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As more individuals and businesses become reliant on technology, more data is readily available and collected from various sources – computers, mobile devices, sensors, machine-to-machine technology, surveillance systems and more. By 2020, the amount of big data generated annual is expected to increase by 4300%.
Various industries including retail, service, bioscience, manufacturing, medicine, agriculture, SEO, social media, espionage and many more, reap benefits from big data analytics. Thus, knowing how to leverage the data will allow the organisation to achieve competitive advantage. For example, oil and gas exploration relies on big data, using vast amounts of seismic data to locate and simulate reservoir while product-oriented businesses are collecting big data to analyze customer preferences and purchases over time. The benefits of big data and incorporating business intelligence are endless.
However, there is still a gap. Gartner said that the current amount of big data collected is in fact astounding, yet “the accelerating merger of the physical and virtual worlds will make the present volumes seem paltry in comparison.”
Here are a few key examples of how companies and industries are using Big Data Analytics.
Example for Risk Management
UOB Bank (Singapore) is an example of a brand that uses big data to drive risk management. As a financial institution, a poor risk management strategy may result in huge losses. The new risk management system incorporates big data to reduce the calculation time of the value at risk – from 18 hours to just a few minutes. Through this initiative, the bank will possibly be able to carry out real-time risk analysis soon (Andreas, 2014).
Example for Supply Chain Efficiency
PepsiCo is a consumer-packaged goods company that relies on huge volumes of data for an efficient supply chain management. The company is committed to ensuring they replenish the retailers’ shelves with appropriate volumes and types of products. The company’s clients provide reports that include their warehouse inventory and the POS inventory to the company, and this data is used to reconcile and forecast the production and shipment needs. This way, the company ensures retailers have the right products, in the right volumes and at the right time.
Example for Oil & Gas
The integration and mining of data produced in the hydrocarbon finding and producing process provides solutions to locate where oil is present and the best way to extract them. For example, Drillinginfo, a leading data and intelligence provider of upstream data for oil and gas decisions, has begun to break the barriers of geography and discipline to consider many potential variables and, based on thousands of wells, create a statistically predictive model for a given area’s producibility.
Streamlining Businesses Through Big Data Analytics is a 2-day training course held from 25-26 April 2019 (Singapore) and 29-30 April 2019 (Kuala Lumpur) to learn the best practices on how to utilise Business Intelligence (BI) for businesses. This course differs from other training programs as it is not vendor specific, and participants will learn the best practices to implement BI throughout the firm and to perform predictive analysis as well. For BI consultants, IT managers or anyone who has an interest in BI, the course includes various assignments and group discussions to better understand how to create and utilise BI frameworks and develop solutions for businesses.
Interested to read other articles? Check out: 4 Benefits of Using Big Data
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