The primary goal of Big Data Analytics is to help companies make more informed business decisions by enabling data scientists, predictive modelers and other analytics professionals to analyze large volumes of transaction data, as well as other forms of data that may be untapped by conventional business intelligence (BI) programs.

History and evolution of Big Data Analytics [1]

The concept of Big Data has been around for years; most organizations now understand that if they capture all the data that streams into their businesses, they can apply analytics and get significant value from it. But even in the 1950s, decades before anyone uttered the term “Big Data” businesses were using basic analytics (essentially numbers in a spreadsheet that were manually examined) to uncover insights and trends.

The new benefits that big data analytics brings to the table, however, are speed and efficiency. While a few years ago, a business would have gathered information, run analytics and unearthed information that could be used for future decisions, today that business can identify insights for immediate decisions. The ability to work faster – and stay agile – gives organizations a competitive edge they didn’t have before.

What are the Use Cases of Big Data Analytics?

^BBC8BC7D3978AFD837AE27F6080F7DD63CDEB9AEEA12A22F1D^pimgpsh_fullsize_distrAnalytics, quite literally can be used for varied industries to improve their businesses. Its versatility is what has made it so popular. As companies and other organizations become more familiar with its scope and reach, Big Data Analytics is slated for a huge rise. As with any other developing technology, this process may take some time but eventually its widespread reach will find use in many more applications.

Big Data Analytics lends itself well to a large variety of Use Cases spread across multiple industries. Financial institutions can quickly find that Big Data Analysis is adept at identifying fraud before it becomes widespread thereby preventing further damage. Foreign Financial Institutions (FFIs) have turned to big data analytics to increase their security and combat outside cyber threats.

Another excellent example can be that of credit card companies which give various offers or discounts to customers on the basis of their past transactions. The offers might range from a selection of favorite restaurants in the customer’s vicinity to important tips related to mutual funds or etc.

Big Data Analytics in action can be seen in the following[2]:

  • Testing and Failing Testing- R&D departments can use it to test their hypotheses before making bigger investments.
  • Finding “Win-Win” Alternatives Treatment– By mapping broad and multi-sourced patient data sets, providers can find more cost-effective treatment.
  • Richer Portraits of Customer Profitability– Beyond churn risk metrics, there is competitive advantage when marketing knows which customers are worth keeping with lavish loyalty program versus those high maintenance hagglers that the competition really deserve.

The following image further shows the various avenues where Big Data Analytics has been making a strong foothold[3]:

Data Analytics

Various applications of Big Data Analytics[4]

How does Hexanika make use of Big Data Analytics?

Hexanika is a FinTech Big Data software company which has developed an end-to-end solution for financial institutions to address data sourcing and reporting challenges for regulatory compliance. Our innovative solution improves data quality, keeps regulatory reporting in harmony with the dynamic regulatory requirements and keeps pace with the new developments and latest regulatory updates.

We help establish a compliance platform that streamlines the process of data integration, analytics and reporting. Our software platform can develop and clean data to be sourced for reporting and automation, simplifying the processes of data governance and generating timely and accurate reports to be submitted to regulators in the correct formats. Our solutions also significantly reduce the time and resources required for everyday-regulatory processes, and are robust enough to be implemented on existing systems without requiring any specific architectural changes.

One of the foremost use cases at Hexanika pertaining to Big Data Analytics is to deal with money laundering. Hexanika Solutions can automate processes and utilize vast amounts of available data to ensure compliance with Anti-Money Laundering requirements. These are basically a combination of Aggregation and Semantic join rules which were created to identify outliers. Some of the challenges currently faced by the market experts are:

  • Problems in identifying Suspicious Activity Account due to insufficient data in different data stream.
  • Consulting analysts to manually identify suspicious activity for different accounts in excel spreadsheets of wire transactions data.
  • ​References to customer accounts, different banks geographic locations were inaccurate and hence require lot of manual review of reports.

Hexanika addresses these problems through its unique solutions. To know more about our products and solutions, read: https://hexanika.com/company-profile/

 

Contributor: Akash Marathe

Image Credits: http://www.dianliwenmi.com/ 

 

[1] Source: http://www.sas.com/en_us/insights/analytics/big-data-analytics.html

[2] Source: http://bigdata.teradata.com/US/Big-Ideas/Big-Data-Analytics/

[3] Source: https://www.qubole.com/resources/articles/big-data-analytics/?nabe=5695374637924352:1&utm_referrer=https://www.google.co.in/

[4] Image source: https://www.qubole.com/resources/articles/big-data-analytics/?nabe=5695374637924352:1&utm_referrer=https://www.google.co.in/

1 Comment. Leave new

  • Sankar, you make a great point about the cleaning of data to be sourced. It seems like lots of large companies would benefit from data analytics. I would imagine that this could help project future decisions on things like marketing and changes. I’m curious to know if there are quantitative research professionals who can help assist with this type of interpretation.

    Reply

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