How does Big Data Analytics help in constant innovation?

Network Science July 25, 2022

The concept of big data has been around for years; most organisations now understand that if they capture all of the data that flows into their businesses (potentially in real time), they can apply analytics and derive significant value from it. Big data analytics refers to the methods, tools, and applications used to collect, process, and derive insights from diverse, high-volume, high-velocity data sets. These data sets may come from a variety of sources, including the web, mobile, email, social media, and networked smart devices. They frequently contain data that is generated at a high rate and in a variety of formats, ranging from structured (database tables, Excel sheets) to semi-structured (XML files, webpages) to unstructured (images, audio files).

Big Data Analytics Processes

By analysing data sets, analytics solutions gain insights and predict outcomes. However, before the data can be successfully analysed, it must first be stored, organised, and cleaned by a series of applications in a step-by-step preparation process:

  1. Gather Data: Every organisation’s approach to data collection is unique. With today’s technology, businesses can collect structured and unstructured data from a variety of sources, including cloud storage, mobile apps, in-store IoT sensors, and more. Some data will be stored in data warehouses, where it will be easily accessible by business intelligence tools and solutions. A data lake can be used to store raw or unstructured data that is too diverse or complex for a warehouse.
  2. Process Data: Once data has been collected and stored, it must be properly organized in order to produce accurate results on analytical queries, especially when the data is large and unstructured. Data availability is increasing at an exponential rate, making data processing difficult for organizations. Batch processing, which examines large data blocks over time, is one processing option. When the time between collecting and analysing data is long, batch processing comes in handy. Stream processing examines small batches of data at once, reducing the time between collection and analysis and allowing for faster decision-making. Stream processing is more difficult and frequently more expensive.
  3. Clean Data: To improve data quality and get stronger results, all data must be formatted correctly, and any duplicate or irrelevant data must be eliminated or accounted for. Dirty data can obscure and mislead, resulting in flawed insights.
  4. Analyse Data: It takes time to convert big data into usable information. Once ready, advanced analytics processes can transform big data into big insights. Big data analysis methods:
    1. Data mining searches large datasets for patterns and relationships by detecting anomalies and forming data clusters.
    2. Predictive analytics makes future predictions based on an organisation’s historical data, identifying upcoming risks and opportunities.
    3. Deep learning mimics human learning patterns by layering algorithms and finding patterns in the most complex and abstract data.

Different types of Big Data Analytics

  1. Diagnostic Analytics: Diagnostic analytics is an advanced type of big data analytics that can be used to investigate data and content. Using this type of analytics, you can answer the question, “Why did it happen?” So, by analysing data, you can understand the reasons for certain behaviours and events related to the company you work for, their customers, employees, products, and so on.
  2. Descriptive Analytics: Descriptive analytics is one of the most common types of analytics used by businesses to stay current on current trends and operational performance. It is one of the first steps in analysing raw data by performing simple mathematical operations and producing statements about samples and measurements. After identifying trends and insights with descriptive analytics, you can use other types of analytics to learn more about what causes those trends.
  3. Prescriptive analytics: Prescriptive analytics uses descriptive and predictive analysis results to find solutions for optimising business practises using various simulations and techniques. It employs data insights to recommend the best course of action for the company.

Why is Big Data Analytics so important?
Big data analytics assists businesses in harnessing their data and utilising it to identify new opportunities. As a result, smarter business decisions, more efficient operations, higher profits, and happier customers follow. Businesses that leverage big data and advanced analytics gain value in a variety of ways, including:

  1. Cost-cutting measures: When it comes to storing large amounts of data, big data technologies such as cloud-based analytics can significantly cut costs (for example, a data lake). Furthermore, big data analytics assists businesses in finding more efficient ways to conduct business.
  2. Making faster and more accurate decisions: The speed of in-memory analytics, combined with the ability to analyse new data sources, such as streaming data from IoT, allows businesses to analyse information quickly and make informed decisions.
  3. New product and service development and marketing: Analytics enables businesses to provide customers with what they want when they want it. With big data analytics, more businesses can develop innovative new products to meet the changing needs of their customers.

Big data analytics can ultimately fuel better and faster decision-making, model and predict future outcomes, and improve business intelligence. A scalable analytics solution can benefit any organisation that works with large amounts of data, which is why many major industries, including retail, entertainment, and healthcare, already use big data to establish strategy, lower costs, and predict customer needs.

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