Currently, the big data market is experiencing significant growth. Big companies continue to thrive with the help of big data analytics. Implementing an appropriate analytics system assists the company in solving various problems it faces.
According to a survey, the data analytics market is predicted to grow at a compound annual growth rate (CAGR) of about 29% to $40.6 billion by 2023. This phenomenal growth has driven many big data companies to come up with excellent solutions. Almost all businesses and organizations, large or small, are already taking advantage of big data analytics.
What is Big Data Analytics?
Big data analytics examines large sets of data. It helps to discover hidden patterns, trends, and correlations and provides ideas for making the right business decisions. This is done using an advanced, efficient, and rapid software system. The ability of data analytics to operate faster gives businesses a competitive edge.
All it takes to get the most out of it is choosing the right analytics. Proper use of big data analytics can bring customer satisfaction and profit to the company. It helps companies improve their decision-making and problem-solving skills.
5 Types of Big Data Analytics
Let’s look at five different types of big data analytics and how they affect your business.
Descriptive analytics is the simplest and most widely used in business today. About 90% of companies worldwide, use descriptive analytics.
It provides the answer to ‘what happened?’ by summarizing past data. It summarizes raw data and converts it into a highly digestible form (usually as a dashboard). Descriptive analytics allows you to infer events that have occurred in the past in detail and derive patterns from that data. Descriptive analytics is most commonly used in businesses to track KPIs (key performance indicators).
It isn’t easy to create standard business intelligence tools and dashboards without performing descriptive analytics. Descriptive analytics can reveal patterns that provide insights. It can be helpful in the sales cycle to categorize customers based on their potential preferences for products and sales cycles.
The diagnostic analytics examines a specific situation in depth to determine the main cause of a problem or to identify opportunities. Technologies such as data recognition, data mining, and drill down are used in diagnostic data analytics.
Data scientists use this technique to detect why something happened. It comes in handy while studying key churn indicators. Organizations use such analytics to create deep connections between data and identify the behavioural pattern.
Diagnostic analytics helps to create detailed information beforehand. Whenever new problems occur, you may already have collected certain data related to the problem. Having the data already available will save you time and effort.
Predictive analytics aims to predict future trends based on what is currently happening rather than focusing on the past. It relies greatly on statistical models that require additional technology and labour. Note that predictions are only an estimate. The certainty of the predictions depends on the quality and details of the data. It is crucial to enter correct data as even a tiny mistake can lead to significant errors in the output.
Predictive analytics is the result of both descriptive and diagnostic analytics. The insights gained from the two are converted into actionable steps. It determines what happens when certain conditions are met and helps to predict and plan for the future. This analytics is widely used in the medical industry to assess a patient’s likelihood of developing a disease. It is also used to support sales and marketing to predict the future estimates.
Prescriptive Analytics helps companies find the ideal solution from a variety of options and suggests choices for future approaches. It also shows the organization suggestions for each decision to improve its decision-making process.
Artificial intelligence (AI) is a great example of prescriptive analytics. AI systems consume a significant amount of data for continuous learning. They gather information and use them to make informed decisions. Well-developed AI systems can communicate these decisions and even implement them. With the help of AI, business processes can be achieved and improved on a daily basis without human intervention. Big data-driven businesses like Facebook, Apple, and Netflix use prescriptive analytics and AI to enhance their decision-making process.
Augmented Analytics uses the power of AI and machine learning to automate diverse data analytics processes such as data preparation and obtaining insights from data. The basis of augmented data analytics is to make data analytics available to users who have no data science training.
It uses Natural Language Processing (NLP) and provides instant results for your search queries. It is quick as it automates the machine learning and data science rendering process. Augmented analytics can quickly scan through a company’s data, cleanse and analyse it, and alter the result into actionable steps. This greatly reduces the data scientist role and accelerates the process. However, augmented analytics requires investments in advanced technologies such as machine learning and AI.
How Big Data Analytics Help Customer Success?
The advantages that big data analytics offers are speed and efficiency. With the help of big data analytics, companies can use their data and identify new opportunities. This in turn leads to smart business movements, more efficient processes, high profits, and happy customers. Let’s look at some instances.
Amazon is the top e-commerce store right now, all thanks to its database. They constantly use big data to enhance the overall customer experience.
Another example is Netflix. With more than 100 million subscribers, they collect vast amounts of data. Netflix utilizes big data analytics for targeted advertising. They send movie suggestions to subscribers by using previous search and monitoring data. This data is used to give subscribers insight into what interests them most.
Tom Davenport in his report, surveyed more than 50 companies to understand how they make use of big data. He found that they benefited from lower costs, faster and better decision-making processes, and new products and services. Davenport also emphasizes that many companies are developing new products using big data analytics to meet customer needs.
Over the past three years, about 90% of the world’s data has been generated and companies spend over $ 180 billion annually on big data analytics. Today, not only the big companies but even small and medium-sized businesses are benefitting from data analytics.
As technology continues to evolve, businesses using big data analytics must keep pace with change. And those who are still hesitant to invest should reassess their organizational policies. Understanding the different types of big data that can be collected and analyzed using analytics can help businesses recognize the potential impact that technology can achieve and improve their sense of direction for big data initiatives.