Diagnosing the state of your data
As mentioned above, for us to be able to define a fixed price model we need to know the state of your data and what the end target is. The end target can be simple to define in terms of a big-picture view, but the steps to get there, whether data cleansing, data standardisation, data enrichment, or data cataloguing, depend on where you are now.
The first step for any customer is to diagnose the state of your data. With a copy of your asset and material data exported from your ERP, EAM, or CMMS system(s), we run a health check to define the snapshot of the state of your data as it is now.
Our healthcheck process includes automated validation and diagnostics together with expert review to define completeness and identify errors, duplicates, and inconsistencies. We then benchmark the state of your data to our in-house metrics and identify and recommend what issues to address in order to give you the biggest impact.
The healthcheck is summarised in a report, which can be used as a basis for planning and detailed budgeting for a data improvement campaign. It can also be used as an independent review of your current state.
This first step typically costs between $7000 to $11000 (USD) per health check. Note that this figure depends on the number of data exports/systems/instances that need to be evaluated.
How your data affects Data Cleansing Costs
Data cleansing is focused on identifying errors and duplicates and removing them (find out more at Data Cleansing). The more data you have, the higher the chances you have errors and duplicates, so having less data is actually a positive influence on reducing the cost for data cleansing (but a negative cost influence for the enrichment and cataloguing costs).
All of this assumes that you have a consistent data structure and standards applied to your data. If not, then it is an additional variable to consider when checking if the information is correct, but one that often happens due to assets having different ages and with multiple instances of an ERP, EAM, or CMMS within a company. The best approach to take here is to first define the common standard and structure to apply and then use that as the basis for evaluation and cleansing.
We also recommend that you identify which fields are the most critical as a group or groups; this is something that we can help you identify and define. These are the fields that are mission critical / give the most value from being correct, and/or have the highest risk if incorrect. By focusing on these fields first, you can optimise your cleansing scope and costs
How can you influence Data Enrichment Costs?
Data Enrichment is focused on adding missing information to existing data sets that are partially complete (you can find more details here at Data Enrichment Services).
Defining the magnitude is simple and something you can do without support, as it is simply the number of fields in total that are currently empty for your existing records in your ERP, EAM or CMMS system.
From our experience, material/item masters in CMMS systems are typically only 20% to 30% complete, but you do not have to hit 100% to benefit from major improvement. As with data cleansing, it is worthwhile to define that fields are the most important generally, or by type of equipment. With these groups defined, it is then simple to focus on and measure the number of missing fields you have and to be more cost-effective in your data enrichment campaign.
Enrichment can also add a new field to existing data and populate the values, such as embedded CO2 footprint or UNSPSC identifiers and similar. These kinds of enrichment scopes are simple to define which is an effective approach for both cost and execution of the work.
What factors can you influence to optimise Data Standardisation costs?
Although the perfect situation is to standardise all your data so that it is harmonised with the same approach, it is often worth breaking up the challenge, which also helps break up the investment. By harmonising one facility at a time, the process can be tuned and optimised for each step while ending at the same final goal.
Data Standardisation gives major benefits to the effectiveness of the process being run in your ERP, EAM, and CMMS systems (as our Data Standardisation Services page shows). But, many organisations do not have a defined approach for data standards and structure or how to implement these in their systems and data. This is typically because their data has evolved over time in the same manner as their facilities.
If you are a company with multiple operations systems and even multiple instances of the same system, then the standards and structures of your data probably vary as well which can make it confusing on how to begin.
To address data standardisation in a cost-effective manner, we recommend breaking it down into steps:
Step 1: Identify what standards you have used as part of your data governance.
Step 2: Check your data to see how consistently your current standards have been applied (or not).
At this point, you have the overview of how standardisation has been applied to your data.
Step 3: Evaluate and identify the gaps in your current data governance standards. For example;
- Do your standards cover all current regulatory requirements?
- Do you want to align with industry initiatives to better engage with your suppliers?
- Should you utilise different/new standards more suited to your current needs and/or align with industry best practices?
Step 4: Create a company-specific reference data library using the standards and initiatives that are relevant to you as the basis.
Step 5: Map your current standards to your new reference data library to begin transforming and harmonising your data.
We are a strong believer that to deliver standardisation this cost effectively requires working with a partner who understands your industry and the standards relevant to it, such as Sharecat Data Services. It is also worth using a partner, like us, who has a range of standard reference data libraries that can be used as a starting point to streamline the process and who can guide and/or work with your teams to adjust the reference data library as needed.
How your current data affects Data Cataloguing Costs
Here’s some good news, the simple answer is it doesn’t.
Data cataloguing is focused on processing new information efficiently to establish data records in your CMMS, ERP, or EAM system. This means that your existing data isn’t relevant, but having data standardisation and governance framework in place is extremely important and will influence the efficiency and quality of data cataloguing. Unclear, inconsistent and generic governance rules and standards often mean worse data delivered and longer time to process.
Having a clear data standardisation approach aligned with the needs of your systems enables the effective use of automation to speed up the processes for cataloguing new information.
Other elements that can reduce the cost are:
- Proactively engage with your major suppliers, in particular those who you buy the most volume of items from.
- Ensure your requirements for information are clearly communicated to your supply chain and are included in your procurement contracts.
- Ensuring an efficient process and systems are in place for collecting information from your suppliers and contractors, ideally pre-checking and giving structure.
