Digital transformation initiatives are challenging to complete in many organizations. When data issues block the path forward, progress stops.
Many companies are thriving in spite of their data issues. The continuing pressure for cost reductions and competition for capital, however, is increasing the priority on resolving data issues. They have escalated into significant impediments that increase costs and risks while reducing net income.
Data issues are easy to identify, and solving them will put your business back on track to realize the many, many benefits of digital transformation.
The data issues that engineering leadership can eliminate, or at least reduce, include the following.
Inconsistent Data
Inconsistencies can occur in reference, master and transaction data. For example:
- Multiple identifiers for key data such as vendor or product across IT systems.
- Component specifications are not used everywhere in project data.
- Variations exist on $1000, such as 1,000, 1000 USD, USD 1000, 1000.00 or one thousand dollars.
- Variations are found in units of measure abbreviations such as kg, Kg, kilogr, and KG.
- Numbers are not left zero-filled.
- Text is right justified as opposed to left justified.
- Multiple date formats are used, such as March 25, 2023; 2023-03-25 or 03/25/23.
- The letter O is used instead of zero.
- Multiple state abbreviations, such as CA, Cal, or Calif, are used.
- Incorrect conversions between EBCDIC and ASCII are evident.
Inaccurate Data
Data inaccuracies can be traced back to several factors, including human errors, data drift, and data decay. Examples of inaccurate data include:
- Designs with mixed units of measure.
- Drawings for buildings using erroneous or multiple elevation values.
- Nulls found where that’s supposed to be impossible.
- Incorrect formulas are used for calculated values.
- Latitude or longitude values are invalid or associated with a wrong or no datum.
Ambiguous Data
Ambiguous data occurs when the end-user isn’t sure what the available data means. Examples of ambiguous data include:
- Missing pieces of drawing revision history.
- Spelling errors.
- Data format issues such as dates with multiple possible meanings.
- No units of measure.
- Time with no time zone or AM/PM indication.
- Misleading column headings and names.
- Use of various languages.