Data is impacted by numerous processes that bring data into your data environment, most of which affect its quality to some extent. Some processes bring data into your environment, referred as the inflow, and some process operate on the data causing data issues. The fishbone below highlight the different causes for data to decay (in no set order).
Root Cause for Data Quality Issues
It is difficult to prioritize this list, although philosophically I can say that lack of governance will most definitely lead to bad data. At the same time, the list is not finite or complete. Organizational events like mergers & consolidations can also lead to bad data quality. The fins on the upper side are processes that bring data into your system, the inflow. The lower fins are internal processes that cause bad data to persist. Either of the fins can cause data corruptions. I have summarized each of the fin below, without bloating this post.
- Legacy Migration: Refers to data that is often migrated from a legacy system. In most cases the data structures and data models are inconsistent between the legacy architecture and the new architecture.
- System Migration: This is almost similar to the above, except that these are due system upgrades. As applications evolve, designs change. New fields get added, when no historical data exists for this field. Or god forbid, some fields are deprecated/removed which may lead to serious problems.
- Workarounds: This is typical of the business community and packaged applications (ERP/CRM et al). Custom fields are heavily used (often with no documentation), which later lead to some misinterpretations.
- Manual Data Entry: Mostly happens when systems collect data from users via a “free text” field. Common examples include Addresses, Phone Numbers etc. In the absence of standards/conventions, or lack of policies, data entry users would want to finish a transaction as quickly as possible rather than worry about the accuracy of the data. If the system is not self correcting, users will never understand that they are introducing bad data.
- Interfaces: These are the connectors between one system to another. For large enterprises, this is how data typically flows – Campaigns to Opportunity to Quote to Order to Manufacturing to Service. To compound the matters, each system is sold/supported by a different vendor with no accountability for data correction.
- Process Automation: This is different from the Interface issue discussed above. This is more about how a system process (within that system) is automated. As existing business processes are re-engineered (due to dynamic nature of the business), applications get out of sync or new data assumptions are made. If this is not relayed to the IT team that supports the system, there will be some data corruption.
- Time Decay: This is especially true for Master data (like customer), where the data was good at some point in time but has since been not updated. Consider your email address (specifically work emails), as customer contacts move from one organization to another their email changes with the move. The data you once had for this customer contact is no longer accurate.
- Data Quality Programs: The irony. Yes, sometimes the data quality programs/initiatives are themselves a cause for bad data. This is mostly because of wrong assumptions on business data and rules around the data. So data may be cleansed incorrectly, aggressive merges (in the case of Master Data Management), data purges etc.
- Lack of Ownership: Very few organizations have complete ownership of a system (a CRM is often shared by Sales and Marketing), they often share sections of the data. With shared ownership, comes conflicting business rules and priorities. Concepts like Data Quality Organizations or Data Stewards are new to most organization, which bring accountability to an enterprise.
- Lack of Governance: Data Governance is a vast discipline that is beyond the scope of this post. It is about arriving at a standard definitions the the common data, via meta data management. It is about analyzing, defining and base lining the current quality of the data; so some of the quality metrics can be monitored. Its about MDM and a lot more. Lack of governance, means the information management strategy is poorly executed leading to more data issues.
In summary, the reasons for bad data quality are many. Before we start looking at cleaning the data, it is prudent that we understand the root causes for bad data. Prioritize and strategize the cleanup activities; devise ongoing monitors to gauge the data and control the inflow of bad data.
