4 Types Of Business Analytics: It’s Time To Make Data Work

There are two ways one can explain business analytics. One’s creative and real-time, the other one’s more textbook-like. We’ll follow Rancho from 3 Idiots’ footsteps.

Imagine going to your favourite ice-cream parlour, where you obviously have a go-to ice-cream order. Let’s assume it’s blueberry cheesecake. Unlike the common flavours of chocolate, strawberry, and vanilla, blueberry cheesecake continues being underrated and unhyped for. The ice-cream brand decides to stop producing this flavour assuming it isn’t bringing in enough revenue.

All until a business analyst steps in, bringing insights supported by the past year’s data. The data suggests that there is an upward trend and growing likeness for blueberry cheesecake. For a new flavour, it has been ordered more frequently and has emerged as a successful flavour than its other new peers. The brand decides to retain the flavour.

That’s what business analysts do. They analyse and evaluate data, translate it into actionable insights that impact major business decisions. Not all heroes wear capes, but they do help businesses make more informed decisions, all with data.

As for the professor’s definition, business analytics is the use of mathematics and statistics to collect and analyse data in a way that can help businesses make data-driven decisions to improve business performance.

Where is Business Analytics Today?

Business analytics is everywhere. It is crucial for businesses to remain competitive, and so, to constantly improve their efficiency, they need business analysts. The demand ranges from IT and consulting firms to many companies in the finance, marketing, human resources and automobile industries, and more.

Thus, business analytics has become a sought-after profession, and the industry continues to grow in this tech-driven world.

Types of Business Analytics

Business analytics is a wide, diverse field of analysis. To navigate it better, let’s try and first look at the broader categories of business analytics. There are four main types of business analytics, each with its own purpose and practical applications. Let’s take a better look at each type below:

1. Descriptive Analytics

Descriptive analytics refers to the process of evaluating existing, historical data to explore changes and trends. This helps monitor or determine a unit’s response to a particular set of variables. Studying past behaviours, patterns, insights, and outcomes, makes good use of existing data and helps find the base of newer insights. Descriptive analytics answers the question of “what” happened.

Benefits of Descriptive Analytics

• Data availability: Since descriptive analytics considers historical data, there’s always presence and access of the data, thus taking out the collection stage and saving time.
• Identifying trends: The data source is historical, meaning a good chunk of it can be made available. This makes it easier to identify any patterns and trends in the data, which can be speculated or expected in the current scenarios too.

Limitations of Descriptive Analytics

• Less predictive power: Descriptive analytics makes use of historical data, which helps understand past data well but does not quite help predict future outcomes.
• Lack of causation: While descriptive analytics can identify correlation between different variables, it may not be able to identify the cause or the “why” behind each.

Examples of Descriptive Analytics

Annual revenue reports, social media analysis, customer segmentation, and portfolio analysis are a few examples of descriptive analytics.

2. Diagnostic Analytics

Diagnostic analytics lies in the nitty-gritties of data. It takes descriptive analytics a step further by uncovering the underlying causes of any patterns or trends that are extracted from the data sets. The outcomes are dissected and evaluated further to understand the root cause of said outcomes. Diagnostic analytics answers the question of “why” it happened.

Benefits of Diagnostic Analytics

• Digs out the root cause: Diagnostic analytics works to pinpoint the exact cause of trends and patterns, as understood post summarising the data, and helps process problems and enable better-target solutions and opportunities.
• Enhanced problem solving: Since diagnostic analytics focuses on the “why” of the trends, it marks an overall structured approach to problem-solving. This helps build more effective strategies and solutions in the future.

Limitations of Diagnostic Analytics

• Subjective nature: One of the biggest benefits of diagnostic analytics is its ability to uncover the root cause of trends. However, the same analysis can often be subjective, which may be influenced by the business analyst’s understanding.
• Time-consuming: Diagnostic analytics dwells in the details, making the process far more time-consuming and complex. As data sets get larger and more complex, the time taken to analyse such data increases too.

Examples of Diagnostic Analytics

Understanding customer behaviour, gauging market demands and trends, and fraud detection are a few examples of diagnostic analytics.

Also Read – Here’s your 5 step roadmap to Master Business Analytics

3. Predictive Analytics

Predictive analytics refers to a type of analysis conducted using machine learning algorithms and statistical models to predict future outcomes based on existing, historical data. It is a more advanced method of data analysis that helps predict future trends and potential risks before-hand. Predictive analysis answers the “what to expect” for businesses.

Benefits of Predictive Analytics

• Effective risk management: Predicting and identifying any potential risks early on gives businesses a chance to develop mitigation strategies and try and minimise the negative impact the risks can leave on the business.
• Informed decision-making: With a somewhat idea of what the future may look like with anticipated trends and outcomes, businesses can make more informed and improved decisions.

