Types of Data Analytics to Improve Decision-Making
Following the insights from the Global State of Enterprise Analytics report, about 56% of respondents indicate data analytics and data management are tools for delivering faster and more effective decision-making. In such a case, exploring different types of data analytics is a direct way to help companies make correct business-related decisions.
Keeping that in mind, our journey entails looking closer at the concept of data analytics to understand why businesses choose it. Besides, we would explore 4 types of data analytics, focusing on key aspects of each one of them. Finally, we will find out what types of data analysis companies choose and how a firm can determine which type it needs.
What Is Data Analytics?
In short, data analytics is the approach toward data examination focusing on identifying market trends, answering business-related questions, and extracting valuable insights. In other words, data analysis translates raw data into insights that companies can adopt to improve their decision-making and grow their business.
When it comes to the approaches toward data analysis, businesses often use instruments presented in Big Data and data science. Adopting different types of data analytics is vital to running the business successfully. Such a massive focus on data is made because data is currently considered to be the most valuable commodity. It helps boost business performance, grants a better understanding of market trends, and helps companies gain a competitive advantage. In such a case, when data analysis is layered on business, it is often known as business analytics. Perhaps the best illustration of the greater demand for data analytics stems from the current and future market trends. Essentially, there is sufficient evidence to suggest the data analysis market is flourishing and soon becoming something not a single company can avoid (see Fig.1).
Keeping all the insights above in mind, the value of different types of data analysis is best seen through the scope of benefits it brings. Notably, the method brings the following advantages:
- Marketers can use customer data coupled with performance metrics and aligned with historical data to plan future marketing strategies accurately. At this point, data analytics is extremely valuable in marketing.
- Product managers can use data analysis to examine industry trends, market requirements, and user preferences. This is done to improve the company’s product and help businesses meet customer needs to the best of their ability.
- Human Resource professionals can use data analytics to create inclusive and diverse work environments. The more they know about employees’ preferences and backgrounds, the easier it is to implement changes to achieve the best company culture.
These are only a few examples showing the benefits of different types of data analysis. Yet, even these are enough to explore data analytics as something worth using. Now, we know about the phenomenon and the advantages it grants. So, let’s proceed to the four types of data analytics, and explore them in greater detail.
Four Types of Data Analytics
In a nutshell, there are 4 types of data analytics available. What are the 4 types of data analytics. Essentially, there are descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Each one comes with a different degree of complexity and is used in different scenarios.
Let’s start with the simplest data analysis type and proceed to more complex ones as we go further.
Descriptive Analytics
Descriptive analytics is all about shuffling data obtained from various sources. It grants insights into the past to understand how to act in the present. In a nutshell, this type of data analytics answers the following questions — what happened in the past?
To illustrate, imagine you are planning to invest in developing new software. You handled a similar project in the past and faced several hiccups. At this point, you employ descriptive analytics to understand why those past hiccups occurred, and you consider the insights to avoid making the same mistake in the ongoing project.
In such a case, among different types of data analytics, a descriptive one has several key business-related use cases: KPI dashboards, sales leads overviews and revenue reports. It is best to use data visualization to tap into what descriptive analytics can offer.
Diagnostic Analytics
When trying to find out what reasons led to some phenomenon, it is time to use a diagnostic analysis. This type of data analytics works with historical data cross-references to other key data types. For example, companies use diagnostic analytics to understand why they missed the net profit goal. Businesses use historical data to measure similar scenarios and layer the insights on the present data.
In a nutshell, diagnostic analytics is an excellent method for in-depth insights into past or ongoing issues. For this type of data analysis to be effective, you need to have effective data management and data collection tools at your disposal. Otherwise, diagnostic analytics will be a time-consuming and laborious process.
When it comes to business-related use cases of this type of data analysis, consider the following:
- A software development company investigates the reasons for its new software underperforming in a specific market
- A SaaS firm examines different factors to understand how certain marketing actions increase leads.
These simple examples show why companies tend to use diagnostic analytics regularly.
Predictive Analytics
At this point, we turn to advanced analytics. In short, predictive analytics is founded on the insights offered by descriptive and diagnostic analytics. Among different types of data analysis, predictive-based methods use Artificial Intelligence (AI) and Machine Learning (ML) to offer businesses accurate predictions on what turns a market will take and how a particular product will perform.
