The 4 Types of Data Analytics and How to Apply Them

Ever since the internet hit the mainstream, businesses have been collecting and storing gargantuan volumes of data. Every button clicked, every name entered, and every purchase made are all data – waiting to be exploited.

Google gets at least 3.5 billion searches every day;

Whatsapp users exchange more than 65 billion messages per day;

And, Facebook users collectively share more than 100 terabytes of data per day.

But collecting and storing data alone doesn’t do any good, or let alone makes any sense. Sticking to just collection and storage is similar to having unlimited superpowers and choosing to sleep all day, every day.

That is where the four types of data analytics come in – the four musketeers that can loot information from data sets and deliver actionable insights – assisting companies in making nothing less than data-driven decisions.

If you’re wondering about the different types of data analytics and how they help businesses, then this article is for you. Let’s find out how each type utilises data sets and how their outcomes can help companies to prosper.

What is Data Analytics?

Data analytics is the process of analysing raw data to find trends, draw conclusions, and answer questions. It employs a wide range of analysis techniques (including math, statistics, and computer science), to perform a multitude of studies, from simply analysing data to theorising ways of collecting data and creating the frameworks needed to store it.

Data analytics can be used by different entities, such as businesses, to optimize performance, foster growth, and maximise profits.

Why is Data Analytics important?

Data analytics can improve a business’s visibility and help it gain a deeper understanding of its processes and services. It provides insight into the customer experience and problems, helping businesses better analyze customer trends and satisfaction. This will lead to new and improved products and services.

For example, a shoe manufacturer may look at sales data to determine which designs to continue and which to retire, or a healthcare administrator may observe inventory data to determine the medical supplies they should order.

Steps in Data Analytics

Here are the steps involved in the data analytical process:

  1. Determine the data requirements or how the data is grouped. Data may be grouped according to age, demographic, income, or gender, and may be numerical or divided by category.
  2. Collect the data through various sources such as computers, online sources, cameras, environmental sources, or through personnel.
  3. Organise the data on a statistical spreadsheet software (i.e. Excel) so that it can analysed.
  4. Before analysing the data, it needs to be cleaned up to correct any errors before it is sent to a data analyst. This can be done by scrubbing it and ensuring there’s no duplication or error and that it is not incomplete.

The 4 Types of Data Analytics

Graph showing the maturity of types of data analytics. Image Credit: gartner.com

The four different types of big data analytics can be summarised as:

  1. Descriptive analytics – What has happened?
  2. Diagnostic analytics – Why did it happen?
  3. Predictive analytics – What is likely to happen?
  4. Prescriptive analytics – How to make it happen?

To help you better understand the different types of data analytics, let’s consider the analogy of Sherlock Holmes — the world-famous fictional private detective.

In fact, data analytics can be seen as a type of investigation — the only difference is we’ll have a pool of data to evaluate, not pieces of evidence like in a crime scene.

Let’s take a closer look at the 4 different types of data analytics and their relevance to real-world applications.

1. Descriptive Analytics

Descriptive analytics describes “What happened?” It is the most elementary form of big data analytics and can be considered as the starting point of your analytics strategy.

Google Analytics extensively uses descriptive analytics to convert huge volumes of data into digestible information. Image Credit: support.google.com

For Sherlock Holmes, descriptive analytics would be similar to looking at the shreds of evidence at a crime scene and explaining to Dr Watson what has happened and just that. Holmes has yet to start his investigation, and descriptive analytics can be considered as his collecting and presenting the evidence.

Out of the four types of data analytics, descriptive analytics tries to deliver a summary view of “What happened” by exploiting historical data (data collected about events that took place in the past). As big data involves multiple data types collected from numerous sources, comprehending such volumes will be tedious or let alone impossible for human brains.

In other words, this process allows you to describe and present data in a format that doesn’t baffle the non-technical audience before proceeding with the actual investigation. It is rather fitting to note that the outputs of descriptive analytics are used as inputs for advanced types of data analytics, such as diagnostic, predictive, or prescriptive.

