Businesses are dependent on data and nobody can deny that. Data helps businesses to understand their customers better and improve the loopholes. But without properly analyzing the captured datasets, how can a business attain insights from them? This is where data analytics service and data analytics types come into the picture.

You can unlock the power of raw data with the help of different data analytics approaches. Data analytics is the procedure through which raw datasets are assessed with specialized data analytics tools and data analytics software, to make contextual business decisions. In other words, the data analytics tools and data analytics software help businesses to dig out the valuable insights that are contained within raw datasets. The information obtained is then used by analysts, etc. to make informed decisions.

This article will discuss the types of data analytics and throw light on what types of data analytics should be used by businesses due to their sizes.

So, let’s dive right in!

4 Data Analytics Types for Improved Decision Making

Suppose you wish to find out the business trends for the previous financial year and get a data-driven explanation for them. Which type of data analytics will you use in that case? The answer is either descriptive analytics or diagnostic analytics.
On the other hand, if you plan to build a strategy or develop a futuristic plan, predictive or prescriptive data analytics software is your go-to approach.
So, let’s learn more about the data analytics types in detail.

1. Descriptive analytics

The simplest form of data analytics is descriptive analytics. With descriptive analytics, answering the question ‘what’ becomes easier. Techniques like data mining, clustering, data aggregation, metrics reports, summary statistics, etc. fall under the umbrella of descriptive analytics.
Businesses frequently use descriptive analytics for measuring sales trends, marketing trends, and/or financial trends.
Think about a retail store in Seattle that wants to develop visibility about their Black Friday sales. Suppose, the owner wants to know the products that had the highest sales and which items had the least. Using descriptive analytics to the recorded data, the owner can find out the information. Plus, an overall view of the Black Friday sales can also be obtained.

Pros

• Simple to use and interpret
• Provides a view of the overall state of business
• Helps in making improvements in operations by analyzing historical data
• Answers the ‘what’ question in a straightforward way

Cons

• Descriptive analytics can study only 2-3 variables at a time
• It does not apply to complex analysis or forecasts

Use Cases

• Identifying prospects and understanding customer behaviour on different social platforms
• Automatic grouping organization of incoming leads based on demographics data and other additional data
• Summarization of past events and operations like campaign performance, employee attrition, regional sales, customer turnover, etc.
• Measuring social metrics like page followers, tweets or Facebook likes.
• Preparation of report, dashboards on the general market, financial trends

2. Diagnostic analytics

This data analytics type answers the question ‘why’. Diagnostic analytics is a type of data analytics that seeks to identify the cause behind an event. It focuses on developing the cause and effect relationship.

After you have applied descriptive analytics and obtained insights from datasets, you can use diagnostic analytics to gain a broader perspective of the event. With proper techniques of diagnostic analytics, you can identify the underlying patterns and how the different factors are related to each other. Simply said, diagnostic analytics is for in-depth analysis. It can include correlation, regression, sensitivity analysis, probability, etc.

Suppose, a business wants to know why their sales in the previous year had been more than the present year or why the previous ad campaign had been more successful than the recent one. Here, diagnostic analytics is applicable to identify the reasons.

Diagnostic analytics is a flexible data analytics type that can be used for various reasons. When used with proper data analytics tools, it can cater to varied business needs.

Pros

• Gives a detailed picture of why something has happened
• Helps in making informed decisions
• Converts complex datasets into easily understandable insights
• Enables overall optimization of business

Cons

• Inability to provide actionable insights for the future
• Though diagnostic analytics helps in understanding the causal relationship, it may not answer all necessary questions

Use Cases

• Identify reasons behind the sudden decline in website traffic
• Refine marketing and sales strategies by looking at the causal relationship between different factors
• Compare the responses of different customer segments to a single ad campaign and identify the underlying factors linked to the response
• Identifying reasons behind the decline in sales or profits

3. Predictive analytics

Predictive analytics is the type of data analytics that is used to mine historical data and generate forecasts. And, is all about using statistical modelling to produce predictions for the future. This data analytics type is more complex than the above two types of data analytics.

The data analytics methods or techniques that can be used in predictive analytics include time series data mining, predictive modelling, Bayesian analysis, etc.

For example, Vodafone has been successfully using predictive analytics to identify clients who had the maximum probability of going skiing. This prediction of how many customers might go skiing helped Vodafone to offer targeted roaming plans and special services to this customer segment.

Pros

• Predictive analytics can be applied to a range of business strategies
• Its techniques can provide decision-making tools for managers
• Predictive analysis tools may be used for influencing upselling, forecasting sales and revenue, etc.

Cons

• This type of data analytics may not be useful for all businesses
• Estimations regarding customer behaviour may not be accurate

Use Cases

• Predictive analytics can be useful for sales forecast
• It can be used for risk assessment, quality assurance
• Customer segmentation can be improved with predictive analytics
• It can also be used for churn prevention
• Streamlining operations

4. Prescriptive analytics

Prescriptive analytics is the type of data analytics that offers future recommendations.

If you want to know what’s next, prescriptive analytics is your go-to data analytics type. It is based on predictive analytics but comes with suggestions as to what and how can be done for the future. Based on these recommendations, decision-makers can chalk out their action plan.

