Deloitte Touche’s recent annual survey on the state of analytics readiness at leading companies validates wide variations in analytics oversight. Accountablity in integrated analytics, KPIs and financial oversight is not where it needs to be to govern and manage risk.

According to Dr. Cindy Gordon, Founder and CEO of SalesChoice, a leading innovator in advanced analytics to manage risk on financial forecasts in sales operating practices: It makes sense that CFO’s and their financial teams own advanced analytics and AI methods to calculate KPIs or generate predictions, and to ensure the methods and practices are sound and are valid. With the increasing risks of data bias, privacy, and accelerating emphasis on transparent and trusted AI, versus Black Box AI, CFO’s and CEO’s must intensify CFO ownership and accountabilities.

CFO’s have for years been data driven and had responsibilities of financial practices. However with the acceleration of big data, and data being populated in diverse lines of business, and in particular high risk information being controlled in sales and marketing that holds confidential customer information, increased operational decision making is in play outside the purview of the majority of CFO’s. Risks of data bias are accelerating.

Analytics cuts across organizational boundaries, and becomes the gateway for CFOs to have more centralized control and leadership to answer questions like:

  • What is the pricing sweet spot for this product or solution, given the company’s historical purchasing patterns?
  • What is the realistic value and health of our sales pipeline and forecasting outlook to manage risk? How can you easily do scenario analysis on customer demand to manage financial and operational risks? How efficient is sales and marketing operations on financial KPIs and process operational excellence?



  • Define your Analytics Strategy – be crisp and clear on the critical KPIs that Finance needs centralized control and access to. Finance won’t necessarily own the data that flows through different business units, but integrated accountability for the integrity of all KPIs using advanced analytics can go a long-way to reduce risk, and ensure centralized accountabilities for KPI Governance Management. For example KPIs in sales and marketing, can be on pricing insights, customer churn risks, demand growth, forecasting KPIs, channel value, etc.
  • Delivering Operational Value with Analytics

One model we think works is having leads from different BDUs form an integrated operating council to plan for analytics operational integration with Finance, playing a leadership governance role to ensure methods, practices, KPIs, and data risks are inspected and validated to be operationally sound. Too often lines of business do not have the financial and risk skills to do the deeper probes and hence CFO’s are in a strong leadership position to deliver operational leadership and improved governance on advanced analytic practices.

Some of the key value areas for CFO’s to intensify their governance leadership in advanced analytics are:

  • Improve Competitive Advantage – Analytics is increasing as a competitive resource to underlie competitive business strategies
  • Improve Decision Making – Advanced integrated analytics give CFO’s the ability to make better decisions, and have early risk signals to plan alternative strategies
  • Increase Visibility into Customers – being able to predict customer churn and best price points to increase growth, reduce forecasting risks, or even identify new products and services sooner than your competitors, are all critical insights to manage operational growth and risk.



In the past, CFO’s were primarily focused on analyzing historical outcomes and now they must rapidly reskill and shift to looking forward, and increase their knowledge of predictive and prescriptive analytics, understand the differences between black box AI and transparent AI, as the data volume growth and velocity (or speed) of data growth is accelerating.

Already, the size of the digital universe is set to double every two years at least, a 50-fold growth from 2010 to 2020. Human- and machine-generated data is experiencing an overall 10x faster growth rate than traditional business data (machine data is increasing even more rapidly at 50x the growth rate). Every CEO and CFO, must attune to the fact that big data volume growth is accelerating 50X due to advanced methods in Artificial Intelligence, Internet of Things (IoT), and other smarter technologies collecting data in all directions.

Here are a few other data points to help put this in context more: Every minute, internet users share more than 2.5 million pieces of content on Facebook, tweet more than 300,000 times and send more than 204 million text messages. Robots, sensors and automated processes produce plenty of data too, and that’s not included in these texts and tweets. We should be particularly aware of the explosive growth projected for the Internet of Things (IoT) sector over the next half decade – it’s estimated there will be 28 billion IoT devices in the world by 2021.



At SalesChoice, we view the CFO being one of the most critical executives to advance leadership governance in centralizing an Analytics Center of Excellence and bringing the BDU operating KPI leaders into tighter checks and balances. Liabilities will accelerate as data risks intensify, few organizations have efficient and effective data management strategies, sufficiently designed in place. Recent estimates by EY advise only 14% of companies are well positioned in managing big data risk and even less have centralized governance practices on KPIs owned by CFO’s.

It’s time for new organizational structures and for evolving the role of CFO’s to be forward thinking and be experts in advanced analytics, predictive and prescriptive analytics.



SalesChoice, winner of 2018 Digital Transformation award for AI Disruption, is a predictive and prescriptive analytics company committed to transparent and trusted AI versus blackbox AI, enabling a competitive advantage on KPI Analytics relevant to 1.) Predictive Forecasting and Simulation Intelligence 2.) Predictive Pricing 3) Account and Opportunity Intelligence and 4.) Data Completeness to minimize risks.