Data Quality

Here are the 17 data quality metrics.

1. Reliability: the completeness, relevance, accuracy, uniqueness, and consistency of the dataset for the intended purposes of use, and the ability to trace the data to a trustworthy source.
   • BSI client data reliability score is “Sufficiently Reliable”.
2. Timeliness: the degree to which the period between the time of creation of the real value and the time the dataset is available is appropriate.
   • December 2023: BSI’s timeliness score is 5 days, 8 hours, 56 minutes (submission to certification);
   • December 2023: BSI client’s timeliness score is 72 days, 17 hours, 23 minutes (last inspection to submission)
3. Validity: the extent to which data is present, in the correct format, within the accepted range, of the correct lookup type.
   • December 2023: Certification Validity, 100%
4. Compliance/Conformance: the degree to which data and composition of datasets is in accordance with laws, regulations, or standards.
5. Accuracy: the degree of correspondence between data values to real values.
6. Completeness: the extent to which data is not missing and is of sufficient breadth and depth for the task at hand; all required data values are present, all required records in the dataset are present, all required attributes in the dataset are present, all required data files are present, metadata are fully described.
   • BSI client record completeness score on submission: 99.8% (99.8% of the required data fields were complete)
   • BSI client file completeness score on submission: 71.7% (71.7% of the files submitted were complete)
7. Consistency: the degree to which data values of two sets of attributes within a record, within a data file, between data files, within a record at different points in time comply with a rule; the degree to which data values of two sets of attributes between records comply with a rule; the degree to which data values between two sets of attributes between datasets comply with a rule; the degree to which data values of a set of attributes of a dataset at different points in time comply with a rule.
8. Integrity: the extent to which data is Attributable, Legible, Contemporaneously recorded, Original or true copy, Accurate, with high levels of Completeness & Consistency.
9. Data Pedigree: the data pedigree is based on the following data pedigree criteria (DPC): Does the data represent what it purports to represent? Was the data produced through a consistent & defined process? Did we get samples from that process? Is there a record of who created the data? Is there a record of when it was created? Are there controls over who has access to the data? Has the data been modified or deleted? Do we have access to the original?
   • BSI client data pedigree score is 100%
10. Objectivity: the extent to which data values are created in unbiased, unprejudiced, and impartial manner.
    • BSI client data objectivity score is 95.9%
11. Uniqueness: the degree to which there are no duplicate records for the same entity or event in the same dataset.
    • BSI client data uniqueness score is 100%
12. Credibility: the degree to which data values are regarded as true and believable by data consumers.
13. Trustworthiness: the extent to which the data originate from trustworthy sources.
14. Believability: the extent to which data is regarded as true and credible.
15. Reputation: the extent to which data is highly regarded in terms of its source or content.
16. Accessibility: the ease with which data can be consulted or retrieved.
17. Ease of Operation: the extent to which data is easy to manipulate and apply to different tasks.

In BSI’s innovative home energy rating system, we know builders need quality data to make timely decisions.

It isn’t about the quantity…

We use a data quality scoring method (Procedure F) published by the American Society for Quality (ASQ), the global leader in Quality since 1946.

While amateurs turn to Indeed or other job-hunting sites to learn about quality management, we are members of ASQ, the ANSI partner for quality standards.

That’s one of the many differences between our professional home energy rating system and our competitor’s.

We know that builders need data that is consistent. That’s why our software only uses the EnergyPlus OS-ERI calculation engine.

HouseRater™ and BSI have built in more than 400 data validation and reasonability checks which couples advanced AI with human reviews to ensure our data is consistent.

As Neil Kruis has stated in more than one public presentation, “Someone with three rating software tools is never sure what the HERS® index is.”

He should know. Neil is the RESNET® Energy Modeling Director.

Software inconsistency has been a known problem for nearly a decade.

So has rater inconsistency.

We know we could do better.

And we have.

Our professional home energy rating system produces data that is useful.

Data with a pedigree that can be trusted.

As we move forward, we publish our data quality scores right here for the whole world to see.

Good, bad, or ugly.

Why?

Because we believe in transparency and integrity.

This is just one more BSI innovation that brings professional quality management to home energy rating systems.

Well, at least to ours.

if you are tired of what you have, go ahead and fill out this application.

Yes, we make it more difficult to get into our tribe.

We’re careful about who we do business with.

We are going to provide you a ton of our support to help you build a better business.

We don’t want to waste it on knuckleheads, chuckleheads, or clowns.

And you don’t really want to be associated with that crowd anyway, do you?

We want people who belong in the room.

Who want to build a better business.

Who stand for integrity, commitment, and transparency.

Who want to become more professional, and earn professional pay for the professional services they provide.

If that’s you, go ahead and fill out this application.

Go ahead.

I dare you.

PLEASE NOTE: Any use of “RESNET®” or other registered trademarks by Building Science Institute, Ltd. Co. does not indicate ownership, sponsorship, or endorsement by the registered trademark owners. Any use of registered trademarks falls under informational, editorial, or comparative use.