Limitations of Predictive Analytics

• Changing organisational environments: If underlying environments are seeing a change at an organisation level, businesses may not be able to rely on predictive analytics fully. More the change in business environments, lesser the effectiveness of predictive analytics.
• Lack of accuracy: Like the name suggests, this type of analytics can only predict data. There is and will be a lack of complete guarantee or accuracy, and the predictions shared will always come with a degree of uncertainty.

Examples of Predictive Analytics

Fraud detection, optimising market campaigns, and predicting consumer behaviour are some examples of predictive analytics.

4. Prescriptive Analytics

One of the more advanced forms of analytics, prescriptive analytics goes beyond summarising, diagnosing and predicting data; it focuses on recommendations and suggestions of the next-ups, based on past data. This type of analytics helps make actionable strategies formulated based on the data in hand. Prescriptive analytics, thus, answers the “what should we do” for businesses.

Benefits of Prescriptive Analytics

• Optimised processes: Prescriptive analytics suggests actionable items to work on. It enhances operational efficiency and resource allocation to a great extent, optimising the preparation for the next step.
• Improved decision-making: Prescriptive analytics go a step further from predictive analytics. The insights shared become a lot more reliable to base decisions on, empowering businesses to make confidently more informed decisions.

Limitations of Prescriptive Analytics

• Complicated datasets: Like predictive analytics, prescriptive analytics can deal with large, complex data sets making the process a lot more time-consuming and extensive.
• Lack of uncertainty: While prescriptive analytics is slightly more confident and informed than predictive analytics, it still holds an element of uncertainty which cannot quite be proven until later.

Examples of Prescriptive Analytics Price forecasting, improving asset management, and risk management are examples of prescriptive analytics.

Also Read – Want a job in Business Analytics? Read top 6 job profiles & salaries

Popular Tools and Technologies Used in Business Analytics

Since the realm of business analytics is quite technical in nature, the tools and techniques used in this domain are just as advanced. All the four types of business analytics cover different aspects of analysis and insights, thus, each makes use of different types of tools too. Here’s a short list of some tools and technologies commonly used in business analytics:

• Advanced MS Excel

MS Excel goes beyond basic calculations and spreadsheets. It has some advanced tools and features that can be used to understand complex sets of data and simplify it into insights that are easy to understand. Excel is an excellent tool to clean, organise, and prepare data for analysis, with advanced functions to perform statistical analysis and analyse data trends and patterns.

• Tableau

Data visualisation is a wonderful technique to understand and present data. And what better tool than Tableau to use for data visualisation? Tableau makes analysing data a lot more fun, with an interactive and intuitive platform. It can connect to many types of data sources, including cloud applications and spreadsheets, making the process a lot smoother. Tableau’s user-friendly interface takes the cake, offering an easy platform and a single view of analysed data.

• Structured Query Language (SQL)

SQL is a programming language that allows the managing, storing and retrieving of large data sets. It is largely used to communicate with databases and get the required information. Having a good hang of SQL is quite essential for business analysts, as it is an efficient tool to manipulate and analyse data. The process of extracting insights post data analysis is a lot smoother with SQL.

You can learn (and master) all these tools, and more, with Proschool – a reputed analytics institution in India. Proschool’s course in business analytics comprehensively covers the theory of business, as well as the statistical end where you can gain a solid understanding of these tools which will be very useful in your journey as a business analyst.

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Case Studies: Business Analytics At Work

Supported by the four pillars of types of business analytics, this field has grown quite a lot in the past few decades. Watching it bloom in the real-world, with real-time differences made by business analytics is a delight. Following are some bite-sized success stories of how business analytics has helped cater to the the biggest of companies out there:

Netflix’s “Watch Next”

It is the era of personalisation and Netflix is doing it just right. The brand uses advanced algorithms to analyse viewing history and preferences of users. Based on this data analysis, Netflix provides personalised recommendations that have significantly impacted customer retention and user satisfaction.

Amazon’s “For You”

Much like Netflix, Amazon is making a great effort in enhancing user journeys. The e-commerce giant uses analytics to specifically segment customers based on their buying behaviour and preferences. Such an in-depth analysis allows Amazon to customise product recommendations and marketing campaigns, which has worked wonders in increasing platform traffic.

Airbnb’s Demand Forecast

Airbnb is available world-wide, in many popular and some uncommon locations too. It uses analytics to predict and understand the demand for accommodations in different locations. This analysis helps optimise the platform’s pricing for different accommodations while enhancing its availability strategies.

