What are the consequences of GOOD data? If we can confidently say we have good data quality, our reports are spot on, our dashboards are telling us what we need to know, and we are making great decisions based on this perfect data. The result is an increased efficiency in decision making that leads to actions that improve the overall effectiveness of our programs and the overall efficiency of our assets. All of which have a positive impact on the bottom line.
But what if by chance, your data is not so perfect? How does this impact your reports, your dashboards and your decisions? Not to say anything about the efficiency of you people, who may be getting a little frustrated because they keep doing the same inefficient things when it comes to reporting.
There is a way you can stop wasting hours on inefficient work due to poor quality data.
We all want to get out of the “bad data” game and move towards what I’d like to call “digital fidelity”.
The question is: how do we make a case for and go about transforming our data?
Better data will make positive impacts across your maintenance program and business. Here are a few of the impacts you will experience what you improve your data:
- Improved and more accurate reports (data quality)
- Automated reports that do not require a complete review of the data (data confidence)
- More accurate dashboards
- Our collective time is more efficient across the board
- Our decisions are based on facts more than gut feelings
- Our actions are more aligned with our objectives
- Our assets are more efficient
- Our programs are more effective
Let’s talk about some actions you can take now to better maintain and sustain your data so that it is as close to perfect as possible.
6 Steps to Improve Data Quality
1. Conduct a Data Assessment
One of the first things you can do to improve your data quality is an assessment of what you have. There are some simple ways to do this using readily available tools like SQL, TOAD, Excel, etc. More than knowing what you have, you also want to understand why you have what you have. The data assessment should look at the processes around how the data is collected in the first place.
For example, I have talked with many people who have an asset onboarding process that is well defined, but there is no alignment between the data identified in the process and how it is captured/recorded in the EAMS or CMMS. This is why the installation, replacement and warranty dates are typically blank. Its critical to know what data you have, what data you think you should have, why you need it and finally, if your processes support the data required.
You can target a specific area where you feel you can have the biggest impact in the shortest amount of time to show a quick win. Planning and scheduling and asset criticality are good examples of these quick win areas. If you are in the process of upgrading or changing your EAMS/CMMS, I would strongly recommend you include time to do a full assessment of your data as part of the bigger effort. If you have a trusted advisor, start asking questions, or feel free to take a look at the following link that discusses our approach to a full EAM assessment.
2. Determine Well Defined Objectives around your Maintenance Data
You must have well defined objectives that are aligned to the overall corporate objectives and site/department objectives. If there is no alignment, data quality, accuracy and completeness will suffer. You can end up wasting time collecting data that is not needed or you might miss critical data that will take additional time to gather. Make sure all team members are on the same page with the objectives and that they are aligned with the maintenance processes.
3. Define and Document What Data You Need
Define what data elements you want to track for each type of record you have, i.e. assets, spare parts, work orders, etc. If we take assets as an example, document what data you want to capture for assets in general and specific data for each class of asset and what type of standards and attributes you will put in place. This gets tricky, but most companies have asset classes identified with a list of attributes for each class already in place, just not being used. If you have criticality on all assets, is it well defined and based on a matrix that makes sense? Based on your industry, similar assets can have very different criticality rankings. Criticality on assets can definitely help identify what work to plan and schedule first and what spare parts should be more readily available than others (note that planning and scheduling are two different things).
Make sure that your processes are documented and that your data definition is also well defined and documented. Value lists are great ways to ensure accuracy of the data and should be used as often as possible. If you do a good job with the assessment and the objectives, you will know what data you are missing and you will understand what you need which takes us to the next step.
4. Determine How You Will Gather/Collect the Data
This sounds simple, but based on what technologies you have in place, it is very important to document how data gets into your systems. When I was in college, we had a paper based work order system with all the instructions on what data to print (with a black ball point pen and in block letters) next to each field. It was a very simple system that was well defined. As for results, it took six months to get a response to the only work order I ever submitted. I wish I had a picture of it. The gist was that my desk light was not secure and did not work. The response – tell the light to get a job and seek counseling!
Over the years, I have been involved in many data enhancement efforts and in each case, it was imperative to define how the data will be gathered. Specific types of data can be loaded into your EAMS/CMMS manually or automatically via an external integration. Make sure that if any of these processes change, the documentation is kept up to date. You may also require technicians to enter data manually, via handhelds or by writing comments on the workorder. One of the best ways to ensure that data is entered properly and efficiently is with handheld technology.
5. Define How You Will Maintain the Data
If you have a dedicated data department focused on maintaining the data, this part is easier, but still requires effort on their side and needs to be aligned with your needs. If you are the data department, you will need to define how you will ensure the data is maintained going forward. Based on experience, the most common mistake is that after data is loaded into a system, it is forgotten. Establish metrics and KPIs that are aligned with your processes and objectives and will show the status of your overall data. This has the added benefit that will allow you to view overall performance of the plant, department, facility, site, or organization. Alignment to processes and objectives is a must. If performance measuring and management is something your organization needs more clarity on, learn more here or give us a shout.
6. Create a Culture of Continuous Improvement
Once you have a better handle on your data and a game plan to sustain the data, you are setting yourself up to transform to a culture of continuous improvement. How does this happen? Once your data is set and you have proper views – metrics, KPIs and dashboards – it becomes easier to look for new areas to improve. For example, if you have job plans in place with the appropriate data, it becomes easier to evaluate the performance of those job plans and adjust them if there is a variation between planned hours and actual hours. It also becomes easier to implement PM Optimization without any false starts due to bad data.
Understanding that we have bad data is the first step. If we focus on a targeted area of data or a subset of assets, we can apply any of the above steps to all other assets when we are ready. If we try to boil the ocean, we may not achieve the results we are shooting for.
What are some benefits we can expect to see? The following is a high-level list that comes to mind. There are many others that you may experience based on your specific objectives.
- Improve Asset Utilization and Performance
- Reduce Capital Costs
- Reduce Asset-related Operating Costs
- Increase Uptime
- Reduce Failures
- Improved efficiency – assets and resources
- Improved Safety
- Extend Asset Life
- Maximize Overall Asset Productivity
- Minimize Total Cost of Ownership
- Know whether it is more cost-effective to continue to maintain, overhaul or replace a failing asset.
The bottom line: Data Quality is worth the effort!
Read the next blog: EAM Reporting v. Performance Management Dashboards