The phenomenon of Big Data has changed the business world like never before. The most important part of this transformation is the strong emergence of analytics to support the shift in modern enterprises from a process-centric viewpoint to one that is more data-centric and data-driven. The data that surrounds the enterprise is being harnessed into information that informs, supports and drives decision making in a timely, repeatable manner.
This biannual KPMG global CFO survey report, Being the Best: Inside the Intelligent Finance Function, brings data & analytics (D&A) concepts into play at the outset, saying of today’s finance function leaders: “Their biggest challenges lie in creating the efficiencies needed to gather and process basic financial data and continue to deliver traditional finance outputs while at the same time redeploying their limited resources to enable higher-value business decision support activities.”
Certainly, the tools, techniques and processes that comprise the field of data & analytics are critical to improving standard, day-to-day data and transaction processing. However, it’s the implications of the second assertion – that leaders need to better deliver the “higher-value” business insights that drive strategic decision making – that make D&A even more central to the intelligent finance equation.
Initially, survey respondents saw D&A as critical to the implementation of “lean finance,” that is, optimising finance processes so the finance function can “minimise waste and other inefficiencies” to reduce costs and “boost speed, flexibility and quality.” Indeed, 41% of high-performing survey respondents considered D&A to be an “extremely important” enabler of lean finance.
Enhancing decision making and strategic value
While everyone is talking about Big Data and touting its value, the true data-driven enterprise is still in its early stages. By better leveraging the power of both the organisation’s structured data (contained in relational, searchable databases) and unstructured data (existing outside of such databases, e.g., emails) – as well as the almost unlimited masses of Big Data that lie outside the organisation – finance leaders can help transform the organisation’s ability to predict outcomes, plan around them and respond appropriately. Not only will this radically enhance corporate decision making, it will significantly increase the finance function’s ability to contribute strategic value to the organisation.
What does the data-driven enterprise look like?
For a given organisation, certain key capabilities will indicate that transformative data initiatives are enabled or under way. These include the ability to:
- Make sense of a broad range of structured and unstructured data and apply that knowledge to business planning, budgeting and forecasting and decision support
- Predict outcomes far more effectively than conventional forecasting techniques based on static historical financial reports
- Provide real-time insights into where the company should invest to close capability gaps and spot emerging opportunities
“Implementing business intelligence (BI) and data & analytics (D&A) means shifting from tactical data delivery to strategically filtering and extracting value from financial and operational data, then converting it to meaningful information that supports business decisions”
- Simulate responses to a wide range of events, from everyday market movements to extraordinary ‘black swan’ events
- Recognise, filter and extract value from financial and operational information to make better business decisions
- Identify competitive advantages to better service customers
- Make predictions – concerning potential fraud, for example – based on complex data patterns
- Create relevant and timely executive dashboards to measure success and drive strategy
Making sense of the data
Data often resides in silos throughout the organisation and is frequently redundant and conflicting. Employees don’t know how to access it or how to make sense of it, and because analytical processes aren’t embedded in platforms and infrastructure, D&A becomes an afterthought rather than a business driver.
Whether your organisation is in the midst of key enterprise transformations such as Big Data initiatives, an ERP implementation, a financial system upgrade or other data migration/conversion/migration initiatives, a BI & D&A solution can be integrated at any stage to help achieve increased operational efficiencies, improved understanding of customer behaviour and data risk mitigation.
To determine where they sit on the transformation continuum, data-ambitious organisations should ask themselves some key questions:
- Is our data growing faster than we can manage it?
- Do we know where our data is and how it’s being used?
- Is our data tied to our business processes and systems?
- Is our data siloed across the organisation, preventing us from getting a meaningful look at our business?
- Can we pinpoint areas to improve operational efficiencies?
- Are we using social media/public sentiment as part of our corporate analytics?
- Do people trust our data and believe it’s accurate?
- Are our data security measures exposing us to risk?
- Is our data giving us actionable information with respect to customer behaviour?
- Can we access information in a fast and timely fashion?
- Do we still privilege gut-instinct over business intelligence?
Accuracy in insight
By using a well-developed business intelligence (BI) framework, organisations can shift the focus away from just the efficient delivery of information (a technology-centric tactical approach) to accuracy in insight – a strategic approach that recognises, filters and extracts value from financial and operational information to make better business decisions.
This expands the focus of BI from basic reporting, scorecards and tools to a broader organisational capability. This capability allows organisations to capitalise on their information, apply relevant insight to respond to marketplace pressures and identify competitive advantages to better service their customers using data & analytics and predictive analytics.
Steps to move toward a data-driven enterprise
- Develop integrated data warehouse
Supported by a master data repository to ensure data is captured only once and used consistently across the reporting chain. - Make data readily available
Provides greater transparency and enables tighter controls and flexible reporting. - Understand limitations
Understand the current systems landscape, including what functionality resides where and any technical limitations to data sharing, to avoid issues down the road related to data traceability or lack of drill-down capability. - Harness tools
Provide more meaningful, forward-looking financial forecasts, predict and manage risk and reveal new market opportunities. - Conduct Business Intelligence & Data Analytics (BIDA) health-check
A periodic BIDA health-check can be used to ensure that people, processes and tools are aligned with the data strategy and roadmap. The health-check can be a three- to four-week exercise once or twice a year and can be used to bring senior leadership and other stakeholders together to reinforce the organisation’s data vision.
Importantly, any organisation currently engaged in or planning key enterprise projects such as Big Data initiatives, an ERP implementation, a financial system upgrade or other data migration/conversion initiatives should plan to embed and integrate business intelligence and data & analytics tools into the process. This is relatively easily done and less costly than retro-fitting tools into cumbersome legacy systems.
Next steps
If you find your organisation lags in data understanding and capability, it’s time to take remedial steps. It’s also important not to jump the gun. The use of data & analytics techniques is increasingly critical, but their effectiveness depends on a strong foundation of optimised finance systems, processes and people.
