5 Stages of Analytical Maturity for Manufacturers

Skye Reymond
By Skye Reymond | Data Scientist | Terbium Labs
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Evaluate your analytical capabilities to move your company forward

You know your company should be using data and analytics more effectively — and you want to. But you have no idea where to begin.

If that sounds like you, you’re not alone. One of the hardest challenges on the journey to becoming an analytical competitor is simply getting started. 

Why are the first steps to becoming a data-driven organization so difficult? The answer is actually pretty simple. Most organizations can’t figure out where to begin because they haven’t first assessed where they are. 

When I work with organizations hoping to create or refine their analytics strategy, I always start by identifying what they’ve done and where they want to go. One of my favorite frameworks for evaluating a company’s analytical maturity is from Thomas Davenport’s book “Competing on Analytics.” In it, Davenport lays out five stages to help companies self assess their current analytical capabilities.

In our work with manufacturing companies, we've identified some scenarios we often see in each stage. In this article, we’ll use Davenport’s framework and walk through some manufacturing-specific examples to help you identify where you are — and where you want to be. 

See also: Connecting Business Data to Business Goals

Stages of Analytical Maturity for Manufacturers

Analytically Impaired

The first stage of analytical maturity is what Davenport calls “Analytically Impaired.” Companies that are analytically impaired have historically placed little to no value on analytical capabilities. These businesses are largely paper-driven and may not have an Enterprise Resource Planning (ERP) system in place. Those that do have an ERP system may find it difficult to understand or extract the available data. 

Analytically impaired companies typically struggle to answer questions such as “Which ten products have the highest profit margin?” or “What is our warranty claim frequency?” At this stage, there are no standards for data quality or data collection processes. Decisions for company strategy are made solely on “gut feeling” or larger market trends. 

Next Steps

Companies that fall under this category should focus their efforts on improving data quality and standardizing their data collection processes. The goal is for the data to be accurate and appropriately structured. 


Your company’s key competitive differentiator is the best place to invest in analytics to drive the most value.
Localized Analytics

Stage two is known as “Localized Analytics.” A company in this category has data that is usable, but difficult to extract, inconsistent, or disconnected by information silos. Analytics are occasionally used in specific departments; however, the company lacks a cross-functional analytics strategy that is used to drive organization-wide decision making. 

Analytics are often reactive and come as the result of a specific executive request. Companies at this level can answer questions like “Who are my top 10 customers by region?” and “What are our monthly sales by salesperson?” Information is often obtained through spreadsheets.    

Next Steps

Companies at this stage should focus their efforts on using their data to set KPIs and create reports or dashboards across all departments. They should continue to focus on building efficient systems to collect consistent and accurate data.  

See also: Put your data to work 

Analytical Aspirations

Davenport’s third stage is “Analytical Aspirations.” At this level, the executive team and management have begun to understand the benefits of data-driven decision making. Data systems are integrated across the organization allowing for easier extraction of clean and complete datasets for analysis. Companies at this stage are making investments in infrastructure and talent to make the most of their collected data. 

These investments support the use of analytics to improve a distinctive capability of their company – something they do well to distinguish themselves from their competition. For example, a company with a repair shop might find their distinctive capability to be quality of service. For a logistics company, speed of delivery could be their competitive differentiator. Your company’s key competitive differentiator is the best place to invest in analytics to drive the most value.  

Companies at this stage can answer questions such as “What area of my business will benefit the most from an analytics strategy?” and “What kind of data will contribute to my company's distinctive capability?”. The analytics at this stage are isolated, but there is a roadmap to future automation.

Next Steps

Companies that fall under this category should focus on finding and refining their company’s distinctive capability. This will be the area of your business you believe to be your competitive differentiator. They should strategically plan automated analytics around this area of their business while continuing to focus on the quality of their data.

Analytical Companies

The next stage is becoming an “Analytical Company.” These organizations have developed an enterprise-wide analytics strategy that is a company priority. They have automated analytics and secured the talent required to make their insights actionable. These companies may be using more sophisticated data science techniques such as machine learning models or connecting their devices using IoT (Internet of Things). 

Organizations at this level have the data processes in place to use predictive modeling and can answer questions such as “What will the demand for Product A be next quarter?” and “What do our product reviews tell us about customer satisfaction?” At this stage, companies may be using third-party data to integrate additional market information into their models. Analytics across each division contribute to an overall strategic goal.

Next Steps

Companies at this stage should focus on ways to use their analytics to gain a competitive advantage. They should set strategic company goals around their analytics capabilities and find ways to use their data to provide value where their competitors cannot. 

See also: Digital Transformation in Manufacturing: 10 Best Practices

Analytical Competitor

The ultimate stage is “Analytical Competitor.” Companies at this level use data-driven strategies to get the most value possible from their business processes. Executives continually search for the latest technologies and talent to refine and grow their analytical capabilities. These companies use their business intelligence as a competitive differentiator, often utilizing predictive models and optimization techniques. 

At this stage, analytics is woven into the culture of an organization and is seen as one of the most important assets to their business. These organizations may have integrated analytics into their processes and products in a way that allows them to analyze and drive customer experiences in near-real time. 

Analytical competitors can answer questions such as “What will my customers ask for next?” and “How can I optimize my assembly line costs while meeting next month’s demand?” At this stage, the company is known for its analytical capabilities and is often on the leading edge of the best data science tools and technologies.  

Next Steps

Analytical competitors should continue to invest in their technology and talent. These companies will need to continually push the limits of their analytical capabilities in order to maintain an advantage over their competition. 

Get started

After identifying the current analytical capabilities of your company, the path to competing on analytics will become more clearly defined. So what are you waiting for? Mark Twain may have put it best: “The secret to getting ahead is getting started.”

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Looking to move to the next stage? Let us help!

Dive Deeper

Check out the Data as a Differentiator episode on our podcast, Data in Depth, for more about how data and analytics can serve as key competitive differentiators for manufacturing companies.

Topics: Analytics, Manufacturing Cloud

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