One brilliant example of prescriptive analytics is Netflix. Have you checked the Netflix recommendations in your inbox? They use prescriptive analytics to take their recommendations to the next level.

Netflix captures different data types like – most-watched shows, previously viewed histories etc. and runs prescriptive analytics to identify the most befitting recommendations.

Pros

• Provides evidence-based insights
• Helps in meeting business goals and developing future strategies
• Helps in the prevention of fraud and limits the risk

Cons

• Only applies to situations where the amount of data is unimaginably huge
• Mostly useful for large scale businesses

Use Cases

• Prescriptive analytics can be applied to designing marketing mixes
• It can be used to forecast demands and optimizing campaigns
• Prescriptive analytics can be used for risk management

What type of Data Analytics should Small Businesses choose?

• Descriptive analytics is the best type of data analytics that small businesses should go for
• It is simple and easy to use.
• With a limited amount of data available, small businesses can use descriptive analytics for various tasks like keeping track of financials, analyze business performance, monitor website traffic, etc.

Which Data Analytics types should Medium-sized Businesses choose?

• Descriptive analytics and Diagnostic analytics are best suited for medium businesses
• Both of these data analytics types can help these businesses to analyze past performances as well as make improvements based on the reasons identified
• Medium businesses can manage their customer experience better by identifying and solving their unique business problems

Which Data Analytics types should Enterprise-level Businesses choose?

• Predictive and Prescriptive analytics is mostly suited to enterprise-level businesses
• Since these large-scale businesses have access to a huge amount of data, these two data analytics types are the most useful
• Enterprises like Vodafone and Netflix (as in examples) use prescriptive and predictive analytics for better customer experience and strategies

Top 5 Data Analytics Tools to Accelerate Your Business Growth

Here is a sneak peek of the data analytics tools and/or data analytics software. Since there are plenty of data analytics tools in the market, you might get confused about which data analytics tools or data analytics software to go for.

We hope this list of top 5 data analytics tools and data analytics software can help you out.

1. Google Data Studio

Google Data Studio is one of the best data analytics tools that rules across industries. It is a free data visualization and dashboarding tool that easily integrates with other Google applications like Google Analytics, Google Ads, etc. This data analytics tool is great for companies that need to analyze Google data.
For example, if a business wants to understand the customer retention rate in a better way, marketers can use Google Data Studio to build dashboards for their Google Analytics or Google Ads data.

2. Tableau

Tableau is a data analytics software or rather a business intelligence tool that can help you level up your business. This data analytics software lets you work on live datasets enabling you to spend more time analyzing the data rather than data wrangling. You can use this data analytics software to create reports and share them across different platforms. It can run both on-premises or on the cloud.

3. Microsoft Excel

Microsoft Excel doesn’t need any special introduction as it is the commonest data analytics software used across businesses in various industries. From small businesses to enterprises, Excel is one data analytics tool that you’ll find everywhere. It helps businesses with standard analytics to slightly complex ones. However, it is not much suited for analyzing big data so enterprises can go for more modern cloud-based analytics platforms.

4. Microsoft Power BI

Power BI is one of the top business intelligence platforms that supports dozens of data. It allows businesses to create and share visualizations, dashboards, and reports. This data analytics software also allows you to combine dashboard groups and reports.

5. RapidMiner

RapidMiner is an interesting data analytics tool that does the job of cleaning, transforming, and integrating data before running predictive analytics and other statistical models. The data analytics tool has a simple graphical interface that can perform nearly all of the functions.

Questions to ask yourself before choosing the apt Data Analytics Types

Choosing the right type of data analytics is the most effective way to level up your business. But, how do you choose the best-suited data analytics type for your business?

Here are 5 questions you can ask yourself before choosing the type of data analytics.

Q1. Where can I find the data?

To get started with data analytics, you need to know about the source of your data. The answer to this question is generally the physical location where your business stores data. It may be a few spreadsheets for small businesses, while for large enterprises, it may be a data warehouse or cloud source.

Q2. Is data modification required?

Often, you’re required to change the datasets for data analytics to be more effective. So, tweaking data is quite justified in businesses to get an accurate analysis. The answer to this question can be yes or no.

You may need to change the datasets by using different tabulation, different formats if the datasets you’re using are inconsistent and has duplicate information. If not, you can continue applying data analytics to the datasets without making any changes.

Q3. How to verify the results?

Before you jump into applying a particular data analytics type, you must be sure about the accuracy of their results. You can observe whether the results are in line with the overall business view to ensure their accuracy.

Final Words On Data Analytics Types

With all the knowledge on data analytics, we hope it is clear as to what importance does data analytics holds for a business. Each of the types of data analytics is linked to one another. Descriptive analytics answers the ‘what’, diagnostic analytics answers the ‘how’, predictive analytics answers ‘what’s next’ and prescriptive analytics answers ‘how to make it happen’.

However, after going through all four types of data analytics, we think that descriptive analytics is most suited to small businesses, diagnostic analytics to medium businesses and predictive and prescriptive are most suited to enterprises.

We hope it’s easier for you to choose the best type of data analytics. Need any help? Let us know through comments.