Business Analytics and Business Analysis: The Definitive Guide

Table of Contents

1. Introduction
2. Business Analytics: Definition
3. Business Analytics vs Business Intelligence
4. Analysis vs Analytics
5. Business Analytics: Main Tools
6. Types of Business Analytics
7. Business Analytics vs Data Analytics
8. Business Analytics: Current Job Market
9. Conclusion

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Introduction

The aim of this piece is to articulate Business Analytics as a term, its relevance within the professional workplace and to highlight the growing trend of Business Analysts who are opting to learn Business analytical skills. Before we dive into this, it is strongly advise you have a good, foundational understanding of business analytics, conceptually, the main features of business analytics and what the main differences between analysis and analytics is.

Business Analytics: Definition

Business Analytics is the application of statistical, logical and computational methods for the analysis of past data in order to make predictions, improve insight and help implement business strategy. Unlike Analysis that involves describing and articulating past data, Analytics uses historical data to obtain new insights, make astute predictions and discover potentialities.

Business Analytics vs Business Intelligence

There can be some confusion with regards to Business Analytics and Business Intelligence. Whilst the two phenomena are similar, used in conjunction with each other and enhance one another’s findings when used correctly, there are distinct differences.

Firstly, Business Intelligence (BI) traditionally focuses on using a consistent set of metrics to measure past performance and steer business planning; the known concept of Key Performance Indicators or KPIs comes into play in relation to Business Intelligence. Moreover, Business Intelligence focuses on descriptive summaries, whereas Business Analytics focuses on prescriptive and predictive summaries and analysis.

Analysis vs Analytics

Before we dive into Business Analytics further we should also make the distinction clear between Analysis and Analytics clearer. Analysis is the investigation of why something has happened. When we look at the variance between actual figures and the numbers previously submitted on a company’s budget plan for example, i.e. Budgeted vs Actuals, we are performing analysis.

For example, if we wish to understand the underlying business performance of a company we would be analyzing the variance of a financial item such as revenue. In a nutshell, analysis looks backwards, towards the past in order to understand how performance has been previously against the expectations of your stakeholders.

Stakeholders have expectations based upon carefully analyzed data through the form of analysis.  This is usually articulated via a formal Business Plan. This plan also contains predictions on future performance. Because the next set of data (the predictions) is forward looking, we need to include certain logical, statistical and or informational models to supplement our predictions in order to make them more realistic. Sometimes, looking in the past is not the best prediction of what could take place in the future.

So, if a company knew the rate of corporation tax was increasing from 10% to 25% this rate change would be incorporated into their business models; this is a form of analytics as opposed to analysis. The analysis studies the data at the current rate, whereas analytics incorporates the newer rate. Therefore, we need sophisticated models to accurately predict potential outcomes. Analytics presents us with a range of models that help us create scenarios that enable us to predict future performance.

In essence, analytics is a strategic asset that enables management and the board to make data-driven and better informed decisions about future performance in the present.

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Just remember, analysis = past, analytics uses past and present data to map out what could happen = future.

Business Analytics: Main Tools and Techniques

The main components of a typical business analytics dashboard include:

• Data Visualization
• Forecasting
• Data Aggregation
• Text Mining
• Data Mining
• Predictive Analytics
• Optimization

Data Visualization

Data visualization is the graphical representation of information anddata. Usually depicted with graphs, maps and charts, data visualization toolscan provide an accessible way to ingest and understand trends, correlations,patterns, outliers and other valuable aspects from data. Prominent tools include:

• Tableau
• Power BI
• Qlikview
• Google Charts

Data Aggregation

Data aggregation is the compilation of data and information fromdatabases with the intention of combining datasets for data processing. Theinformation gathered can be summarized and presented. The data may be gatheredfrom multiple sources and doesn’t necessarily need to come from a singlesource.

Data Mining

Data mining is the process of finding anomalies, patterns andcorrelations within large data sets to predict outcomes and enhance data-drivendecision making.

Forecasting

The process of analyzing historical data from a specific period in orderto make informed estimates that are predictive in determining future events isa forecast. Think of the weather “forecast” for a common example. The weatherpeople use past and present data to rationally guess what tomorrow to nextweek’s weather is likely to be.

Text Mining

Sometimes referred to as text analytics text mining is an artificialintelligence (AI) technology that uses natural language processing (NLP) totransform the free (unstructured) text in documents and databases intonormalized, structured data which can be used for analysis.

Predictive Analytics

Predictive Analytics is the use of data, statistical algorithms andmachine learning (ML) techniques to identify the possibility and likelihood offuture outcomes based on historical data. Using a variety of statisticaltechniques that can create predictive models, data can be extracted fromdatasets, identify patterns and provide scores for a plethora of organizationaloutcomes.

Optimization

Once trends have been deduced and predictions have been made, businessescan employ a host of simulation techniques to test out optimal or ideal scenarios.

Types of Business Analytics

Business Analytics is usually arranged into the following categories:

1. Descriptive Analytics
2. Predictive Analytics
3. Prescriptive Analytics
4. Diagnostic Analytics

Descriptive Analytics

Descriptive Analytics is the interpretation of historical data to betterunderstand changes, fluctuations and outliers that have occurred within abusiness.

Prescriptive Analytics

The combination of both descriptive and predictive forms of analyticsequals prescriptive analytics. Prescriptive analytics provides insight on whatmight take place, providing a process by which users can anticipate what, whenand why an occurrence could take place.

Examples include:

• Tracking player spending in casinos
• Monitoring sales of a good or service
• Tracking productivity
 

Diagnostic Analytics

Diagnostic analytics is used as beta test data and derive from theresults what should be used in further work. You use diagnostic analytics tohelp find data-quality issues; making you aware of them and helping you fix them.

