The Evolution of Business Analytics: From Reactive to Proactive

Business analytics — Boy have things changed!  In the past static reports were all that we had to use now there are interactive dashboards that allow users to dig deeper into their data and utilize it while it’s still current.

Forecasting Made Easy

Gone are the days when forecasting was done with crystal balls. People are using “intelligence” in their “business”. Business forecasting has seen a tremendous shift in methodologies from gut instinct to statistical forecasting and demand modeling for example.

No matter what you’re trying to achieve, either estimating future monthly sales or optimiz-ing your supply chain, forecasting is all about using existing data to predict the future. To accomplish this, very large amounts of data must be processed quickly and efficiently. Nobody can beat a machine when it comes to that. By using this processing, improved data access is possible and previously hidden insights can be uncovered.

Advanced analytics revolves around predicting trends and future possibilities and making recommendations based on potential outcomes. Businesses are using sophisticated tools like simulation, machine learning and data mining to identify trends and patterns in structured as well as unstructured data. There are numerous potential use cases for machine learning- risk detection, behavioral analysis, customer support, image recognition, text analysis, and much more.

Machine learning and Artificial Intelligence (AI) will make estimates of future behavior just as accessible as historical data.

Outlier identification simplified

Machine learning enables businesses to find anomalies as they occur, and this real-time identifi-cation lets them take immediate action. This can be used in cases where real-time information can drive valuable responses, such as in fraud detection & surveillance, allowing fraud to be identified immediately and an alert sent to the customer. Real-time information processing can also assist image & voice recognition, and product recommendations.

To take it a step further, businesses should consider feeding this information into their business intelligence system (BI) to aid in policy exchange and product development.

Machine learning and AI capabilities, when combined, allow businesses an opportunity to catch inconsistencies in real time and correct them before they can become problems and or issues.

Implications of Machine Learning

In the case of machine learning, you get what you give. Machine learning is based on algorithms that learn from data and is dependent on relevant and reliable data. In other words, we are teaching a program how we make decisions. The value extracted from machine learning depends greatly on the quality of algorithms, reliable data, and the degree to which these systems process structured and unstructured data. So, businesses must analyze all available data for quality.

Organizations should have a clear goal in mind. What are the questions that need to be answered and what data is needed to answer those questions?

The adoption of machine learning enabled AI applications allows faster decisions and more accurate insights. This is useful for product development, supply chain, logistics, customer relationship management, marketing to name a few. In a hyper competitive world, businesses that can realize value from their data assets using advanced analytics such as machine learning and artificial intelligence will be ahead of the competition.

~Kunika Sodhi, Associate

One Reply to “The Evolution of Business Analytics: From Reactive to Proactive”

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