Data should be the driving force of your organization, enabling you to optimize and manage your recycling, waste management or sustainability programs, efficiently, effectively and accurately, all in real time.

However, the degree to which this is possible depends on whether your company is data-driven, and if so, to what stage of data maturity.

Regardless how you may be managing your current recycling, waste management or recycling programs, you are collecting, processing and leverage data in your organization in some shape or form today.

It is important to assess your organization's relationship with data, as that's the first step in evaluating how to better leverage data--and not only any data but data that is of top quality, available in real-time and is 100% accurate--in order to elevate the progress and results you're achieving today.

Read on to understand various data strategies and how they might apply to your organization.

Data Strategies 

We have adapted Christopher Penn’s framework on the evolution of a data-driven company(1), applying it to the world of recycling and waste management. According to this methodology, there are five categories that an organization might find themselves in when it comes to how they value and use their data.

1. Data-Resistant

“We’ve always done it this way.” 

“Change comes with headaches.” 

“It’s working so far, isn’t it?”

2. Data-Curious

"Data-curious organizations are not yet reaping the benefits of their data collection; the analysis that helps to develop strategy is not yet being leveraged." 

Unlike data-resistant organizations, data-curious ones are aware that they have data and know that that data is valuable – but they remain unaware how to unlock that value. Usually, these organizations will only discover this value inadvertently, not through planning or prescription, and begin to collect it. This collection might happen through web or social media analytics, or other tools the organization might be using (EPR, CRM, financial/accounting tools).



Data-curious organizations are not yet reaping the benefits of their data collection; the analysis that helps to develop strategy is not yet being leveraged. In the waste and recycling industry, this might look like an organization that employs some sort of accounting software and maybe an inventory tool. They understand that these tools are keeping track of something that relates to their goals, but they are only using them to perform very specific, everyday tasks. Deeper insights are not yet being leveraged, and questions are not yet being answered. As with the data-resistant maturity level, the costs are lost opportunities and resource loss from inefficiency.

3. Data-aware

"Because there is value being gained here, many organizations stay data-aware for years."

At the next level, data curiosity gives way to data awareness: the value of data is starting to be extracted. Data-aware organizations focus on analysis, asking retrospective questions, such as:



• What happened last quarter? 
• What percentage of landfill diversion did we achieve last year? 
• What was last month’s tonnage pick-up goal, and did we achieve it?

These questions result in practical value and tactical decision-making; organizations can make determinations about what they want to continue or stop doing in order to improve performance areas.

Because there is value being gained here, many organizations stay data-aware for years. They can satisfy stakeholders that they are improving operations without rocking the boat. But the downside of this strategy is that it tends to come with another cost: data-aware organizations invest in tools to better glean insight from their data. These could be data storage and warehousing, analysis, or cloud tools. When the cost from all these tools because prohibitive, organizations might begin to wonder what they’re spending resources on, prompting them towards the next data maturity level.

4. Data-Savvy

Data-savvy organizations leverage data to answer “Why” questions.

Whereas data-aware organizations tend to ask “What” questions, data-savvy organizations leverage data to answer “Why” questions.

• Why did participant X’s performance dip last month?
• Why were we able to meet our landfill diversion goals?
• Why are we recycling more of Y and not Z?



For these organizations, they see the value of data beyond the tactical; they are starting to become strategic. Rather than merely describing what happened, they can provide the insight needed to explain why it did. This takes time and introspective investigation, necessitating organizations to stay at this level for some time. Once they are ready to turn these insights into actionable items, they are achieving a data-driven level of maturity.

5. Data-driven

​In a data-driven organization, data is captured at every level and utilized to improve 

all processes.

Now that organizations can describe the “what” and explain the “why,” they need to answer the next question: “What are you going to do about it? What’s next?” Data-driven organizations have in place the strategies to easily retrieve and harness their data; they are not spending endless hours and resources trying to make meaning of this information. They may say things like:

• Our manifest data shows certain geographies are not being reached; we will increase pick-ups in those locations by 40% next quarter 
• This report on our performance is ready for auditing, and our auditing time is down to less than a quarter of what it previously was
• We know why our volume of materials collected is not commiserate with our volume of materials recycled, and we have a plan in place to fix this issue



​In a data-driven organization, data is captured at every level and utilized to improve all processes. Every strategic decision is powered by high-quality, reliable data, and this is evident in meetings, governance structures, and performance metrics.