Data collection is the foundation of all evidence-based decision making. By unlocking the insights held in our data, we can move away from guesswork, assumptions, and intuition, and set our organisations up for sustainable change and long-term success.

But what data collection tools and options are available, and when should I use them? Let’s explore the top four methods leading asset intensive organisations use to bolster their business improvement initiatives.

1. Observational studies 

There are two primary types of observational studies used by asset owners to collect data to aid decision making – ‘tool time’ observations and ‘day in the life’ observations (DILO).

Tool time, also called time and motion studies, is a favourite of mine. It involves observing field workers and recording specific data points throughout a shift. It’s a trusted, powerful, and flexible option and helps you understand what’s really going on by getting close to the work as it’s happening. Tool time studies provide a way to look closely at a specific process or task and understand how things are being done. You can examine disruptions, inefficiencies, and delays, and identify worker frustrations and concerns in real time.

Tool time is a great solution if you’re looking to build a baseline and better understand your current performance, showcase before and after results, determine the root cause of a problem, and design and implement a solution. We have a number of more detailed tool time resources available if you’d like to take a deeper dive into this fascinating data collection method. Check out the following articles on our website:

DILO, on the other hand, targets leadership, management and support roles and is a great way to capture high-value tasks versus low-value tasks. As an example, you may want to understand what percentage of a person’s time is spent in the field ensuring safe work practices, versus rekeying maintenance data, or attending to non-urgent emails.

The power of benchmarking your data

While collecting tool time and DILO data is one thing, using this information to assess your results against industry averages and best practice is another. Working with business partners who have access to such valuable insights can significantly boost your performance and enhance productivity.

Minset has extensive experience in both tool time and DILO observations, particularly in asset-intensive industries. We’ve even developed our own app called MinObs, which helps us collect hundreds of data points for each role in a single shift and conduct multiple observations of the same role across different crews.

MinObs is not only a data collection tool; it also allows us to identify the often-overlooked challenges that workers face in their roles. This empowers employees by giving them a voice, leading to increased engagement and job satisfaction.

By leveraging solutions like MinObs, you can make well-informed decisions based on data and focus on improving your business without unnecessary delays.

2. ERP and business system data 

Accessing data held in your business systems and specialist supporting software solutions is a quick and cost-effective way to leverage existing resources. This data collection method is easier to use than many people realise. Contemporary systems have sophisticated graphical and reporting capabilities to help you analyse business trends and patterns. You may be surprised by the depth and breadth of information your organisation automatically records in its day-to-day operations. Your solution may be as simple as contacting the finance or IT team, a site ERP champion, or specialist software providers to discuss the data you’d like to explore.

Consider collecting ERP and system data to verify past performance’s effectiveness or estimate future trends and performance across operations, asset performance, purchasing, financial, safety, or compliance arenas.

Specialist systems can help you collect data on everything from work order management performance to shutdown effectiveness. Irrespective of the topic, your success depends on defining the information you need to support your improvement agenda.

3. Surveys 

Surveys are a versatile and popular data collection method that can give your workers a sense of anonymity and confidentiality, increasing participation rates. You can create your own paper-based or online surveys, or purchase survey data collected by third parties, including industry associations.

You can use surveys to collect quantitative data, such as ratings and scales, or subjective qualitative data, such as worker opinions and experiences. Surveys are a cost-effective way to ask all participants the same questions. You can survey workers on different shifts and locations at the same time. You can also pilot and test your approach with a small group of people before you roll out the final questionnaire to your complete list of participants.

4. Focus groups  

Focus groups gather in-depth insights on specific topics, such as changing a work method, analysing workflow processes, or brainstorming knowledge-based solutions.

Focus groups are semi-structured meetings and typically consist of a sample of workgroup participants. Often, a focus group is considered an ‘exploratory’ data collection method that takes place early in the research process. An experienced moderator leads each session, such as a facilitator with subject matter expertise. Focus groups can be expensive and more complex to manage than other data collection strategies. You also need a clear understanding of what you want to achieve to get the maximum value from this approach.

How do I know which data collection method to choose?

It’s essential to make sure you’re using the best data collection method for the job at hand. The solution must match the problem. Using the four strategies above as a guide, ask yourself the following questions:

  • What problem am I trying to solve? How do I know this is the right problem?
  • What specific information sources do I need to collect to help me address my problem?
  • What existing resources can I use to support my approach (people, systems, processes, forums etc)?
  • Do I need to collect quantitative data (numbers-based), qualitative data (subjective commentary), or a mix of both?
  • What will support my analysis – in-depth information, multiple perspectives, observations of real-world practices, trend information over months or years, or consolidating views into set responses or ratings?
  • What can I physically achieve in the time allocated?
  • Are there ethical considerations I need to address (e.g. guaranteeing confidentiality and anonymity)?
  • What methodologies or tools can I afford?
  • Should I conduct the data collection in-house or hire external experts?
  • What will ‘data collection success’ look like to me and my major stakeholders?
  • How much data will be required to validate my analysis? Addressing a higher risk problem will require more rigour (and therefore larger sample sizes and potentially more data collection sources).
  • Outside of the data, what else do I need to consider as part of my planning?

Next steps 

If you would like to find out more about data collection and its applications, please reach out to the Minset team.