Business Analytics vs Data Analytics

Data analytics is an umbrella term often used to describe the science ofanalyzing raw data in order to transmute the data into useful information fromwhich metrics and trends can be revealed. Both data analytics and businessanalytics aim to optimize operational efficiency. Business analytics isspecifically geared towards business use cases. Data analytics is intentionallybroader; both business intelligence and reporting and online analyticalprocessing (OLAP) fall under the data analytics umbrella.

Business Analytics vs DataScience

Data science is a multidisciplinary field that utilizes scientificsystems, methods, algorithms and other models to study both structured andunstructured data in order to derive where information comes from, what itmeans and how it can be transformed into a valuable resource for informationaland technological purposes.

Data science is a combination of data analysis, statistics, machinelearning and other related methodologies used in conjunction to betterunderstand and derive meaning from the influx of data associated with therapidly changing world of information technology.

Business Analysis vs Business Analytics

A business analyst (BA) is a conduit between software development andbusiness. A BA is expected to present data and information to both sets ofstakeholders to ensure constant, transparent and coherent alignment during aproduct development build or a project. Some of the main duties of atraditional BA include:

• Evaluating business processes for efficiency, cost, and other valuable metrics
• Communicating insights with business teams and key stakeholders
• Preparing strategic recommendations for process adjustments, procedures and performance improvements
 

Business Analysis is the underlying skill set required for aprofessional Business Analyst. Institutions such as the IIBA. The CharteredInstitute of IT and others have a defined criteria of what skills constitute a BA. Business Analytical skills do not fall within the traditionally acceptedframework of what a BA is expected to perform; this however might be changing. You can read our guide to Business Analysis by clicking here.

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A BI Analyst, Data Analyst, Data Scientist or any Business Analytics professional might be expected to perform the following duties:

• Design and maintain data systems and databases and handle requests and troubleshooting queries
• Perform data and or text mining in preparation of analysis
• Prepare reports and present the findings to stakeholders
 

Common Skills and Tools

There is a lot of crossover between the areas. A data and analyticsprofessional are expected to effectively communicate her or his findings in acompelling and coherent way. Common tools across both disciplines include:

• Microsoft Excel
• SQL
• Tableau/ Power Bi/ Qlikview
 

Furthermore, as you will see in the next section, many BAs are learning more Analytical skills in order to supplement their natural skill set and to maintain relevance within the rapidly changing job market.

Business Analysis: Current Job Market

With the emergence of Big Data, AI, Machine Learning and other relatedroles such as Product Owner, BAs are finding themselves needing to meet thenewer requirements of the rapidly changing work landscape. This means, many BAs are opting to learn more computational and analytical subjects such as Businessand Data Analytics, Python programming and Tableau /Power BI development.

According to LinkedIn Learning, the most common courses taken by people whosejob roles is Business Analyst are in the following table:

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LinkedIn has a network of some 500 million users, many of whom are“Business Analysts.” Of those who elect to learn through the platform these arethe top 5 courses with their annual growth from last year.

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Fastest Growing Skills for Business Analysts

As you can clearly see, more traditional BA subjects such as Agilemethodologies, or Requirements gathering, or Business Process mapping do notappear. Why is this? The demands of the global economy appear to be shifting. More and more companies now demand different skills, and many BAs appear to becognizant of this and are adapting accordingly.

More Evidence

According to ITJobsWatch, a UK based service that monitors and tracks trends within the UK ITjob market “Business Analysis” as a search term has declined by 25% whencompared to this time in 2019 and 2020 respectively. On this graph, the closer to zero, the better, because the rank is higher (most popular = number 1). Therefore, the 2019 position may be “lower” but it is actually “higher;” the measurement is inversed.

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Just like the LinkedIn data, Business Analysts from both an employee andemployer perspective are moving away from more traditional BA concepts andappear to be moving closer to more STEM (Science, Technology, Engineering,Mathematics) subjects and skills.

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Business Analyst: Trends

As you can see from the graph above, the number of live vacanciesadvertised for BAs has been on a relatively steep decline. From a figure of 5,042 in 2019 to 1,808 in 2021 (so far), meaning there’s been a 64% decline inBusiness Analyst hires according to the ITJobsWatch data. Whilst this is only one source from one company in one country, it is worth noting.

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Business Analytics within Current Job Market

Data is more valuable than oil in today’s world, so the ability toconvey stories and implement business strategy derived from data isessential. In virtually all industries, banking, retail, medicine, insurance,FMCG, Finance, entertainment and so on, a company’s ability to be able toutilize its abundance of user and business data is crucial to survival andsuccess. It is no surprise then that Business Analytics is trending higher onboth ITJobsWatch and Google Trends respectively.

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For this graph above, we can see the two terms fluctuate in terms ofinterest and searches. Whilst there’s no clear distinction, Business Analysismight be on a slow decline due to the changing interests and the direction inwhich the economy is moving.

The two lines are almost inseparable.

Within the UK job market for example, employees according to ITJobsWatch have 25% less interest in Business Analysis as a skill and as aprofession. None of the top courses enrolled by Business Analysts and none ofthem were for more traditional BA topics. Stay relevant, stay in touch withothers and make sure you keep up with the latest news and information.

Conclusion

There’s certainly a shift taking place for Business Analysts. More and more employers are looking towards business analytics as a skillset and requirement. Whilst the market is changing, now is the time to learn and grow. Business analysis won’t be disappearing anytime soon, but it would be good tofutureproof yourself; learn some new business analytics skills and pushyourself to the next level.

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