Recently, large companies such as PepsiCo and Nestle have invested in implementing digital innovation and data analytics through technologies such as predictive and prescriptive analytics, artificial intelligence (AI) and machine learning.

These investments have led to deep insights into the ability to run their businesses in a more efficient and precise manner in the following ways:

  • Gain a better understanding of customer behavior and the path to purchase
  • Appraise the impact of new product innovations more easily
  • Assess sales in real time and more quickly pivot a sales strategy
  • Manage inventory and supply chain more effectively through predictive analytics
  • Reduce poor performing SKUs and products to focus on meaningful and profitable items

If a company does not utilize data analytics in its operations and finance functions, it is missing a huge opportunity.

This blog will break down some questions to consider when adopting a data-driven mindset.


There are many ways to collect data about a business operation, and most companies already have a few data sources available. When making larger investment decisions, here are a few points to make data useful and actionable:

  • What data is available from the general ledger, customer relationships and sales software? Does this paint a complete picture of operations?
  • When is a service line, product, SKU or geography considered optimally profitable? Are these distinctions based on historical records, industry predictions or real-time projections?
  • Consider the customer experience. How are trends or customer concentrations identified and utilized to enhance innovation, assess product reviews, calculate customer acquisition costs and enhance lifetime customer value?
  • What key performance indicators (KPIs) would be most beneficial for tracking progress, and what data would be needed to track those KPIs?

If none of the above questions have been considered yet, an easy starting point is determining any gaps in visibility. Are there any bottlenecks in operations or finance that are causing delays in manufacturing or payment processing? If the answer is yes or unclear, those should be the focus.


Collecting data is one thing, but leveraging that data is another. Many companies in the food space are using their business data to determine patterns, predict trends and respond to market changes before they occur.

One example could be tracking the expiration date by product to ensure perishable items are turning over inline with expiration to better track inventory management. Another example is tracking the temperature of perishable product in trucks and receiving alerts if the temperature exceeds a specific threshold.

Predictive analytics is utilized to forecast what the data is expected to look like in the future, while prescriptive analytics recommends or ranks which plan of attack should be conducted. For example, predictive analytics can be utilized to assess previous customer preferences and actions to recommend a new product or determine the likelihood that customers will purchase this product. Prescriptive analytics can then be utilized to rank different scenarios in a go-to-market approach.

AI and machine learning are tools in which a machine can learn to read the data and provide important data points to inform KPIs. AI and machine learning can sift through a tremendous amount of data quickly to allow for real-time insights. For example, machine learning through Microsoft Azure can be conducted for sentiment analysis to sift through the text of customer reviews and rank each review by positive, neutral or negative. This can then be further analyzed to assess if the product is getting better or worse reviews over time.


Data extraction software can be utilized in conjunction with data visualization and business intelligence software to present data and results in new, easy-to-digest formats. This can be built through a platform such as Microsoft PowerBI.

These graphs can provide a greater understanding and increased perception of operations and can be utilized to think about the business with an alternative mindset, since the data may reveal information in an unexpected manner, both positive and negative.

Presenting relevant data in an easy-to-grasp form also helps by gaining buy-in from stakeholders.

Overall, data analytics is imperative to consider and implement in any business operation. Without analytics, the company may put all of their eggs in the wrong basket.

Interested in learning more about this topic? Reach out to GHJ’s Food and Beverage team.

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Colin Nierenberg

Colin Nierenberg, CPA, has seven years of public accounting experience providing external and internal audit services to clients primarily in manufacturing and distribution in the for-profit sector and private foundations in the nonprofit sector. Colin provides clients with audit and consulting…Learn More