In short, predictive analytics answers this question — what is going to happen? Essentially, it would be an understatement to suggest that predictive analytics is a vital forecasting tool. For instance, a software development company looks for a market to introduce a new product. In such a case, fueling different data types into the predictive analytics mechanism is a great way to see how likely the new product will succeed. Respectively, this forecast will affect the final executive decision on whether one should invest in the new project or not.
In most cases, predictive analytics is used for risk evaluation, sales forecasting, and customer segmentation. At this point, among available types of data analysis, predictive analytics exploits sophisticated algorithms to look into the near future.
Prescriptive Analytics
If predictive analytics forecasts what is likely to happen, prescriptive analytics focuses on what actions a company should take to minimize or even eliminate future issues. This type of data analysis uses the full force of Big Data and data science. To illustrate, global companies often use prescriptive analytics to identify financial and investment opportunities. Respectively, these businesses anticipate how smaller companies will grow and invest in them in advance to profit from future stock rises.
Prescriptive analytics is based on sophisticated algorithms brought by AI, ML, and deep learning. In a nutshell, these instruments consume vast amounts of data and have the analytical capacity to turn raw data into easily digestible and adaptable decisions. What is more, prescriptive analytics can be fully automated. Finally, let’s not forget tools like AI are still in the stage of their germination. One can expect the instrument to evolve and occupy a broader market and the accuracy and effectiveness of prescriptive analytics to rise (see Fig.2).
With 4 types of data analytics in the open, you can see how each is used and what answers they provide. In such a case, one can say that companies can use different types or focus on a specific one meeting business needs. Keeping that in mind, let’s find out what particular types of data analysis companies commonly prefer.
What Types of Data Analytics Usually Do Companies Choose?
There is the following prioritization of different data analytics types:
- The first place occupies descriptive analytics, with 58% of companies preferring it over other types of data analysis.
- The second place is behind predictive analytics, with 38% of respondents offering this method as their preference.
- The third place is occupied by diagnostic analytics, with 34% of companies preferring it over other types of data analysis.
Respectively, such a disparity does not offer a clear picture. Namely, often, companies can use different types of data analytics based on the particular stage of product development. For instance, during the initial stages, descriptive analytics might not be enough, especially when a business determines whether to make an investment. It is time to tap into predictive and prescriptive analytics.
As a result, while descriptive analytics formally occupies the first place in terms of usage, it can be explained by the fact this type of data analysis is the simplest. However, it does not mean it grants better insights than diagnostic or predictive analytics. Often, you need to use various types of data analytics as a symphony, each complementing the other.
How to Determine What Types of Data Analysis Your Company Needs?
When determining which type of data analytics your company needs, there are several key prerequisites to consider:
- First, assess your current data analysis tools and understand the value of insights they bring.
- Second, determine what degree of insights you want to receive from the data. In other words, you need to see how deeply to plunge into raw data and what insights you want to receive from that.
- Third, compare the insights you receive from your current data analytic instruments to the insights you need.
At this point, if the first insights match the ones you need, it means the data analytics tool you currently use is sufficient. If those insights do not match, you will see what types of data analysis you need. If the difference is slight, descriptive or diagnostic analytics will be enough. If the difference is massive, there is a high chance you need to think about predictive and prescriptive analytics.
However, if you are unsure, you can handle the above-mentioned process, it is time to look for an experienced data analytics provider. It should be a company with expertise in Big Data and data science. The vendor will closely examine your company and the data analysis tool you need and provide an evidence-based explanation of what type of data analytics you need and why.
Conclusion
Data is much more valuable now than it was a decade ago. Data helps to look into the future and anticipate different market trends. Respectively, companies who know how to exploit data correctly will be the ones with the most competitive advantage. Knowing different types of data analytics and choosing the right one means the difference between a good and a bad investment. As a result, data analytics helps us learn from past mistakes and exploit sophisticated algorithms to avoid similar mistakes in the future. In the current turbulent market conditions, this can be a game changer.
Reach out today to discuss how Intetics experts can help you tackle your challenges in handling data analytics.
