Public corruption has cost governments millions of dollars, and severely worsened the plight of those who are poor. The Panama Papers revealed how high-ranking officials, energy companies, foundations, and trusts colluded, especially in tax-free areas. Corruption has proven to be very difficult to solve and fighting it remains challenging. However, experts are turning to a new tool to fight corruption: big data analytics.
How does big data analytics work?
Governments and corporate records are often lengthy, complex documents that the average person cannot read easily. Indeed, it would take someone of immense discipline and technical knowledge to discover the intricate patterns that indicate mishandling of funds – a sign of corruption. However, with big data analytics, it’s possible to comb through the information to find intricate details that humans can miss. For example, the Panama Papers was written because journalists used big data to analyse to review over 11,000 documents.
Furthermore, big data analytics can assess several different information sources to discover trends that are otherwise easy to miss. For example, the European Commission and Transparency International used their own proprietary big data analytics software to compare data from private and public institutions, to find irregularities, conflicts of interest and other signs of corrupt behaviour. The ability to analyse and compare different sources of information is one of the biggest advantages of big data.
Big data analytics can employ data mining techniques to discover tax fraud and evasion. David Frankel, a New York finance commissioner, used data mining techniques to survey the tax records of businesses based in the city. Investigators used big data to find businesses that did not fit the normal tax payment pattern, a possible sign of tax evasion. The new information allowed investigators to identify businesses that were potentially using schemes to evade taxes. In other words, investigators could conduct a smarter, more efficient tax evasion investigation process.
The presence of big data analytics could deter corruption simply because it is so effective in identifying incidents of corruption. Recently, Timothy Persons, who was the Chief Scientist of the United States Government Accountability Office (GAO) in 2016, said that corruption is a cost-benefit analysis, those who commit to corruption do so because the benefits outweigh the costs.
Challenges of big data analytics
Despite the immense potential of big data in fighting corruption, and its use by major organisations like the European Commission, there are still some difficulties to overcome.
The need for open data
Most public institutions do not have a framework for sharing data. Therefore, it is very difficult for most people to find data connected to budgets for development projects, or policy decisions. Having a common framework for open data helps set a common standard. As a result, it will be much easier to read and interpret the information from different organisations. When it is easier to read data, investigators can ask more complex questions about how resources were spent, making it much easier to track down incidences of corruption.
Data and analytics tools must be reliable
There is no point in investing in data analytics platforms if the data is not accurate. The latest analytics platforms are intelligent with AI and machine learning capabilities. However, these platforms are still designed to process data and, if the information is not accurate, the results will not be accurate either. This highlights the importance of national institutions responsible for collecting the data that analytics platforms will use.
The analytics tools also require a strong team of data scientists to get the best out of the tools. However, finding qualified data scientists remains a huge challenge because data analysts are in high demand, and many of them are lured into the private sector via lucrative offers.
Corruption has always plagued bureaucracy, costing governments millions of dollars. Fighting corruption has proven to be a challenge. However, big data analytics could change the situation. Data analytics can assess terabytes of information to find trends and identify cases deviating from the norm to improve investigations into corruption. However, despite the immense potential of analytics, there are still some challenges to overcome, like the need to standardise open data, make sure data is reliable and building a strong analytics team.
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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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