In today’s hyper-competitive business landscape, data has emerged as the most valuable asset. However, raw data alone holds limited value. To transform it into actionable insights, organizations must employ a multifaceted approach to data analytics. By understanding the various types of data analytics and their applications, businesses can make informed decisions, optimize operations, and gain a competitive edge. According to a research by McKinsey & Company businesses leveraging data analytics are 23 times more likely to outperform their competitors in customer acquisition. Let’s explore how different analytics methodologies can illuminate your business path.
The Four Major Types of Data Analytics
Data analytics can be broadly categorized into four types. We start with the simplest one descriptive analytics and gradually move towards prescriptive analytics; the pinnacle, explaining how they are chronologically related.
Descriptive Analytics: Answers What Happened
Descriptive analytics describes a past state based on historical data. It is a high-level approach to identifying current trends and relationships from historical data. It doesn’t dig much deeper than giving out some facts and is the simplest form of data analysis. The reports of descriptive analytics provide the background for business intelligence (BI) tools and dashboards.
A common example of descriptive analytics can be a report from your sales team. This report contains information about prospective leads, new clients, conversion ratio, and goals. You can look at this report to find out for example, “How many customers are interested in your product? How many of them are from New York?”
Your organization uses descriptive analytics almost daily. Microsoft Excel, Google Charts, and Tableau help you to parse data and identify trends and relationships between variables and help you to visualize them. Techniques involve data aggregation, data mining, clustering and/or summary statistics.
Diagnostic Analytics: Inspects Why Did This Happen
Like descriptive analytics, diagnostic analytics also takes a look back at the past. However, it scratches on the surface to find answers to why something happened. It uses historical data to find out what were the causes behind what happened and establish correlations between variables.
Imagine you have witnessed a sudden drop in traffic after introducing a new feature in your app. Diagnostic analytics helps to get an explanation for this. This is often associated with collecting customer feedback like “How are the customers rating the app after integrating the new feature? How frequently do the users use the new feature?” With diagnostic analytics, you can identify the outliers and isolate patterns. Isolating patterns requires looking beyond the existing dataset and identifying the source of the pattern.
Diagnostic analytics is guided by hypothesis testing. Hypothesis testing is statistically proving or disproving an assumption. Coming back to the example of the app, a historically oriented hypothesis can be made for example, “The integration of a certain JavaScript library for this feature is slowing down the entire user experience.” This gives your analysis a direction to investigate and prove or disprove that.
Diagnostic analytics uncovers relationships through correlation or causation. While correlation means two variables may be interrelated but they are not necessarily the cause of another’s change. In the above discussed example, losing your traffic may also be correlated to reduced number of user reviews but one is not the cause of another, you may be simply not asking enough for reviews through various channels. Causation, however, establishes the cause of change in one variable due to another, the more app uninstallation happens, the lower the app store ranking.
Determining why something happened involves techniques like principal components analysis, sensitivity analysis, and training algorithms for classification and regression analysis. The “why” of diagnostic analytics sets path for the “what” of predictive analytics. Your product team can start to find answers to what changes will make your app more user friendly and popular.
Predictive Analytics: Speculates What Can Happen in the Future
Predictive analytics builds on the information supplied by descriptive and diagnostic analytics to present actionable insights. It forecasts potential future outcomes based on historical data. Predictions range from near future timeline- for example, how a certain machine will malfunction to distant future predictions like- how the cashflow is going to look for the upcoming years.
An important example of predictive analytics shines in the manufacturing sector in the form of predictive maintenance. Based on historical data and comparing realtime data connected through IoT-enabled devices it tries to predict the time when a machine warrants servicing to prevent unplanned breakdown. For example, for a boiler feed pump predictive analytics can read monitoring data supplied continuously and decide if there is a likelihood of failure and also the lifetime of other ancillary equipment. Boiler feed pumps being a very important component in a coal-based energy production system demand the ability to generate electricity continuously without failure. Predictive analytics prevents them from sudden failure and maintains an uninterrupted supply of power.
Although predictive analysis can be done manually it is preferred to take the help of machine learning algorithms when dealing with large volumes of data in a narrow timeline. Machine learning models utilize concepts like confidence intervals, T statistics, K-S statistics, and P values for predictive analytics.
Prescriptive Analytics: Suggests What Should be Done Next
Prescriptive analytics takes things ahead of predictive analytics by prescribing an optimal course of action. Prescriptive analytics algorithms provide data-informed recommendations. However, human discernment is always supreme.
It can serve as an important tool in product development. Product managers can gather user data by surveying customers, running tests with a product’s beta versions, conducting market research with people who aren’t current product users, and collecting behavioral data as current users interact. All this data can be analyzed—either manually or algorithmically—to identify trends, discover the reasons for those trends, and predict whether the trends are predicted to recur.
Unleashing the Power of Data Analytics for Your Business
Data is the lifeblood of modern organizations, but raw data alone holds limited value. To transform information into actionable insights, you need a robust data analytics strategy. But where do you start?
Understanding Your Data Analytics Needs
The first step is to assess your current data landscape. Consider these questions:
- What is the maturity level of your data analytics function?
- How deep do you need to delve into your data to uncover meaningful insights?
- What is the gap between your current insights and your desired business outcomes?
These answers will guide you in selecting the appropriate data analytics tools and techniques. Remember, a successful strategy often involves a gradual progression from basic to advanced analytics.
Building a Strong Foundation for Data Analytics
To maximize the return on your data analytics investment, consider partnering with an experienced data analytics provider. They can help you:
- Assess your data landscape and identify opportunities for improvement.
- Select the right mix of data analytics tools and technologies to meet your specific needs.
- Implement and manage your data analytics solution efficiently and effectively.
By leveraging their expertise, you can accelerate your time to value and focus on core business activities.
At the pinnacle of data analytics lies prescriptive analytics. By combining artificial intelligence, machine learning, and neural networks, organizations can move beyond predicting future outcomes to recommending optimal actions. This level of insight empowers businesses to make data-driven decisions with confidence.
