Do you understand your business processes?
Do your processes degrade your data?
How well do you understand your business processes?
Each individual process may be well documented, implemented and assured and even delivering great business performance, however, can these same processes be reducing your data quality?
Could any of the following issues be happening:-
- Data entered at one point that is then altered or even deleted (incorrectly) by a downstream process
- Confused areas of boundaries i.e. Responsibilities and Accountabilities
- Staff unclear on how the overall process works
- Downstream processes trying to find data that should have been recorded by upstream activities
- Downstream processes require intensive data corrections
- Lack of governance around system/data changes
On the surface, business processes may appear to be having the desired results in terms of business outputs, how about taking a look under the surface. Could your staff be spending too much time on data corrections, managing or reversing poor change decisions, trying to find data not recorded by ‘upstream’ activities etc. A failure to understand your organisation’s processes and how they interact with each other can lead to these issues which all can contribute to reduced quality of data. Also, there may be unnecessary double handling, inconsistent approaches or the dreaded assumption that some one else will.
An organisation may be considered to be successful in its field, but is it ‘world class’ and excelling in what it does?
Poor process understanding can lead to several failings, including:
- Corrected data not being fed back to the originating process resulting in repeated corrections each time the data is extracted – Wasted effort
- Over-writing good data with bad – Wasted effort, continuously cleansing degraded data
- Not recording data because staff don’t know what it is used for – failure of downstream activities and reduction in efficiency
- Multiple ways of recording similar or the same data – Inconsistent and/or conflicting information
- An ever-expanding data set without any ‘mastering’ or impact assessments on other processes – Can’t see the ‘wood for the trees’
- No clear data ownership – Someone else’s problem
- No definitions for field headings – “we can use that for our needs, it’s close enough” – Ambiguity, analytical constraints
These issues don’t just affect the quality of the data but can have a wider impact to organisational efficiency, effectiveness and profitability. If the same or similar tasks are being done by multiple teams then consolidating and combining the process around these tasks should result in a leaner organisation.
The answer to these challenges may be to dig a little deeper into ‘how the organisation works’ and understand the users view. Weed out the duplication and inefficiencies to improve efficiency and profitability.
In turn you could start to see improvements to the quality of your data sets. After all, the aim of an organisation is to be better than its competitors.
For help understanding your data challenges and developing suitable and pragmatic ways to improve the quality of data why not contact us?