From Excel to Advanced Analytics: Capturing Greater Value from Current Data

Excel to Advanced Analytics_ Capturing greater value from current data

The CFO role has evolved into becoming a trusted strategic business partner with a unique, cross-functional perspective that can connect the dots across the organization. Leveraging analytics is important in this new role, empowering the Finance organization to derive actionable insights to drive strategic decision-making in a world where organizations are under constant pressure to perform better, cheaper and faster than ever before.

The use of analytics to deliver greater tangible value is one of the most powerful tools a company has in its arsenal. Luckily, organizations already have much of the financial data they need to support better strategic decision-making, increase organization efficiencies and reduce operational costs – in some cases they just can’t see it. The lack of visibility into pre-existing data may be why Forrester found that 73% of enterprise data goes unused for analytics, with most companies lacking an articulated data analytics strategy or centralized data governance function. 

Many CFOs have recognized the need to take action and utilize data analytics to provide more meaningful business insights, but prior to initiating a data analytics program, CFO’s should understand that unlocking the full potential of data begins with defining an approach that has stakeholder buy-in across your holistic organization, establishes value-add cross-functional capabilities and aligns the analytics program to the organization’s business strategy. To begin we recommend the following:

  • Define the insights you want to generate to drive your analytics strategy.
  • Understand the sources, uses and types of data available throughout the organization, and collaborate cross-functionally to drive better, deeper operating insights.
  • Confirm data is readily accessible in your department and highlight information gaps.
  • Establish a sustainable analytics process that delivers actionable insights and supports your business strategy.

Deploying advanced analytics requires a longer term vision that includes robust processes for model validation, monitoring and continuous improvement. Effective programs also benefit from built-in flexibility that allows for model evolution with the needs of the organization.

predictive analytics

A defined strategy to support discovery and understanding

Before you start digging into the tremendous amount of financial data at your fingertips, it’s important to first define your goals so as not to become overwhelmed by the sheer volume of information available. Unfocused analytics projects won’t deliver the ROI expected from stakeholders, and an inadequate approach that relies on bad or incomplete data might end up costing the organization in the long run. IBM estimates the cost to the US economy resulting from poor financial data at approximately $3.1 trillion per year.

Once you have outlined what you hope to accomplish through analytics, you can begin identifying which data sources are relevant and available. Understanding your financial data and knowing what to look for helps narrow the scope of analytics projects and delivers faster returns versus attempting to predict issues without any foundation to build upon.

Key questions to understanding your data


  • Where does your data come from? (e.g. company results, benchmarking, 3rd party sources).
  • What systems are used?
  • Are there any existing extract, transform, load (ETL) processes for analysis, consolidation or otherwise?
  • Where is there human intervention?


  • How is your data used today?
  • Where will additional data be incorporated into existing systems and processes?
  • How will additional data be used in creating new systems and processes?


  • What are the components of the sources and uses of data?
  • How does it get from point A to Z?
  • What are the key values needed to execute analysis, and how are they constructed?
  • Is there human intervention?
  • Is it the result of an automatic calculation?
  • Can it be broken down into its original components?


  • How will the outputs drive business strategy?
  • What are the interim and end states for the data?
  • Will new reporting be executed? Current reporting adjusted?
  • Will new processes for analysis be implemented? Current processes adjusted?

Cross-functional business partner

While the prospect of developing analytics-driven insights is an exciting one, CFOs are advised to first define a data map across the organization to best leverage, interpret and enable effective data analytics. Without a defined, complete, and cohesive data map across the organization that includes a full understanding of the different sources and drivers of your data, the information you are analyzing may be incomplete, creating gaps in the strategic enablement you are trying to create across the broader organization. Data validation is an ongoing concern that requires continuous monitoring. As such, it is important to create supporting processes to properly scrub, scrutinize and analyze your organization’s data at regular intervals.

The most effective analytics strategies will capture data points throughout the entire lifecycle of an organization’s value proposition. Beyond the ERP and financial systems, CFOs may need to look to non-financial data such as that in CRM systems to derive valuable insights. This will require support from cross-functional teams for interpretation and analysis. 

Opportunities for valuable cross functional analysis and insight start with asking the right business questions for your organization. For example:

  • Can we improve the way we target our customers or approach particular geographies?
  • Are there opportunities to bundle purchases to optimize pricing?
  • Is there the ability to re-negotiate contracts and consolidate vendors?
  • Can we leverage rule-based triggers to improve risk management?
  • Are there customer order patterns we can anticipate to consolidate deliveries?

Enable analytics and derive insights

One of the biggest hurdles to any analytics program is data integrity, as analysts will encounter a wide variety of data that is unusable in its raw format. In those cases, it will need to be transformed so it can be digested and illuminated by analytics platforms through extract, transform and load (ETL) processes 

Depending on the current state of the organization, there are a number of ways to streamline these tasks and create an effective process which supports the analytics program and increases overall ROI:

  • Establish formal data governance processes to standardize data collection, translation and management.
  • Leverage ETL tools to transform data and optimize existing capabilities.
  • Automate and streamline data collection and cleansing efforts.
  • Employ the use of dashboards and reporting to analyze new vs. existing data outputs.
  • Enable analysts and teams with tools to enhance reporting and analytics functionality.

From there, Finance organizations should create KPIs and set benchmark targets to accurately gauge performance metrics. Creating real-time, dynamic dashboards will empower the Finance organization to collaborate on analytics projects and share insights on business developments as they emerge. 

As your analytics efforts ramp up, it can be helpful to create rule-based triggers to keep up with quickly evolving circumstances and deliver informed recommendations faster than ever. As Finance organizations become more efficient and savvy with analytics processes, they will be able to more quickly spot correlations between different financial drivers within the organization, and in turn, provide analytics-driven, actionable insights to capture value across the organization.

Build the foundation for analytics success

Enhanced analytics capabilities are an important component of any long term Finance transformation strategy. Before launching your analytics program, start with a robust analytics strategy. Position your program to deliver value beyond your one-off needs. Doing so will yield more powerful insights while developing stronger analytics muscle within the organization.

To learn more about how Finance transformation is fundamentally changing how CFOs operate, and what you can do to prepare, download our comprehensive Finance Transformation guide.

If you are interested in learning more about CFGI’s Finance Transformation services related to advanced analytics, please contact Andres Garzon, Partner (, 617-306-1888), Oscar Palacio, Managing Director (, 617-921-3469) or Robert Winslow, Managing Director (, 203-482-9764).


Key Contributors:
Kathryn Streeter, Senior Manager, Data Analytics SME (, 832-506-5279).
Igor Stelea, Senior Manager, Data Analytics SME (, 617-372-3360).

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