Every year, poor data runs up to an average cost of more than $15 million. This will ultimately increase the complexity of data ecosystems leading to poor decision making. In this blogpost, Axel shares 4 key takeaways to find out if your organization has the proper solution in place to proactively validate the quality of your data.
IDC forecasts that the amount of data "created, captured, copied and consumed in the world" will continue to climb at a breakneck pace. The market-research firm estimates that the amount of data created over the next three years will be more than all the data created over the past 30 years, and the world will create more than three times the data over the next five years than it did in the previous five.
Nonetheless every year, poor data quality runs up to an average cost of more than $15 million, according to Gartner's Data Quality Market Survey of 2017. Apart from the immediate financial impact, poor Data Quality will ultimately increase the complexity of data ecosystems leading to poor decision making. That is why the emphasis on Data Quality will only increase. Gartner further predicts that by the end of 2022, 70% of organizations will rigorously track Data Quality on multiple levels to significantly reduce operational risks and costs.
These facts prompted us to look into the matter thoroughly and figure out how to enable organizations with the aim to improve Return On Information (ROI).
Four Critical Takeaways to avoid dealing with bad data
A recent Data Quality think tank session with AE consulting professionals revealed 4 key and essential takeaways from their real-life customer experiences:
- Prevent, don't just curate: Find out whether or not your organization has the proper
solution in place to proactively validate the quality of your data. Stay ahead of the game.
- Move on from legacy tools that can't keep pace with modern problems:
To proactively mitigate the bad data problem, organizations require modern data
management tools that provide visibility into the entire data lifecycle, from where the
data is created, to how it is presented and all the way back. A sound Data Quality
solution should imply inherent simplicity, self-training, metadata driven focus and
extensibility. In the world of microservices could there be such thing as Data Quality
As a Service?
- Data Quality is not an act, it’s a habit: “A fool with a tool is still a fool”, the saying goes.
For Data Quality the same applies it is not only about tools or technology, it is for a large part also a matter of having the proper guardrails in place, such as methodologies and best practices.
- Treat your data as critical assets: Despite the recognized importance of Data Quality in many to most organizations the IT or business project portfolio rarely or never includes sufficient budget for Data Quality initiatives. Be proactive, create a roadmap, separate quick-wins from larger scope initiatives but make sure sufficient funding is there.
Curious how we can help optimize your Data Quality ? Get in touch for a chat with one of our experts.