Advanced Analytics

Advanced analytics employs predictive modeling, statistical methods, machine learning and process automation techniques beyond the capacities of traditional business intelligence (BI) tools to analyze data or business information. It leverages data science that involves mature methods of analysis to project future trends and anticipate the likelihood of potential events. 

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What is the Value of Advanced Analytics?

Advanced analytics provides businesses with a way to operationalize and get more value from data assets. It can include the use of predictive analytics or recommendation engines. It represents a potential way for many businesses to extract value from their data, whether that be data stored in a data warehouse or real-time data generated by business operations. Businesses that employ advanced analytics tools are able to build upon knowledge discovery to inform the likelihood of future events, providing greater confidence for more repeatable business results. Advanced analytics solves the more complex business problems that traditional rearview BI reporting cannot.
Some techniques often used with advanced analytics include data mining, machine learning, data visualization, sentiment analysis, network and cluster analysis, forecasting, pattern matching, complex event processing, and more. All or some of these techniques can be used to explore and model a company’s data to improve the business based upon actionable insights.

What are Some Advanced Analytics Techniques or Capabilities?

The following capabilities are included under the umbrella of advanced analytics:

  • Predictive Analytics: Data analytics used to make predictions concerning future business outcomes, based upon historical data and using statistical modeling (regression models) and machine learning (ML) techniques.
  • Clustering: Data clustering is the process of grouping things together based on similarities between the things in the group.
  • Calculations: Different calculation tools like aggregations on visualizations, expressions, and more can be used in advanced analytics.
  • Statistical Features: It’s important to have statistical features in order to perform advanced analytics, including clustering, box plots, comparison circles, and relationships between categorical variables (Chi-square).
  • Machine learning: Machine learning algorithms learn from the data to produce detailed models that can identify complex patterns and make highly accurate predictions. They are well suited to use cases such as micro-segmentation, personalization, root cause analysis of complex processes, fraud detection, and customer churn.

Why Is Advanced Analytics So Important Today?

While traditional BI tools used to be enough for most companies, executives are now turning to advanced analytics for a greater return on investment (ROI) on their BI investments. Why is that? Why is advanced analytics attracting so many companies today?

With huge amounts of data being generated every day, businesses are looking for new ways to take advantage of all that data. Advanced analytics helps companies to do that. It enables companies to optimize their operations and innovate to gain a competitive advantage. With better customer analysis, predictive analytics, and statistical modeling, advanced analytics is helping companies to improve decision making and keep pace with extremely competitive, quick-changing markets. Where traditional analytics and BI tools fail to hold up, advanced analytics positions companies toward future success.

Benefits of Advanced Analytics

Contributing to real business value, the following are top benefits organizations can obtain a successful advanced analytics program:

  • Predict the Future: Organizations that use advanced analytics can act quickly and with a greater degree of confidence about future outcomes. It enables organizations to make data-driven decisions and gain deeper insights on market trends, customer preferences, and key business activities. Implementing advanced analytics allows businesses to to assess market conditions faster and react to changes before their competitors, gaining a significant advantage.
  • Reduce Risks: With more accurate predictions from advanced analytics, businesses can avoid making costly, risky decisions based upon inaccurate predictions. Advanced analytics provides businesses with a comprehensive understanding of the business, past, present, and future, to better identify and manage risk.
  • Anticipate and Solve Problems: Advanced analytics can help companies solve challenges that traditional BI can not. Based on likelihood, it can prescribe actions to result in better business outcomes.

What are Some Use Cases for Advanced Analytics?

If your business is already using traditional BI tools, it could probably benefit from advanced analytics as well. Advanced analytics can be used for:

  • Supply Chain Optimization: Global enterprises need a supply chain that can react quickly to changes in market conditions, including market demand, customer preferences, and cost fluctuations. Advanced analytics can help, especially in manufacturing, to reduce distribution costs, avoid delays or machine failures, and streamline processes.
  • Marketing Analytics: Understanding customers has never been more difficult and selling and marketing to those customers in the right way at the right time can be a huge challenge. With advanced analytics, companies can create personalized sales and marketing campaigns to reduce spending on inefficient strategies, identify up-sell and cross-sell opportunities, and improve customer experiences.
  • Risk Analysis: Every business looks for ways to mitigate future risk, but only those that employ advanced analytics can manage large data sets, or even data streams in real time, to identify patterns of risk. Advanced analytics techniques can be used to detect fraud in payments and orders or even to monitor reputation among current customers.
  • Human Resources Analytics: Employee satisfaction has a huge impact on business performance, yet most companies fail to take advantage of human resource data. Advanced analytics can be used in HR to reduce employee turnover, costs associated with recruiting and hiring, and to invest in effective rewards programs to maintain or increase employee satisfaction.
  • Business Operations: Traditional BI tools are not agile enough to enable operational excellence that reacts to changing market conditions and optimizes decisions based on predictions about future trends. Advanced analytics allows organizations to improve performance and productivity, increase revenue, and resolve operational issues faster.