Why big data analytics is important
Data has become a new gold for organizations. Companies can use big data analytics software to process data and make data-driven decisions, which can significantly improve businesses. Some key benefits of data analytics include accurate targeting and marketing, uncovering new revenue opportunities, enhanced operational efficiency and product personalization. In the current competitive business landscape, effective strategy enabled by information can offer a competitive advantage over rivals.
How big data analytics
Big data analytics is the collection, processing, curating and analyzing large datasets to help businesses operationalize their data. This work is done by professionals, including data scientists, data analysts, statisticians, modellers, and other professionals. The process entails:
- Collection of data
Data is gathered in both structured and unstructured form from different sources like mobile applications, IoT sensors, cloud storage and daily transactions, and more. Some of this data is stored in warehouses where business intelligence tools can easily access it. This data is raw and diverse and is assigned metadata or stored in a data lake.
- Data processing
Once data has been gathered and stored, it is organized to get accurate results on analytical queries, especially when data is massive and unstructured. With the exponential growth of data, data processing is always a challenge to organizations. One of the approaches to processing is batch processing, where data in large blocks is processed over time. There is also stream processing where small batches of data are processed simultaneously, shortening the delay between collection and analysis phases. This is done to speed up decision-making.
Regardless of the size, data requires some scrubbing to improve its quality and results. Cleaning involves formatting data correctly and eliminating any duplicates or irrelevant data. Doing this is crucial because such data can mislead.
Even if you are in possession of data, getting it into use takes time. Once you have done all the other tasks and data is ready, advanced analytics processes are undertaken to turn big data into insights. Some methods often used in the analysis include data mining, predictive analytics and deep learning. With the data mining method, large datasets are sorted out to identify patterns and relationships by identifying anomalies and creating clusters. On the other hand, predictive analytics uses the historical data of an organization to make predictions and identify the upcoming risks and potential future opportunities. Deep learning imitates the learning patterns of humans using artificial intelligence and machine learning to find patterns in complex and abstract data.
Key data analytics tools
Key technologies used to support the data analytics processes include:
- Hadoop- This is an open-source framework used to sort and process large data sets. It can handle massive amounts of both structured and unstructured data.
- Stream analytics- These are tools used in filtering, aggregating and analyzing big data stored in different formats.
- Predictive analytics- This is hardware and software that processes large data amounts. It uses machine learning and logistical algorithms to make predictions about future outcomes.
Generally, big data analytics has become highly beneficial in business. It is now used by companies in different sectors to enhance decision-making.