Driving business success with data-driven strategies

The ability to make informed decisions is crucial for success as today’s business landscape rapidly evolves. Data plays an invaluable role in driving business transformation, and leveraging data analytics, AI and strategic technology partnerships can unlock new potential and enhance operational efficiency.

In a recent webinar, industry leaders Cindy Bratel, Dave DuVarney and Mike Hollifield from Baker Tilly and Andrew Condos from IFS uncovered how organizations can harness data to optimize processes, boost decision-making and gain a competitive edge in an increasingly data-centric world.

Bratel: Given so many organizations still have a way to go in their data and analytics journey, where would you suggest they start?

DuVarney: Everyday, organizations are trying to use their data to make the best decisions possible, which means having the right data programs in place is the difference between success and failure. For organizations still progressing in their data and analytics journey, the key to getting started is establishing a solid data strategy and governance framework . A focused business is an efficient one, and companies who create a shared language and understanding around their data will know what the key indicators are, what they mean and why they are important to business.

There are five key steps to build a stronger data strategy. A successful data strategy begins with discovery and prioritization. Catalog existing reports and data sources, focusing on key analytical themes rather than just migrating old reports. Prioritize themes like sales, revenue and profitability to align with business goals. Develop a model to measure and analyze these themes effectively. Assemble the right team with defined roles to support the program, ensuring continuous value addition. Implement a clear road map to guide the organization through the data strategy, from discovery to execution and beyond.

Data governance is also essential, with clear ownership and management of data quality, compliance and architecture. Organizations should adopt a "data first" approach, especially when migrating systems, which involves setting a clear vision, performing thorough data extraction and transformation and ensuring clean, deduplicated data is moved into new systems. Ultimately, building a robust data foundation early on will support analytics and drive long-term value.