Also Read – Top 10 Business Analytics Courses | Online & Offline

Ethical Considerations

In a tech-driven field like business analytics, there are many ways of dealing with data. To ensure that the data is always used in the right, legal, and acceptable ways only, here are some code of ethics to be constantly mindful of:

• Data Privacy and Security

Business analytics is all about dealing with data. The first and most important step is to collect data with informed consent of the users. Next is ensuring that the data collected is authorised and in complete compliance with data privacy regulations is a must. Furthermore, collected data should be kept secure and protected from unauthorised access and breaches.

• Transparency and Fairness

Insights can be drawn from different datasets and algorithms, but it is always suggested to be as transparent as possible about the data used. Algorithms can lead to improper results if there is bias involved, further leading to discriminatory outcomes. Such biases should always be kept at bay, to keep the process of analytics fully fair.

• Misuse

The process of data analytics is as beneficial as it can be tricky. There is always a risk of data being misused, which can have drastic effects on those relying on this data. Avoid any wrong use of data analytics for harmful purposes.

• Social Impact

The profession of business analytics aims to help businesses grow, and perhaps boost the customers’ experience with the brands too. Similarly, data analytics can be used to tackle environmental and social challenges. The data-driven decisions must always be made mindfully, considering the social impact it may have.

As a business analyst, data and data analytics are your Bible. To be reliable and credible, keeping these ethics in mind is always the better way to go. It builds a longer and more truthful, and strong career for you in business analytics.

Also Read – Top 8 Tools Every Business Analyst Should Use To Solve Problems

How Proschool Packages Business Analytics For You?

Are you fascinated by the world of business analytics? A good introduction to business analytics from a credible source is just what you need to get started. You’re in luck! Because Proschool, ranked among the top 10 analytics institutes of India, packages the entirety of business analytics in an extensive course made for analytics enthusiasts like you!

Here’s Why You Should Choose Proschool:

• Proschool equips you with the “business” and the “analytics” of business analytics for you, with all the right tools including Tableau, Excel, and programming languages like Python and SQL.
• Classes at Proschool are quite interactive, where the professors solve all your doubts and questions.
• Get a good insight of business analytics in the real-world, by learning from 15+ case studies and projects.
• Proschool also helps build and enhance essential soft skills as well as technical skills, such as problem-solving mindset.
• The faculty at Proschool also focus on sharpening your skills and that prepare you to put your best foot forward in your interview process.
• And finally, Proschool also offers a placement program that connects students with leading companies across various industries that are hiring for business analysts.

Conclusion

The vastness of business analytics is astonishing, but equally exciting. And for all its diversity, the field of business analytics is quite structured and segregated too. Like the four types of business analytics, there are many different divisions and components that make up the whole of business analytics.

Together, it marks the presence of a promising field with many growth opportunities lined up for businesses, business analysts, and the society at large. It’s a long, wonderful path that awaits us all ahead. Happy journey!

Frequently Asked Questions

1. What are the types of business analytics?

There are four types of business analytics:

• Descriptive analytics: This type of business analytics involves the analysis of historical data, to extract insights based on trends and patterns observed in the data.
• Diagnostic analytics: Taking descriptive analytics a step further, diagnostic analytics evaluates the data in hand to understand the cause and effect of certain outcomes.
• Predictive analytics: After understanding and evaluating the data, predictive analytics formulates trends and patterns to watch out for, based on existing data.
• Prescriptive analytics: This type of analytics focuses on action. Prescriptive analytics works on strategy suggestions and recommendations for businesses to apply into action.

2. What are the 4 V’s of business analytics?

There are 4 V’s of business analytics: Volume, velocity, variety, and veracity. Volume covers the amount of data being generated, velocity captures the speed of data generation, variety refers to the different types of data, and lastly, veracity talks about the quality and accuracy of the data.

3. What are the 4 components of business analysis?

Business analytics can be broadly categorised into these four components: Business requirements: This component involves understanding the needs, objectives and goals of the business. Process analysis: This is the study of existing business processes to identify areas of improvement. Data analysis: This component covers the collection, cleaning, and analysing data into making proper insights. Solution design: The final component which involves designing and developing solutions based on the insights extracted from the data.

4. What are the 7 pillars of analytics?

Analytics stands strong on 7 pillars that govern the process of data analytics. They are as follows: Data governance, to ensure quality and security of data. Data management, to store and manage data effectively. Data visualisation, to create clear visualisations to communicate insights. Predictive analytics, to use statistical models to predict future trends. Prescriptive analytics, to recommend actionable strategies based on insights. Self-service analytics, to encourage users to independently access and analyse data. Advanced analytics, to put sophisticated techniques like deep learning to process complex analysis.