Big data analytics refers to the strategy of analysing large volumes of data or ‘big data’. This big data is gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. There’s one clear notion that differentiates big data analytics from other forms of analytics: the volume, scale, and diversity of the data being analyzed. Big data analytics software analyses all this data to uncover patterns and connections that might otherwise be invisible.
This process will provide valuable insights that assist in bettering an organisation’s products and services. Big data analytics software helps organisations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more seamless operations, higher profits, happier customers, and a much needed competitive edge.
Here are a few benefits of leveraging big data analytics software.
Traditional methods of data analysis just won’t make the cut anymore
To analyse such a large volume of data, big data analytics software utilise specialised software tools and applications that cover a range of data analytics operations – from predictive analytics to data mining to text mining to forecasting, and data optimisation.
Collectively, these processes are separate but highly integrated functions of high-performance analytics. Using big data tools and software enables an organisation to process extremely large volumes of data that a business has collected over time. This can then be used to determine which data is relevant and can be analysed to drive better business decisions in the future.
For most organizations, big data analysis is a challenge. Considering the sheer volume of data and the different formats of the data that is collected across an entire organisation and the many different ways different types of data can be combined, contrasted, and analysed to find patterns and other useful business information.
The first challenge is in breaking down data silos to access all data an organization stores in different places and often in different systems. A second challenge is in creating platforms that can pull in unstructured data as easily as structured data. This massive volume of data is typically so large that it’s absolutely difficult to process using traditional database and software methods. It would account for tediously long and laborious hours of sorting out information and then deciphering the information to leverage.
Naturally, this would take way too long and your competitors would have run laps around you while you were still busy tying up your shoelaces.
Gaining in-depth customer insights using big data analytics software
Every time customers click on a digital ad, write a review, “like” a product, browse your website, contact your call centre, place items in a cart, and make or walk away from a purchase, they are generating interaction data. Along the way, companies like yours are collecting an unprecedented amount of customer data. All of it can be enriched with all types of publicly available data and then analysed.
These types of customer data make up a vast percentage of big data that’s contributing to the 2.5 quintillion bytes of data being created by consumers and enterprises every day. Armed with the right big data analytics software, you can analyze your customer data to generate valuable insights into each customer’s journey, such as patterns, trends, traits, and tendencies that will help you take informed steps to accelerate customer acquisition, prevent churn, and supercharge your business’s overall efficiency.
Having access to numerous metrics, for example, in the context of social media, such as likes, post reactions, post replies and more, allows a business to better understand the very nature of the interaction between their customer base and their content.
Detecting fraud using big data analytics software
In highly regulated sectors like financial, healthcare, insurance, retail, and social security, combating fraud is essential as there are a multitude of compliance, regulations, risk management measures, and monetary consequences to be dealt with. The proliferation of modern technology has produced more sophisticated fraud techniques, but technology advancements have also enabled smarter approaches to detect fraud.
Fraud data analytics play a crucial role in the early detection and monitoring of fraud. These data analytic techniques will help the organization to detect the possible instances of fraud and implement an effective fraud monitoring program to protect the organization. The result? Unprecedented savings.
Today’s advances in big data analytics software allow researchers and data analysts to perform what our ancestors would call ‘miraculous’, From decoding human DNA in minutes and determining which gene is most likely to be responsible for certain diseases to which ads you are most likely to respond to on Facebook, organisations who are serious about achieving efficiency and optimizing revenue need to employ big data analytics software.
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