Advanced Analytics – What Does It Really Mean?

by Darin Ellingson, Validation SME, Verista

Modern businesses collect massive amounts of data; it’s the nature of how business is conducted. What each business does with their data is critical to how efficiently they can operate. Is your data utilized to its fullest? What insights does your data provide? Does it help you manage risk? What about decision making? Historically these items were referred to as Business Intelligence (BI), but today we refer to them as Advanced Analytics (AA).

Is there a difference or is this a shift in nomenclature? The major underlying differences are BI uses data to determine if something occurred, when did it occur, who did it and the nature of what occurred. AA addresses the why, will it happen again, what happens if a process is changed, and what else can our data tell us. Most businesses are already well versed in BI (Reports, Metrics, KPIs, Dashboards, Scorecards, Database Queries, Real-Time BI), however AA can still be confusing. AA encompasses many different technologies and techniques (Artificial Intelligence [AI] and Machine Learning [ML], Predictive Analysis, Deep Learning, Process Automation, Data Visualization) working together to provide deeper understanding and insights.

Techniques employed in AA

  • Predictive Analysis – utilizing historic data to predict future occurrences/outcomes using statistical and machine learning models.
  • Data Visualization – utilization of graphical representations of data to ease analysis and consumption across multiple areas of the business. The concept of a digital twin arose from these techniques, which allows businesses to create a complete digital representation of their operations. Data is updated in real time from associated systems. Business scenarios can be simulated virtually at little expense, and ML models can aid with reasoning and decision making.

  • Machine Learning – a form of AI that uses algorithms to process data, learning from what it has processed by identifying patterns and applying what it has learned to better accomplish its work. If a process has predetermined rules and relies on specific data, ML models can be applied.

  • Data Mining – process of sorting through datasets to identify patterns and relationships within data via various analyses.

  • Event Processing – analysis of complex events using low-level data to predict high-level events.

  • Clustering – process in which unstructured data sets are analyzed for similarities and/or matches.

  • Anomaly Detection – algorithms detect data points that are significantly divergent from the rest of the data.

Advantages to AA

  • Faster Data Driven Decisions – business moves faster and faster every day, with real-time data and decision support modeling, decisions can be made much more rapidly than with traditional BI.

  • Deep Data Insights – using predictions from AA, deep data insights are gained that aid in data driven decisions such as how well critical business processes are functioning, market trends, and customer trends.

  • Risk Reduction – with higher accuracy, deeper insights, and predictive modeling, risk reduction is greatly enhanced, and high-priced errors and mistakes are more easily avoided.

  • Predictions and Forecasting – AA provides predictive analysis capabilities that enable validation of forecasting models and insights towards where strategies may require change.

  • Opportunities and Problems – the same predictive capabilities can be used to identify areas of opportunities and potential problems. This provides businesses knowledge to steer clear of problems and act on new opportunities that traditional BI cannot.

Advanced Analytics Use Cases

  • Supply Chain – utilizing AA, businesses can monitor and predict critical supply chain parameters such as demand for specific products, fluctuations in vendor cost, trending of lead times and raw material quality across vendors.

  • Risk Management – application of AA to specific sets of data can help identify patterns where high levels of risk reside and provide trending to validate remediation efforts. Using predictive analysis, new areas of risk can be identified, and existing risk areas monitored and predictions for potential issues provided.

  • Operations – businesses can actively streamline operations by using predictive models and deep data insights, providing more future proof operating models.

  • Manufacturing – moving from preventative to predictive maintenance can increase output, reduce downtime, and increase revenues by eliminating unnecessary maintenance prescribed in preventative models.

Next steps for companies to continue their Pharma 4.0 enablement is to dig deeper into Artificial Intelligence and Machine Learning. What is Data Science? Why are Data Warehouses and Lakes critical to AI/ML? What is required to get started with AI/ML and what skillsets does your enterprise require to effectively utilize these technologies will be discussed in part 3 of this series.

Reach out to Verista to see how we can help you with your next Advanced Analytics project!