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Types of Data Analytics: What Every Business Should Know

May 27, 2021
Average: 5 (2 votes)

The business analytics and intelligence software app market is predicted to hit 14.5 billion USD in 2022.

Industries that are currently benefitting from data analytics the most are banking (specifically retail and investment), healthcare, agriculture, real estate, and telco.

But what are the types of data analytics, and why do businesses need to know them?

What is Data Analytics?

Data analytics is the process of analyzing sets of data to find trends, patterns, and correlations, and drive insights that help make better business decisions.

It is related to but not the same thing as business analytical tools, which are application software that retrieve data from systems and collect it in a pool, such as a data warehouse. These can be as simple as a spreadsheet with a statistical function or as complex as a sophisticated data mining tool.

Ideally, companies would use all of their data to derive value. Such data includes web servers, log files, customer data, and transactional data.

Businesses can use tools to perform analytics. These are usually referred to as BI tools, or business intelligence tools.

Data Analytics Steps

  • Understanding business problems: Defining goals, understanding problems, and planning solutions are all part of the first step of data analytics.

  • Collecting data: Up next is collecting customer information and transactional data. This will help you address the current challenges your business is facing.

  • Cleaning data: The data you have gathered thus far will be disordered. This step involves removing redundant, unwanted or missing values from your data to prepare it for analysis.

  • Exploratory data analysis: This step uses BI and data visualization tools, data mining, and predictive modeling.

Benefits of Data Analytics

  • Improved customer service

  • More efficient marketing

  • Streamlined operations

  • Better decision making

Effects of COVID-19 on Data Analytics

It's not just supply chains that have been disrupted by the pandemic. It's consumer behavior itself.

The use of data has not dropped. (In a 2020 survey of 150 executives by West Monroe, 57% of respondents reported adopting data and analytics platforms.) Rather, it has merely changed.

How does one use past data to predict future behavior and make the right choices during a situation that is unprecedented in living memory? For most companies and organizations, it is not business as usual.

For example, past data is no longer a reliable guide for predictive analytics in countries still reeling from COVID-19. Many organizations are trying to decide which data has remained relevant.

Subsequently, there has been a shift towards descriptive analytics and external data and away from machine learning, according to MIT. Predictive analytics is difficult in normal times – during a pandemic, it is even more so.

Organizations are also creating or searching for disaster models, which will help in the case of hurricanes, future pandemics, and so on.

The 4 Types of Data Analytics

The types of data analytics are interrelated and are typically implemented in stages.

No type is "better" than the other – they are merely used for different purposes. Most organizations use more than one type of data analytics to drive insights.

  • Descriptive Analytics

    Descriptive analytics is usually the first step in data analytics, and explains what happened in a given time period. Data mining and data aggregation are used for this.

    This analytics type divides data into smaller units and is a common tool used by organizations. It concerns what has already occurred and draws from various sources to drive insights. For example, tracking assignment grades, collating survey results, or identifying the amount of time taken to complete an assignment.

    However, such insights do not clarify why those things happen.

    Hence, if an organization has the resources, experts suggest combining descriptive analytics with other types of data analytics. Whether this is done in-house or by professional data analytics services is up to the organization.

  • Diagnostic Analytics

    Diagnostic analytics has the goal of answering why something happened during a given time period.

    For example: A healthcare professional using diagnostic analytics to help determine that various symptoms point to the same infectious agent. This would help to explain a sudden spike of patients

    Techniques used for diagnostic analytics include data mining, correlations, data discovery, and drill-down.

  • Predictive Analytics

    You can't look into a crystal ball, since those don't exist, but the closest thing we've got is predictive analytics.

    Predictive analytics breaks down past information to help businesses make educated guesses about what could happen in the future. This helps to plan effectively, set practical goals, and restrain expectations.

    Organizations such as Amazon and Walmart utilize predictive analytics to identify trends and forecast customer behavior and inventory levels to offer customized product recommendations and predict sales.

    Techniques for predictive analytics include machine learning, data mining, data modeling, artificial intelligence, and deep-learning algorithms.

  • Prescriptive Analytics

    Prescriptive analytics helps managers take the best course of action given a variety of choices, and emphasises actionable insights as opposed to data monitoring. It can even measure the effects of a business choice based on various future scenarios.

    It collects data from both predictive and descriptive sources and borrows heavily from computer science and mathematics. Prescriptive analytics is considered the "final" step in business analytics. It is built into most contemporary business intelligence (BI) tools.

    As an example, Youtube uses prescriptive analytics to offer a customized viewing experience.

    Techniques for prescriptive analytics include algorithms, computational modeling, business rules and machine learning.

Conclusion

While the types of data analytics are often implemented sequentially, it is not necessary to do so. For example, organizations can, if required, leap immediately to prescriptive analytics.

Analyzing the data from your websites, machine logs and social networks enhances a business' ability to make better business decisions, whether that means greater profits or achieving goals.

If you're looking for accurate, comprehensive insights, you can take advantage of BluEnt. Our team of analysts will ensure that your decisions are based on the latest, most reliable information. Whether it's predictive analytics or big data solutions, we've got you covered.

We serve Fortune companies, SMEs and funded startups. Take a look at how we've helped them achieve their goals here.

To bring the benefits of data analytics to your business, contact us now!

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