Metrics data management and analysis are essential to any business. Success depends on selecting the right metrics. To define and categorize metrics, follow the process below:
1. Start with a vision statement for the corporate metrics. This will include several categories for your industry, products, management, employees, regulations, stockholders, customers, suppliers, etc.
2. Define specific, measurable, achievable, relevant, time-bound (SMART) goals to help build your initial list of metrics for each category. For example, if you know that installing smart meters can reduce your electricity use by 10% then a goal like “reduce electricity use for our manufacturing boilers in department 301 by 10% in 2017” satisfies all the SMART criteria.
3. It’s often helpful to seek specific advice from organizations and consultants based on your company goals and metric categories. There are several organizations for specific industries and subject matter, including the Sustainable Supply Chain Foundation (SSCF) for supply chain metrics, International Financial Reporting Standards (IFRS) for financial metrics, as well as the Global Reporting Initiative (GRI) for sustainability metrics.
Once a set of metrics has been identified, there are additional important steps to properly define the data structure for data management and reporting. Let’s suppose you want to use a GRI standard metric such as total water withdrawn by source. To the business user, the data field may simply appear as:
GRI may recommend that you associate other fields to your metric for ease of analyzing and reporting. For example, adding the GRI number G4-EN8, proper description, and unit of measure would make our data look like this:
|G4-EN08||3454||kl||Total water withdrawal by source|
Though there may be supporting fields that an organization requires, you might also want additional fields for both external and internal analysis and reporting purposes. The comprehensive supporting data around the metric value itself can be derived by performing a thorough use case analysis. A use case is defined as a set of business scenarios that when aggregated make up a complete business function. The use case analysis identifies supporting data attributes (fields) and the frequency in which the data is stored. Below are scenario examples:
- Metrics must be entered at the site level and rolled up for each plant per region.
- A site must enter a metric weekly for one product line and monthly for another. This scenario of conditionally partitioning data collection across different time periods identifies additional business rules and supporting fields such as frequency.
- Other scenarios may identify additional fields such as who entered the data, the valid metric limits, when to notify others, and more.
Having gone through the complete process, an example of a final data record would be as follows:
|GRI Number||Value||UOM||Metric||Country||Region||Plant||Entered by||Alert limit||Date Entered||Frequency|
|G4-EN08||3454||kl||Total water withdrawal by source||Brazil||South America||B1||Don Ahearn||5000||10/21/2016||Weekly|
Microsoft Excel® makes it easy for users to insert calculations, new fields, and test data. Most enterprise metric system solutions require another set of tasks to transform an Excel prototype to work in their system. The step to transform working spreadsheet prototypes can be avoided when Excel is simply coupled with a database that ensures proper data governance, management and collaboration. Though use of a traditional SQL database is one possible solution, most require database design and configuration skills to implement. By using xOverTime’s Excel-enabled cloud database, the Excel prototype is quickly made to be production grade, with nothing more than Excel-based skills.