With this type of data analytics, companies can determine which among their strategies helped in increasing sales and revenue and which didn’t – without the answer as to why it did or didn’t.

A simple example of this is A/B testing a paid ads campaign and determining which iteration delivered the best result. Methods such as data aggregation and data mining are used in descriptive analytics to yield useful insights from historical data.

Here are some applications of descriptive analytics:

  • Calculating the average duration taken by learners to complete a course.
  • Preparing monthly profit and loss statements.
  • Measuring the ROI (return on investment) of a social media campaign.
  • Inventory tracking at a warehouse.
  • Benchmarking yearly sales and revenue of a company.

2. Diagnostic Analytics

Diagnostic analytics answers why did it happen? It is also called as root cause analysis and can be considered as the successor of descriptive or the next advanced stage of analytics.

Here’s a video explaining diagnostic analytics in simpler terms with an easy to follow example.

With Sherlock Holmes’s reputation as “the master of deduction,” diagnostic analytics is similar to his conclusion of why a crime was committed just by looking at the pieces of evidence and interrogating the witnesses. This will help him uncover the criminal’s dirty motives and determine whether their existence can put anyone in danger.

In this type of data analytics, techniques such as data discovery, data mining, drill-down and correlations are utilised to determine the factors that led to a particular outcome.

As previously mentioned, descriptive analytics results are used as inputs for diagnostic analytics. By merely looking at data, analysts cannot answer the questions such as why a particular store witnessed an increase in foot traffic. With diagnostic analytics, analysts can uncover several hidden stories in data.

The majority of diagnostic analytics solutions use machine learning (ML) and artificial intelligence (AI) to recognise patterns, detect unusual occurrences, and identify the anomalies and the factors that contribute to the key performance indicators (KPIs).

In some instances, experts may confine the scope of diagnostic within descriptive analytics itself and thereby discuss 3 types of data analytics.

Here are some applications of diagnostic analytics:

  • HR departments can determine how absenteeism or overtime affects the performance of an employee.
  • Health organisations can discover the reason why there is a sudden increase in patient inflows.
  • Marketers can comprehend why a particular campaign outshined the rest.

Once you have the answers to “What happened” and “Why did it happen,” you can move forward with your efforts to answer “What is likely to happen?

3. Predictive Analytics

Predictive analytics answers “What is likely to happen.” Of course, it doesn’t predict any future events per se but helps predict the probabilities of an event to take place. In essence, predictive analytics can be compared to fortune-telling with the speculating.

The dashboard of a personal expense solution powered by predictive analytics that forecasts the income and expenses of a month. Image Credit: boldbi.com

For Holmes, predictive analytics will be similar to predicting when and where to catch Professor Moriarty (his most infamous antagonist) by examining the nature of his past appearances.

The key motive behind predictive analytics is to make data-driven decisions – thereby reducing the risks associated with the “What if we are wrong?” scenario. It gives businesses actionable insights to optimize their marketing efforts, and to create products that customers will like using – along with many others.

The more voluminous and diverse the data sets are, the better. Thus, with predictive analytics, the more knowledge you have about the past, the more prepared you can be for the future.

The information gathered from predictive analytics can help companies formulate a set of actions that can positively impact operational effectiveness, sales and profitability, customer satisfaction, branding and many more.

Businesses can identify customers on the verge of churning and offer specialised discounts or incentives to retain them. Companies can also predict customer lifetime value (CLV) and channel their resources to retain customers with the highest ROI.

Here are some applications of predictive analytics:

  • Identifying natural disasters and storm movement patterns using weather forecasting.
  • Fraud detection by identifying customer activity out of the ordinary.
  • Price optimization to offer competent prices.
  • Banks can predict the likeliness of an individual to default on loan repayment.
  • Companies can forecast changing trends and tweak their supply chain process.

