The last few decades have made big data popular and more widely used in various industries. This includes spheres such as e-commerce, information technology, and marketing- to name a few. According to New Vantage, 97.2% of organizations are actively investing in big data and AI.
This trend is seen in big data investment enterprises such as Goldman Sachs, IBM, and Bank of America. The survey states that the primary focus was on big data solutions and artificial intelligence. The study not only found a strong presence of big data in leading companies but also calculated how much these companies spend on data analytics services.
In simple terms, big data refers to comprehensive data sets that are collected and stored. Since they are largely unstructured in nature, companies hire trained data scientists and use modern data mining software or analytical software to retrieve the hidden insights from Big Data. Let’s learn more about why top firms must leverage big data and analytics:
Why Big Data and Analytics?
Big data analytics can fuel most things we do online in any industry. There are advantages that come with adopting big data analytics in one’s enterprise:
- Cost Savings: Organizations can prioritize the right investments by saving costs on expensive equipment and redundant processes. Cloud-based technologies can significantly reduce costs when compares to on-premise infrastructure. A data lake can help with a variety of things that include streaming analytics, machine learning, and data engineering.
- Faster and more informed Business Decisions: Big data allows in-memory analytics and speed for analyzing new sources of data. Among many things, streaming from the IoT helps businesses analyze and read the information in real-time- allowing accurate and efficient business tactics to form.
- Creating and marketing new products: One of the biggest benefits of adopting big data and analytics are the tools that come with the new technology. It enables the enterprise to gauge customer needs constantly so as to provide a superior experience. This ultimately helps in empowering the business using analytics and ultimately enhances customer satisfaction.
- Increased Volume and Veracity of Data: Most often, big data volumes are significantly bigger than the volume of processed data in a normal system in an enterprise. This paves the way for newly-designed systems and architectures that help contain high volumes of data. The usual reason for high volume is that the data is from a number of IT systems that are merged- this causes the amount of data to be multiplied in number. There are concepts, methods, and tools that help to ensure that the data is not incomplete or labeled incorrectly. Owing to the process of data ingestion being different across systems, data accuracy must be given the utmost significance.
Let’s now discuss the different kinds of big data analytics and where some of its benefits reaped through application:
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Types of Big Data Analytics
There are four main types of big data analytics to discuss. They are as follows:
- Diagnostic Analytics: This is one of the more advanced types of big data analytics that is used to investigate both data and content. It helps to gain insight to comprehend the reasons for certain behaviors and events that are related to the company. Some common tasks in this technique include searching for patterns in data sets, filtering the data, regression analysis, and using the probability theory.
- Descriptive Analytics: In descriptive analytics, work is taken ahead based on the type of incoming data. In order to mine and derive insights from these sources, analytics need to be deployed to create a description based on the kind of incoming data.
- Predictive Analytics: Predictive analytics uses historical and present data in conjunction to make predictions for the future. It uses tasks such as data mining, artificial intelligence, and machine learning in order to analyze the data and make predictions of the future. Market trends and customer trends are an integral part of gauging future activities.
- Prescriptive Analytics: This type of analytics looks into the intricacies of a single problem and streamlines the solution. Prescriptive analytics is a combination of descriptive and predictive analytics as it relies on AI and machine learning. In the use case example of an airline, prescriptive analysis can be used to find out the flight fares based on different factors such as customer demand, destination, weather, holidays, etc.
Challenges to Big Data Analytics
As much as big data analytics can boost businesses, it also comes with its own set of challenges. Let’s discuss some of these challenges:
Access to Data and Security
As big data inadvertently leads to bigger volumes of data, storage, and processing of this data become a challenge. Big data should be maintained in the right security environments so threats can be mitigated. Companies that operate on the cloud can adopt big data analytics tools, security tools to aid with encryption, and establishing access to business intelligence tools to protect data.
Data Quality Maintenance and Tool Selection
Along with higher volumes of data from dissimilar sources and in different formats, there comes a need to properly manage the data quality by using a proper data management system. Data quality checks are required to be taken forward so as to ensure that processes require considerably lesser time, effort, and resources. Choosing the right big data analytics tools requires the tool to align with the user requirements and the overall organizational infrastructure.
Big Data for Real-Time Results
Big data analytics is driving some of the biggest and most ambitious businesses across the globe forward today. Enterprises can convert data into information and real-time information into actionable insights all in a matter of seconds with the help of big data analytics.
There are a number of advantages that range from risk management, product development and innovations, improved customer experience, and focused targeted campaigns that big data analytics can help with. Predictive analytics and advanced analytics play a massive role in many spheres of work such as in healthcare, education, and public service. Different types of big data analytics aid businesses in unique ways to process and make use of raw data in the most efficient manner across the enterprise.