Big Data Analytics is the collection and analysis of a large amount of data to achieve valuable results that are useful for decision making. For this, it uses advanced collection systems of large collections of structured and unstructured data to produce valuable information for companies. It is widely used in industries as varied as Health Tech , fintech, education, insurance, artificial intelligence, retail, and carrier to understand what works and what needs redirection to improve processes, systems, and profitability.
Customers generate a large amount of data. Every time we open an email, tag someone online, use our smartphone apps, talk to any customer service representative, make an online purchase, or contact a virtual assistant, service providers and corporations can take advantage of this information, collecting that raw data.
These huge, disorganized groups of data are called big data. In simpler words, Big Data by itself is a huge volume of data, And these data sets come in various forms and from multiple sources. Information is the backbone of any business organization – any integral part, just like other business applications such as on-board software, data rooms, financial applications, etc.
Yes, companies understand the importance of data collection; they are constantly looking for more and more raw data. However, it is not enough. Corporations have to securitize raw data into more pragmatic insights for better insights and informed decisions. This is where Big Data Analytics comes into action to process all this batch of information.
For example, Big Data analytics is an integral part of the modern healthcare industry. As you can imagine, thousands of patient records, insurance plans, prescriptions, and immunization information need to be managed. It comprises vast amounts of structured and unstructured data, which can provide important insights when analytics are applied. The results provided by this method do this quickly and efficiently so that healthcare providers can use the information to make informed diagnoses that save lives. For this reason, it is necessary that you consider Make a consultation to integrate this process into your company's decision-making.
How does Big Data Analytics work?
Big Data Analytics works as a process of discovering trends, patterns, and correlations in large amounts of raw data to help make data-driven decisions. These processes use familiar techniques of statistical analysis, such as clustering and regression, and apply them to larger data sets with the help of newer tools.. Its use has become popular since Big data has been a buzzword since the early 2000s, when software and hardware capabilities made it possible for organizations to handle large amounts of unstructured data.
Since then, new technologies, from Amazon to smartphones, have further contributed to the substantial amounts of data available to organizations. With the data explosion, the first innovation projects such as Hadoop, Spark, and NoSQL databases for big data storage and processing were created.
This field continues to evolve as data engineers look for ways to integrate the vast amounts of complex information created by sensors, networks, transactions, smart devices, web usage, and more. Even now, Big data analytics methods are being used with emerging technologies such as machine learning to discover and scale more complex insights. Big Data Analytics can be summarized in the following steps:
- data collection it looks different for every organization. With today's technology, organizations can collect structured and unstructured data from a variety of sources, from cloud storage to mobile apps, in-store IoT sensors, and more. Some data will be stored in data warehouses where it can be easily accessed by business intelligence tools and solutions. Raw or unstructured data that is too diverse or complex for a warehouse can be assigned metadata and stored in a data lake.
- Data processing. Once data is collected and stored, it must be properly organized to get accurate results from analytics queries, especially when the data is large and unstructured. Batch processing is useful when there is a longer response time between data collection and analysis. Stream processing analyzes small batches of data at a time, reducing the lag time between collection and analysis for faster decision making. Stream processing is more complex and often more expensive.
- Clean data. Big or small data requires cleaning to improve data quality and get better results after Big Data Analytics; all data must be in the correct format and any duplicate or irrelevant data must be removed or accounted for. Dirty data can obscure and mislead, creating the wrong insights.
- Analysis of 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 analytics analysis methods include:
- predictive analytics uses an organization's historical data to make predictions about the future, identifying upcoming risks and opportunities.
- Deep learning mimics human learning patterns by using artificial intelligence and machine learning to overlay algorithms and find patterns in the most complex and abstract data.
Because it is important?
Big Data Analytics is important for the development of your company because it helps to take advantage of your data to identify opportunities for improvement and optimization. Across different business segments, increased efficiencies lead to smarter overall operations, higher profits, and satisfied customers. Big data analytics helps companies reduce costs and develop better customer-centric products and services.
For example, its implementation helps provide information that improves the way our society works. In healthcare, big data analytics not only tracks and analyzes individual records, but also plays a critical role in measuring COVID-19 outcomes on a global scale. It informs the ministries of health within the government of each nation on how to proceed with vaccinations and devises solutions to mitigate pandemic outbreaks in the future.
Nearly eight in ten users (79 percent) they believe that “companies that do not adopt big data will lose their competitive position and could even become extinct”, according to a report from Accenture. In its survey of Fortune 500 companies, Accenture found that 95 % of companies with revenues greater than $10 billion reported being “very satisfied” or “satisfied” with their big data-based business results.
Some of the benefits of implementing Big Data Analytics with the perfect help:
- Costs reduction: Big data Analytics can reduce costs by storing all business data in one place and performing follow-up analysis to find ways to work more efficiently to reduce 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 companies understand product feasibility and keep up with trends.
- Strategic business decisions– The ability to constantly analyze data helps companies make better and faster decisions, such as cost and supply chain optimization.
- Better customer experience: Data-driven algorithms aid marketing efforts (targeted ads, for example) and increase customer satisfaction by providing an improved customer experience.
- Risk management: Businesses can identify risks by analyzing data patterns and developing solutions to manage those risks.
Some of the most common uses of Big Data Analytics are the following:
- Entertainment: Offering a personalized movie and music recommendation based on a customer's individual preferences has been transformative for the entertainment industry (think Spotify and Netflix).
- Education: Big data Analytics helps schools and edtech companies develop new curricula while improving existing plans based on needs and demands.
- Health: Tracking patients' medical records helps doctors detect and prevent disease.
- Government: Big data can be used to collect data from CCTV and traffic cameras, satellites, cameras and body sensors, emails, calls and more, to help manage the public sector.
- Marketing– Customer information and preferences can be used to create targeted advertising campaigns with a high return on investment (ROI).
- Fintech: Big Data Analytics can help track and monitor illegal money laundering.
In Codster, we can be your ally in the development and implementation of Big Data Analytics to exploit the potential of your company, creating technological solutions for identity validation tailored to your needs. If you want to know more, do not hesitate to contact us.