check position and make decisions about next steps. Many of the requirements to be a functional analyst are needed by a system analyst. I 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 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 role 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 together 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 work as team members on analytical projects which I review next.

Data Analyst

Data analysts work with data to find insights that can be leveraged for business value. One area that data analysts differ from the other types of analysts mentioned is the hands-on involvement working with the data. 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 the insights and formulate data findings to communicate to organizational 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.

As a data analyst, you will leverage technical skills for data collection, 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 also can cleanse and transform data to apply business rules or increase data quality and usability.

During the process of working with data, data analysts partner with other analyst types 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 large data sets to support improved decision making versus improving processes.

There are fine lines between a business analyst and a data analyst role, with the primary difference being data analysts focus more on the technical aspect of wrangling and analyzing the data. 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 I 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 (https://www.gartner.com/en/topics/data-and-analytics). 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 of where analytics is used.

Analytics will be explained in detail throughout this book, but to give it a definition, it is an approach organizations develop to support the analysis of data using statistics, math, and algorithmic models to improve decision making. Analytics can be descriptive, diagnostic, discover, predictive, or prescriptive according to Gartner (https://www.gartner.com/en/topics/data-and-analytics). These primary analytical types have been adopted 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 (/https://static.usenix.org/event/usenix99/invited_talks/mashey.pdf) 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, with the combination of data, computing power, statistics, and math, the value of data started to evolve. Big data is mostly the norm now, however, the concept of the data explosion has continued with more data points (data that is 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, well 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 US use EZPass (https://www.wsj.com/articles/how-to-understand-the-data-explosion-11638979214, 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 climate 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 the catalyst 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 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 surmise 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 into the business world. 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 everyday to service its customer. 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 non-business user referred to as ARPU (pronounced R-PU) would be known to a business analyst working in this industry.

Data is generated by technology which 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 for 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 on 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 possible root causes or provide valid recommendations to problem solve without context. Business analysts bring the experience and expertise needed to determine if data insights actually exist. This is why business analysts are a key part of the analytics process.

Analytics

Many terms exist that can be merged under the word of analytics. It is 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 as a way 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 is used to describe all activities and capabilities that an organization uses to exploit data sets for insight.

Gartner has determined the primary analytical techniques that organizations use and these were mentioned prior: descriptive, diagnostic, predictive, and prescriptive (https://www.gartner.com/en/topics/data-and-analytics). Often another is thrown in which 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 to answer the question: What has happened? This uses business intelligence tools, dashboards, and scorecards to monitor and manage the business, to address business questions such as: 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 such as; 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 form such as uncovering hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. 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: What is happening next. Example 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 drives 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 is merging with artificial intelligence (AI).

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 Analytics and Data Analytics

Data analytics emphasizes technical proficiency, with a focus on data handling, statistical analysis, and algorithmic application. Business analytics, on the other hand, requires a more strategic and business-centric approach. However, both fields necessitate a foundational understanding of business principles, data management, and visual communication. This synergy underscores the multidisciplinary nature of analytics, where technical expertise and business acumen coalesce to drive insights and decisions.

Business Analyst Contributing to Analytics Value

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

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 business representatives are contributing to the business understanding of the project. They use techniques such as facilitating discussion, gathering facts, reviewing outcomes, and reconciling the feedback from different business representatives.

Assisting with testing of 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, I will explore some of the different business problems addressed by analytics. The first example I will look at is improving the decision making process.

Example 1

The goal of many organizations includes becoming data-driven and making better decisions. For example, a leader of a financial technology 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 internal departments. Dashboards were created to make data consistent and shareable resulting in removing hours in report preparation by IT and data being available real-time for decision making. Prior to this, data was only available after preparation by the IT department (https://hbr.org/2006/01/competing-on-analytics).

Example 2

An online business wanted to have insight into customer activity and the life cycle to optimize marketing campaigns where the business was able to optimize marketing campaigns and make quicker decisions on how to increase customer satisfaction and brand loyalty (https://hbr.org/2006/01/competing-on-analytics).

Example 3

Tom Davenport of the Harvard Business School wrote in his book called “Competing on Analytics” and highlighted multiple examples of how analytics provides business value.He highlights several organizations that have gained value from incorporating analytics. Two examples that stand out are that of Marriott International and United Parcel Service (UPS).

Marriott International has leveraged analytics to determine the optimal price for rooms using an analytics process called revenue management. Marriott has used analytics to develop systems that optimize offerings to customers and determine customer churn 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.

Example 4

Logistics cannot be successful without the application of operations management and analytics. UPS uses analytics to track packages real-time and predicting customer churn as well as root cause analysis of problems. An example includes UPS can accurately predict customer churn by reviewing delivery 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 profit margin and prevent customer churn. What is the value of both of these analytical enabled capabilities to these organizations?

Collaboration with Other Teams

Organizations tend to have siloed data, which means data is not centralized or integrated hindering an enterprise view. Additionally, business expertise can be siloed as well. 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, but there could be many different factors that impact a customer’s experience. Marketing, sales, customer care, product warranty could all be touch 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.

Python and R

The current trend in analytics is to use open source tools, and Python and R are the leading tools. Python is a full programming language which 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 are fueled by the different packages and libraries available.

Statisticians and academics developed R and it currently has over 12 thousand packages available in its open source repository. Each package (synonymously used with library) contains different statistical capabilities that may be needed by a business analyst. R has strengths with its visualization capabilities and the ability to publish findings in a document.

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 they become available in R. Python uses application programming interfaces (APIs) easily and if you wish to productionalize your machine learning code, Python is the simplest approach.

Analytics Project Lifecycle

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 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. I will explore the analytics project life cycle in detail in chapter 2 and it will become clear the value of each of the phases.

Summary

In this chapter I 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 basically, a business analyst focuses on understanding and defining the requirements of business problems, 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. I also introduced 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.