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Today’s companies produce massive amounts of data, but utilizing and managing that data effectively directly translates to stronger decision-making and growth. Artificial intelligence (AI) and machine learning (ML) are two technologies that have recently garnered significant attention due to their potential for significant productivity and insight gains. But, how companies align them to current and future strategies and goals is critical to analytics success.
AI and ML are important facets of an effective advanced analytics strategy that applies intelligent statistical methods to data to uncover efficiency and growth opportunities. In fact, advanced analytics is the foundation on which generative AI (GenAI) can deliver capabilities like intelligent forecasting and modeling. As AI and ML adoption increases, organizations will benefit from enhanced operational efficiencies, accelerated decision-making processes, and the ability to leverage data-driven insights for strategic growth initiatives.
The growth of advanced analytics has dramatically increased the value of data, and the availability of data grows with every digital transaction, action, interaction, and record. It’s a virtuous cycle — with more data and more types of data come more meaningful analysis and new opportunities for discovery.
“Data has always been a valuable asset, but digital transformation initiatives have created a greater depth of more refined data that can support more informed business decisions,” says RSM US Principal George Casey. “Advanced analytics tools like AI and ML can unlock the powerful insights that data holds, solving critical business problems and strengthening product and service offerings.”
Modern advanced analytics solutions can be leveraged in many ways, providing new avenues to use existing data to enhance key processes. These include:
Organizations can use their data to transform business processes using GenAI tools such as Microsoft Copilot or ChatGPT. These capabilities enable companies to sift quickly through data to answer questions or conduct detailed analyses. Companies can also utilize GenAI to create content, summarize large documents, and develop interactive agents.
CASE STUDY: As its population grew, The City of Kelowna in British Columbia sought to scale and modernize public resources. RSM worked with city leaders to understand the potential of incorporating GenAI into Kelowna’s new IT strategy. That collaboration led to the development of a building permit AI assistant that facilitated more rapid permit processing to address the housing and construction shortage with the potential to extend to additional permitting processes across the city.
Companies use advanced analytics tools like AI and ML to create new products and services, personalize customer experiences through behavioral research, and enhance product performance with predictive maintenance. More specifically, they combine and analyze multiple data sources to identify patterns and outliers, then respond with actions to achieve a desired outcome.
CASE STUDY: Corewell Health has a strong focus on enhancing patient health and reducing health disparities, and the organization sought a data-driven approach to improve the overall patient experience and promote health equity. RSM developed an advanced analytics approach for Corewell Health, integrating the patient experience survey data with clinical records and enabling a holistic view of patient interactions. This approach facilitated the implementation of targeted strategies to improve overall patient satisfaction and operational efficiency.
Thanks to advanced analytics, enhanced risk mitigation, fraud detection, and anomaly recognition can now be done at scale. Quickly analyzing large datasets enables rapid decision-making and interventions. Advanced analytics empowers companies to reduce risk through early detection, predictive maintenance, and proactive measures.
With advanced analytics, specifically GenAI, organizations can analyze multiple datasets to more accurately forecast demand, reducing costs with more efficient planning processes that utilize macroeconomic data. Beyond analyzing their data, companies can employ syndicated datasets to search for specific signals that may affect business — all at scale.
CASE STUDY: A national nonprofit organization embarked on a digital transformation process to utilize data analytics to replace time-consuming manual processes that yielded minimal insight. The RSM team worked with the organization to centralize data into an enterprise data warehouse built on Microsoft Azure, implement a governance framework for data democratization, and utilize interactive analytics with key performance indicators to analyze member journeys.
Capital-intensive companies can use advanced analytics to balance supply and demand by considering the relevant aspects of production, such as workforce, local regulations, suppliers, and infrastructure. Understanding and reaching the right balance of physical assets for current demand — and making agile decisions to meet changing demand — simply cannot be effectively achieved without advanced analytics.
CASE STUDY: A national food manufacturer and distributor aimed to improve sales forecasting and planning to allocate assets to meet demand. RSM automated a company’s forecasting models that covered sales, costs, and commodities while leveraging millions of external datasets. The company now has a detailed forecast model with data that is refreshed hourly, leading to an improvement in forecast accuracy and more access to leading macroeconomic indicators.
Data analytics strategies based on AI and ML are more accessible, affordable, and powerful than ever. However, rather than focusing solely on technology, a more effective approach takes a holistic view, starting with the specific business problem at hand and leveraging strong technical knowledge and deep industry experience. While technology plays a key role, aligning processes and adopting the right perspective uncover deeper insights, leading to data-driven and impactful truths.
From understanding customer preferences and satisfaction drivers to outlining product demand patterns, middle-market organizations now have access to advanced data solutions and a wealth of data resources.
Discover how RSM’s advanced analytics and AI solutions enable you to achieve targeted business outcomes, including increasing profitability, enabling data-driven decisions, improving operational efficiency, and managing risk.
About the Author:
George Casey is a principal at RSM US LLP and leads the firm’s advanced analytics practice, with a focus on artificial intelligence.