Show notes
Show Notes: The Umbrex Business Analytics Diagnostic Guide that is discussed in this episode can be downloaded at no cost here: https://umbrex.com/resources/business-analytics-diagnostic/ In this episode of Unleashed, Will Bachman and Adam Braff discuss the creation of a data analytics diagnostic guide. Adam, a former partner at McKinsey and a consultant on data analytics, discusses the importance of data analytics in solving business problems in any company or investment firm. He explains that a business analytics diagnostic is designed for organizations with multiple people, computers, and analytics processes. The goal of this diagnostic is to determine the performance and alignment of the data science or analytics function with the overall mission of the company. He explains the size and type of company that uses this and who would monitor and manage the data analytics of a company The Diagnostic Guide Format Explained The diagnostic guides follow a format with scorecards for individual pieces of an area, typically 15 to 25 different scorecards, and within each one, objective criteria ranging from nascent to optimized. These guides are divided into categories and subcategories, such as analytics strategy, data management, advanced analytics, AI, talent, decision-making process, tools, and infrastructure. Adam explains the format of the diagnostic guide, beginning with top level categories including analytics strategy, strategic alignment, performance measurement, and future roadmap. Analytic strategy involves understanding the business objectives and problems to be solved, such as growth, customer retention, risk management, and problem-solving. Strategic alignment also involves determining the location of analytics people, whether centrally located in a Center of Excellence or distributed across different functions. Performance measurement involves tracking key performance indicators for the analytics function, such as cross-sell, revenue, pricing, and marketing ROI teams. Benchmarking this number against competitors can help determine if the company is on track and if it is underinvesting in analytics. Performance measurement also includes ROI, which is the understanding of specific goals and projects that the analytics team is working on. By tracking these metrics and reporting the total impact analytics has on the business each year, the analytics strategy part can be evaluated. A Roadmap for the Analytics Strategy Adam emphasizes the importance of having analytical people distributed throughout the business and dedicated resources for analytics initiatives. To round out the analytic strategy, it is crucial to have a roadmap of the next eight quarters, such as tackling Net Promoter Score analysis, customer satisfaction drivers, or adopting a new data management tool. This roadmap should include hiring and development strategies, cutting-edge innovation, and research, which can be revisited and changed strategies as needed. This helps ensure the analytics team is effectively working towards achieving their goals. Data Management: Warehousing, Sourcing and Integration Adam goes on to talk about the importance of warehousing, data sourcing and integration involving sourcing data from internal systems or external sources, such as customer satisfaction surveys or third-party surveys. This is crucial for asset managers who need to acquire data for investment analysis and decision-making. Automating data loading processes is also important, as it allows for efficient data flow. Business intelligence is another important aspect of data management, which involves creating interactive dashboards and alerts for all stakeholders. Data quality is a critical aspect of data management, involving conscious decisions on the quality of data. More mature businesses have higher standards for accuracy, timeliness, and completeness of data, with constant profiling and monitoring to ensure data meets these standards. Data governance encompasses coordination across different parts of the business, ensuring consistency in data definitions, appointment and training of stewards, and governing data for regulatory and compliance purposes. Advanced Analytics and AI-Driven Decision Making Adam discusses the importance of analytics in a company's operations, particularly in areas like operational analytics and revenue. He highlights the need for centralized, advanced analytics functions that focus on predictive modeling, machine learning, and AI-driven decision making. These functions should be evaluated for their maturity and effectiveness. Another area of focus is AI-driven decision making, which involves how a company uses AI to improve operations. He goes on to talk about talent management and three main areas: people, performance, and technology and how these tools can be used in this area. Training and development are crucial aspects of analytics talent management. This includes understanding skill gaps within the team, designing a curriculum to fill them, and providing continuous learning opportunities. Internal or external certifications and specializations can also be beneficial. Lastly, community engagement and collaboration are essential aspects of analytics talent management. This involves sharing knowledge with the organization, building collaboration, and engaging with external partnerships and networks. Adam explains how innovation and co-creation initiatives can help spur creativity and innovation within the analytics team. These efforts can be internal or external, pushing the envelope on innovation and ensuring the success of the business. Overall, analytics talent management is a critical aspect of a company's operations. The Decision-making Processes in a Data-driven Culture The decision making process involves three buckets: data driven culture, analytical decision making, and predictive decision making. A data-driven culture focuses on controlled testing of experiments and measuring things rather than relying solely on intuition. This includes tracking demand for analytics use cases, managing cultural change, and ensuring data accessibility and democratization. Analytical decision making starts with analytical frameworks and tools, such as customer lifetime value frameworks and CLV calculations. It also involves decision-making process integration, ensuring checks are in place before recurring functions occur to ensure data analysis is involved. Performance tracking and feedback are essential for comparing individual decisions made with data to the overall function. Adam explains how and why analytical decision making is used, and how predictive decision making involves planning out budgets for next year, understanding macroeconomic impacts, weather, and operational and financial budgets. Predictive analytics can help manage various risks, such as customer numbers, macroeconomic impacts, and weather. Predictive data is used for strategic planning questions, forecasting sales, and risk assessment. He explains how infrastructure scalability involves capacity planning and management, disaster recovery, and business continuity. Analytics diagnostic guides can help organizations prioritize their future state and decide what they want to invest in. Consulting firms should consider the bigger picture strategic choices, such as whether they are a data-driven company or if it's not important to spend time and effort on data and analytics. Companies may also want to focus on specific examples of demand in the business that they don't know about today, which can help them make better decisions. Data Analytics: Tools and Infrastructure Adam talks about the various platforms that can be used, and how choosing a point along the continuum of low maturity, intuitive, data-driven, and algorithmic can help companies determine if they want to be more analytical or not. By understanding the needs and preferences of their clients and identifying areas for improvement, businesses can make informed decisions about their future state and investment in analytics. He talks about the importance of being able to integrate tools, scalability, fitting the needs of the business and customers, and the ability to customize the tools. Adam discusses the concept of a company's approach to building capabilities and whether they want to be an analytical firm or not, and which analytics will help the business. He suggests that companies should make strategic choices about centralized or distributed analytics functions, monetizing external data, and maintaining a high level of customer consent. He also suggests that companies should build these capabilities aggressively, gradually improving over time, and that companies should start with quick wins on important use cases and gradually build on more complex ones, such as marketing ROI models. For listeners interested in learning more about his practice, Adam recommends visiting braff.co, which offers resources such as a blog, an annual forecasting contest, and programming course. He also mentions that he has taught this content in graduate programs at Brown and NYU and has started teaching a corporate version of the analytics intensive course. Timestamps: 01:18 Setting up data analytics function in a company 07:02 Analytics Strategy and Measurement 12:52 Data management categories and sourcing 16:12 Data management, analytics, and AI in businesses 22:06 Managing and developing analytics talent 26:50 Data-driven decision making and analytics in business 29:13 Data-driven decision making and analytics tools 34:44 Data analytics maturity and strategic prioritization 40:17 Building a data analytics function for a business Links: Website: https://braff.co Unleashed is produced by Umbrex, which has a mission of connecting independent management consultants with one another, creating opportunities for members to meet, build relationships, and share lessons learned. Learn more at www.umbrex.com.