Once you have the answers to “What happened,” “Why did it happen,” and “What will happen,” you can level up and find the set of actions required to answer “How to make it happen.”

4. Prescriptive Analytics

While other types of data analytics “describe” what, why and how things will happen, prescriptive analytics “prescribes” the actions to take to “make something happen”.

The scope of predictive and prescriptive analytics. Image Credit: commons.wikimedia.org

Sherlock Holmes’s case, prescriptive analytics can be compared to the actions he suggests to Inspector Lestrade or Scotland Yard to catch a criminal or to prevent murders. For example, prescriptive analytics in action for Holmes would be suggesting the number of police officers required to catch Moriarty at a particular venue and time.

From a business point of view, prescriptive analytics allows analysts to suggest a course of action that would enable businesses to fully seize an opportunity or eliminate the chances of a financial crunch (or, even worse, bankruptcy). It also allows businesses to estimate the influence of a particular factor on a potential future.

Prescriptive analytics heavily relies on artificial intelligence, machine learning, computational modelling, optimization techniques (such as linear, integer and non-linear programming), and rules-based techniques (such as decision trees, inference engines, and scorecards), among many others.

Google’s self-driving car is an excellent example of leveraging prescriptive analytics. By considering numerous factors, such as real-time traffic conditions, upcoming obstacles or potential threats, and route maps, the vehicle decides when to slow down or speed up and when and where to make a turn.

Compared to other types of data analytics, prescriptive analytics are least utilised by organisations, primarily because they require advanced technologies and tools to fully embrace – hence, making it expensive and complicated.

However, if you succeed in embracing the potential of prescriptive analytics, it will save you more than you spent on it. If predictive analytics helps a business determine the likelihood of a customer churning, it can also deliver a set of actionable insights to retain them.

More precisely, prescriptive analytics looks into what has happened, why it has happened and a handful of what might happen scenarios to suggest the best actions to take.

Google Maps uses prescriptive analytics to suggest multiple routes by taking into account the distance, mode of transportation, and real-time traffic.

Here are some applications of prescriptive analytics.

  • Google Maps making multiple route suggestions by considering real-time traffic conditions, distance, and mode of transportation.
  • In learning management systems, new courses are suggested that best suit the learner, considering previously interacted content, interests, the pace of learning, and more.
  • Helps companies to enhance customer experience and satisfaction by delivering the right products at the right time.
  • The healthcare industry can rely on data-driven decisions and recommendations rather than intuition or past experiences. 

    Why is Data Analytics training important for organisations?

    Data analytics training is crucial for organizations as it equips employees with the skills and knowledge to interpret and utilize data effectively, driving informed decision-making and strategic planning. 

    In today’s data-driven world, the ability to analyze and derive meaningful insights from data is not just a technical skill but a strategic asset. 

    Training in data analytics enhances problem-solving capabilities, enables predictive insights, and fosters a culture of evidence-based decision-making. This can lead to more efficient operations, improved customer experiences, and increased competitiveness.

    Furthermore, data analytics training democratizes data access within the organization, empowering employees across various departments to engage with data directly and make autonomous decisions. This reduces dependency on specialized data teams and improves the agility of the organization in responding to market changes and opportunities. 

    By investing in analytics training, organizations not only improve their operational efficiencies but also cultivate a workforce that is adept at navigating the complexities of the modern business landscape, ensuring sustainability and growth in the long term.

In Conclusion

A data analyst must be keen to observe data without any bias or prejudice – just like Sherlock Holmes. When diving into the pool of data, the analyst must have only a pair of goggles (analytics tools) to see what’s around them and nothing else.

Although many may disagree, data is the new oil and is still in abundance. Businesses that fail to identify the potential of data (more precisely, big data) are more than likely to sink into this data-driven world.

Out of the four types of data analytics discussed, companies may directly jump to predictive or prescriptive analytics (without implementing the four sequentially) as they may have already established a traditional method similar to descriptive. If you’re planning to becoming a data analyst, you’ll have to employ one or all of these types.

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