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Data are being generated in more ways, in greater volumes than ever before, from both structured and unstructured sources. Every day, billions upon billions of bytes of data are created. In fact, statistics show that 90% of the data that exists today was generated in the last two years. From transactional records to social media to geolocation data, we are currently in the middle of an information explosion.
The trouble is, while there is no shortage of information hidden in this volume of data, the vast majority of organisations have no idea what to do with it, let alone how to generate usable insight from it that will be of benefit to the business. Organisations need to take control of their big data, harnessing the power of advanced data mining and predictive analytics to understand their data better and to make use of it to unlock competitive edge.
Most big corporates remain unaware of the power that can be unlocked from big data. The majority of businesses are currently only using around 20% of the total data collected to make decisions. The question is, how can any decision made be accurate if it is only taking into account a fifth of the total data? The reality is that it is not. Decisions made on a fraction of the available data will never be completely accurate, and will always involve some element of guesswork. The result of this is that organisations are unaware of the drivers that push costs up, cause customer churn, or have other negative impacts on the business.
Data mining and predictive analytics can be used to optimise transactions, process data and aid smarter decision-making at the point of impact, based on the current situation. Data mining collates structured and unstructured data from a variety of sources and uncovers patterns in this data using predictive techniques. Predictive analytics combines these advanced analytic techniques with decision optimisation, which uses analytical results to determine which actions will drive the best outcomes. These recommended actions, along with supporting information, can then be delivered to the necessary systems and decisions-makers, enabling faster, smarter decisions based on all the facts. Predictions can also be made based on historical patterns, enabling a more agile, proactive business environment which delivers that all important competitive edge.
One of the major benefits of using historical data to predict trends is more accurate budgeting. Using statistical algorithms and historical and seasonal trends, predictive analytics tools can determine more precisely which areas of a budget should be increased or decreased. This takes the guesswork out of budgeting, which means that it is more accurate and funding can be allocated where it is needed to grow the business. These predictive tools can also be used to identify customer behaviour patterns and trends, which in turn will identify drivers for customer churn. Once these drivers are identified they can be addressed and efforts can be directed towards customer retention.
Besides more accurate budgeting, in finance harnessing predictive analytics can also ensure lower risk and fewer wasted resources. Predictive analytics has a variety of applications across the organisation, from human resources to marketing, sales, operations and the call centre to name but a few, regardless of industry.
In HR, trends can identify drivers behind staff churn, which means that measures can be taken to retain employees and staff turnover can be reduced. In marketing, predictive data and a more accurate customer view can allow for targeted campaigns that promote the right things to the right people to deliver high levels of success. In the call centre, real-time data can be used to provide better customer service and once again reduce customer churn.
Sales teams can use predictive data to target customers who are likely to buy a specific product based on data about products they have purchased in the past. Within operations, predictive data can be used to monitor maintenance on equipment and machinery, to predict when it will fail based on historical data and service it before this happens. This in turn means that resources can be better allocated, and service continuity can be assured.
In the past, statistical analysis has been the sole domain of mathematicians. However, new data mining and predictive analytics tools have brought the functionality and power of statistics to the business user. Using analytical information derived from the vast amounts of data generated every day, organisations can leverage this business asset to make more informed decisions that drive better business outcomes and turn big data into a competitive advantage.
Max Ottavini
Director
Cortell Corporate Performance Management
www.cortell.co.za
The trouble is, while there is no shortage of information hidden in this volume of data, the vast majority of organisations have no idea what to do with it, let alone how to generate usable insight from it that will be of benefit to the business. Organisations need to take control of their big data, harnessing the power of advanced data mining and predictive analytics to understand their data better and to make use of it to unlock competitive edge.
Most big corporates remain unaware of the power that can be unlocked from big data. The majority of businesses are currently only using around 20% of the total data collected to make decisions. The question is, how can any decision made be accurate if it is only taking into account a fifth of the total data? The reality is that it is not. Decisions made on a fraction of the available data will never be completely accurate, and will always involve some element of guesswork. The result of this is that organisations are unaware of the drivers that push costs up, cause customer churn, or have other negative impacts on the business.
Data mining and predictive analytics can be used to optimise transactions, process data and aid smarter decision-making at the point of impact, based on the current situation. Data mining collates structured and unstructured data from a variety of sources and uncovers patterns in this data using predictive techniques. Predictive analytics combines these advanced analytic techniques with decision optimisation, which uses analytical results to determine which actions will drive the best outcomes. These recommended actions, along with supporting information, can then be delivered to the necessary systems and decisions-makers, enabling faster, smarter decisions based on all the facts. Predictions can also be made based on historical patterns, enabling a more agile, proactive business environment which delivers that all important competitive edge.
One of the major benefits of using historical data to predict trends is more accurate budgeting. Using statistical algorithms and historical and seasonal trends, predictive analytics tools can determine more precisely which areas of a budget should be increased or decreased. This takes the guesswork out of budgeting, which means that it is more accurate and funding can be allocated where it is needed to grow the business. These predictive tools can also be used to identify customer behaviour patterns and trends, which in turn will identify drivers for customer churn. Once these drivers are identified they can be addressed and efforts can be directed towards customer retention.
Besides more accurate budgeting, in finance harnessing predictive analytics can also ensure lower risk and fewer wasted resources. Predictive analytics has a variety of applications across the organisation, from human resources to marketing, sales, operations and the call centre to name but a few, regardless of industry.
In HR, trends can identify drivers behind staff churn, which means that measures can be taken to retain employees and staff turnover can be reduced. In marketing, predictive data and a more accurate customer view can allow for targeted campaigns that promote the right things to the right people to deliver high levels of success. In the call centre, real-time data can be used to provide better customer service and once again reduce customer churn.
Sales teams can use predictive data to target customers who are likely to buy a specific product based on data about products they have purchased in the past. Within operations, predictive data can be used to monitor maintenance on equipment and machinery, to predict when it will fail based on historical data and service it before this happens. This in turn means that resources can be better allocated, and service continuity can be assured.
In the past, statistical analysis has been the sole domain of mathematicians. However, new data mining and predictive analytics tools have brought the functionality and power of statistics to the business user. Using analytical information derived from the vast amounts of data generated every day, organisations can leverage this business asset to make more informed decisions that drive better business outcomes and turn big data into a competitive advantage.
Max Ottavini
Director
Cortell Corporate Performance Management
www.cortell.co.za
