BIG DATA ANALYTICS

Big data analytics uses advanced analytics on large structured and unstructured data collections to produce valuable business insights. It is used widely across industries as varied as health care, education, insurance, artificial intelligence, retail, and manufacturing to understand what’s working and what’s not to improve processes, systems, and profitability.?

It comprises vast amounts of structured and unstructured data, which can offer important insights when analytics are applied. Big data analytics does this quickly and efficiently so that health care providers can use the information to make informed, life-saving diagnoses.?

What is big data analytics?

Big data analytics is the process of collecting, examining, and analysing large amounts of data to discover market trends, insights, and patterns that can help companies make better business decisions. This information is available quickly and efficiently so that companies can be agile in crafting plans to maintain their competitive advantage.

Technologies such as business intelligence tools and systems help organisations take unstructured and structured data from multiple sources. Users input queries into these tools to understand business operations and performance. Big data analytics uses the four data analysis methods to uncover meaningful insights and derive solutions.

How big data analytics works

Big data analytics refers to collecting, processing, cleaning, and analyzing large datasets to help organizations operationalize their big data.

1. Collect Data

Data collection looks different for every organization. With today’s technology, organizations can gather both structured and unstructured data from a variety of sources — from cloud storage to mobile applications to in-store IoT sensors and beyond. Some data will be stored in data warehouses where business intelligence tools and solutions can access it easily. Raw or unstructured data that is too diverse or complex for a warehouse may be assigned metadata and stored in a data lake.

2. Process Data

Once data is collected and stored, it must be organized properly to get accurate results on analytical queries, especially when it’s large and unstructured. Available data is growing exponentially, making data processing a challenge for organizations. One processing option is batch processing, which looks at large data blocks over time. Batch processing is useful when there is a longer turnaround time between collecting and analyzing data. Stream processing looks at small batches of data at once, shortening the delay time between collection and analysis for quicker decision-making. Stream processing is more complex and often more expensive.

3. Clean Data

Data big or small requires scrubbing to improve data quality and get stronger results; all data must be formatted correctly, and any duplicative or irrelevant data must be eliminated or accounted for. Dirty data can obscure and mislead, creating flawed insights.

4. Analyze Data

Getting big data into a usable state takes time. Once it’s ready, advanced analytics processes can turn big data into big insights. Some of these big data analysis methods include:

• Data mining sorts through large datasets to identify patterns and relationships by identifying anomalies and creating data clusters.
• Predictive analytics uses an organization’s historical data to make predictions about the future, identifying upcoming risks and opportunities.
• Deep learning imitates human learning patterns by using artificial intelligence and machine learning to layer algorithms and find patterns in the most complex and abstract data.

The importance of big data analytics

Big data analytics is important because it helps companies leverage their data to identify opportunities for improvement and optimisation. Across different business segments, increasing efficiency leads to overall more intelligent operations, higher profits, and satisfied customers. Big data analytics helps companies reduce costs and develop better, customer-centric products and services.

Data analytics helps provide insights that improve the way our society functions. In health care, big data analytics not only keeps track of and analyses individual records but it plays a critical role in measuring outcomes on a global scale.

Benefits of big data analytics

Incorporating big data analytics into a business or organisation has several advantages. These include:

• Cost reduction: Big data can reduce costs in storing all business data in one place. Tracking analytics also helps companies find ways to work more efficiently to cut costs wherever possible.
• Product development: Developing and marketing new products, services, or brands is much easier when based on data collected from customers’ needs and wants. Big data analytics also helps businesses understand product viability and to keep up with trends.
• Strategic business decisions: The ability to constantly analyse data helps businesses make better and faster decisions, such as cost and supply chain optimisation.
• Customer experience: Data-driven algorithms help marketing efforts (targeted ads, for example) and increase customer satisfaction by delivering an enhanced customer experience.
• Risk management: Businesses can identify risks by analysing data patterns and developing solutions for managing those risks.

Types of big data analytics

Four main types of big data analytics support and inform different business decisions.

1. Descriptive analytics

Descriptive analytics refers to data that can be easily read and interpreted. This data helps create reports and visualise information that can detail company profits and sales.?

Example: During the pandemic, a leading pharmaceutical company conducted data analysis on its offices and research labs. Descriptive analytics helped them identify consolidated unutilised spaces and departments, saving the company millions of pounds.

2. Diagnostics analytics

Diagnostics analytics helps companies understand why a problem occurred. Big data technologies and tools allow users to mine and recover data that helps dissect an issue and prevent it from happening in the future.

Example: An online retailer’s sales have decreased even though customers continue to add items to their shopping carts. Diagnostics analytics helped to understand that the payment page was not working correctly for a few weeks.

3. Predictive analytics

Predictive analytics looks at past and present data to make predictions. With artificial intelligence (AI), machine learning, and data mining, users can analyse the data to predict market trends.

Example: In the manufacturing sector, companies can use algorithms based on historical data to predict if or when a piece of equipment will malfunction or break down.

4. Prescriptive analytics

Prescriptive analytics solves a problem, relying on AI and machine learning to gather and use data for risk management.?

Example: Within the energy sector, utility companies, gas producers, and pipeline owners identify factors that affect the price of oil and gas to hedge risks.

Big data analytics tools

Harnessing all of that data requires tools. Thankfully, technology has advanced so that many intuitive software systems are available for data analysts to use.

• Hadoop: An open-source framework that stores and processes big data sets. Hadoop can handle and analyse structured and unstructured data.
• Spark: An open-source cluster computing framework for real-time processing and data analysis.
• Data integration software: Programs that allow big data to be streamlined across different platforms, such as MongoDB, Apache, Hadoop, and Amazon EMR.
• Stream analytics tools: Systems that filter, aggregate, and analyse data that might be stored in different platforms and formats, such as Kafka.
• Distributed storage: Databases that can split data across multiple servers and can identify lost or corrupt data, such as Cassandra.
• Predictive analytics hardware and software: Systems that process large amounts of complex data, using machine learning and algorithms to predict future outcomes, such as fraud detection, marketing, and risk assessments.
• Data mining tools: Programs that allow users to search within structured and unstructured big data.
• NoSQL databases: Non-relational data management systems ideal for dealing with raw and unstructured data.
• Data warehouses: Storage for large amounts of data collected from many different sources, typically using predefined schemas.

The big challenges of big data

Big data brings big benefits, but it also brings big challenges such new privacy and security concerns, accessibility for business users, and choosing the right solutions for your business needs. To capitalize on incoming data, organizations will have to address the following:

• Making big data accessible : Collecting and processing data becomes more difficult as the amount of data grows. Organizations must make data easy and convenient for data owners of all skill levels to use.
• Maintaining quality data : With so much data to maintain, organizations are spending more time than ever before scrubbing for duplicates, errors, absences, conflicts, and inconsistencies.
• Keeping data secure : As the amount of data grows, so do privacy and security concerns. Organizations will need to strive for compliance and put tight data processes in place before they take advantage of big data.
• Finding the right tools and platforms : 0New technologies for processing and analyzing big data are developed all the time. Organizations must find the right technology to work within their established ecosystems and address their particular needs. Often, the right solution is also a flexible solution that can accommodate future infrastructure changes.

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