Moving House = Moving Data: Lessons Learned from Cloud Data Quality Methodology
In the past 30 years, my family and I have moved to a new home four times. And between each move—like most families—we accumulated lots of bits and pieces. While many of these items were used every day, we found that we had ended up keeping many items because we thought they might be useful someday: Some of these “valuable” items have included a VHS video player, an Apple Macintosh 128K, assorted children’s toys, and clothes that were in fashion 20 years ago! And with each move, the amount of space our family needs has increased—right along with the cost of moving our belongings.
So, why am I telling you this? For the first three moves, I paid house movers to transfer more than 30 large boxes of stuff—even though in many instances we had forgotten what was in the boxes, hadn’t checked to see if it would fit, whether it worked, or if it would be suitable for the new house.
This got me thinking about how there must be a better way of moving from one location to another. It was when I was updating the Cloud Data Quality Methodology at work, that I realized that this approach could also work for moving to a new house.
Let me explain further: The Informatica Cloud Data Quality Methodology consists of four key stages: Discover, Define Rules, Apply Rules, and Monitor. The table below illustrates a quick comparison between how this approach applies to moving to a new house and how it also works when you’re talking about moving data.
||Moving to A New House
||Cloud Data Migration
||Make a complete inventory of your belongings? Are they in good condition? Will they fit in new location?
||Understanding the current state of data, profiling it for quality or other problems, discovering sensitive data, and determining data relationships
|Agree on the on how to decide what to keep, donate, or discard?
||Defining cleansing and standardization rules that ensure relevant data is parsed out to populate the target application fields
|Once you decide on the rules, make sure
all family members use them to quickly
and easily decide what is worth moving what should be discarded, or any items
that need to be altered
|Integrate the defined rules into your data quality processes to quickly and easily remediate quality and format issues across all sources
||After you have unloaded your valuables, you should check for damage. To avoid creating clutter in your new home, make sure that everyone in the household
follows the agreed-upon rules. (After all, you never know when you will move
|To maintain data quality, you need to continuously monitor and report on data quality, against all targets and across all sources
Extending the above comparison a little more, there is little value in making up the rules as you go along, or to apply rules inconsistently. Then too, many organizations attempt to tackle data quality by implementing tactical solutions to improve quality within a single application or a single business process: While this approach may mitigate the problem for part of the organization in the short term, such limited initiatives generally fail to achieve long-term data quality improvement on a broad scale. To truly solve the data quality issue for good, you’lll need an enterprise-wide approach that:
- Involves more people in the process
- Has a clear understanding of the negative impact of poor data
- Extends to all data domains
- Applies standard data quality rules to all applications
- Is continually measured and monitored
Although moving to a new house can be one of the most stressful life events, you can remove most of the stress and make it into an enjoyable experience with good organization and a little planning. Similarly, using a tried and tested data quality methodology when migrating to a new cloud application can accelerate deployment, increase adoption, lower costs, and keep everybody happy.
If you want to see the Informatica Cloud Data Quality methodology in action, why not sign up for a free trial here?