Growers are awash in data. Making sense of and use of it can be tricky and require the right systems support and software partner. Ultimately growers must convert those mountains of data into metrics and comparisons that have meaning and can help with decision making.

Understanding what types of analytics there are can help tailor your individual analytics and data architecture to get the results you desire from your data program.

According to Gartner’s analytics maturity model, there are 4 categories of analytics.

Descriptive Analytics

Descriptive analytics describes what happened, in fact, they answer the key question “What happened”. They are historical in nature, looking back at your raw data, gathering, aggregating, and displaying key metrics about what your operation has done. These are also referred to as hindsight.

Descriptive analytics answer questions like:

• What were Sales last month?
• How much did inventory cost in May?
• How much did we produce in March?
• What was our year over year transportation cost changes?
• What are store inventory levels compared to last year at this time?

Most growers understand and use descriptive analytics the most. Some have their staff manually gather data and put the answers to the questions into Excel Spreadsheets. Other growers use multiple reports from their grower ERP software to synthesize manually the story. And others, who use more advanced analytic platforms like AGS Direct to Store (DTS) or Microsoft PowerBI have these values automatically available to them.

Diagnostic Analytics

Diagnostic analytics help answer the question of “Why did something happen”. Diagnostic analytics is typically a layer deeper than descriptive analytics and, based on the situation have some cause-and-effect impact on the descriptive analytics you are investigating.

For example, if your descriptive analytics regarding production shows that you were down year over year, the diagnostic analytics that may be needed to help answer the why could be your labor usage and availability, space utilization, customer plans, or availability of raw materials to name just a few. 

Typically, diagnostic analytics are connected either directly or implicitly to your descriptive analytics.

Example of building and connecting diagnostic analytics

Copyright Advance Grower Solutions

Predictive Analytics

Predictive Analytics tries to answer the question of “What will happen”.  Predictive analytics are also referred to as forward-looking analytics where future stats are predicted from knowns and assumptions or other causal data sources.

For growers, the simplest example of predictive analytics is calculating a ready date from production dates and plant grow times based on weather and geography assumptions.

More complex examples try to predict future sales volumes based on customer trends and other economic indicators.

Typically, predictive analytics requires both internal and external data. This is an area where the burgeoning AI and Machine Learning software space can help. This area of analytics is also a high focus of research and algorithmic work, building data sets and algorithms to predict outcomes.

In many cases, short term predictions can be accurate within some defined uncertainty threshold. The further out in time the predictions are the more uncertainty is added. Think of the difference between the weather forecast for tomorrow versus the weather forecast for next winter.

Prescriptive Analytics

Prescriptive Analytics tries to answer the question “How can we make it happen”. What do we need to measure and correlate to tailor future outcomes?  Gartner describes this as being on the optimization end of the analytic spectrum.

Making positive things happen is not just about analytics and data but coupling those with your staff and operational practices to make improvements in results happen.

In the grower industry, this is harder simply because of the ephemeral nature of our products and the seasonality aspects that don’t affect other businesses.

Gartner’s analytics maturity model

Source: Gartner

So What?

Having an understanding of the types of analytics and how they can help will give a grower an advantage as they improve the use of their data for better planning and results.

AGS has systems and personnel that that can help with this setup and transition for you. Contact us today for more information about our analytic capabilities and products.