Ahh the concept of data quality. So important, yet elusive to so many. It may seem like data is the only buzzword you hear these days. Big data, data governance, data strategy, and of course data quality are buzzwords flying around big time. But there’s a good reason for this, and it isn’t just hype. We are at the cusp of a new technological era, or as Forbes explains “we have reached that same point [as the beginning of the industrial revolution] of critical mass.” Forbes, of course, is referring to another buzzword with this statement, the “Internet of Things.” But just imagine how innundated your organization is right now with data. Now imagine compounding this when your devices are collecting environmental data on their own and communicating with each other. That’s a LOT of data.
The concept of data quality is not a new one. Having quality data gives you better insight in any context and thus the ability to execute anything with more precision. However as tecnological advances have allowed organizations to collect massive amounts of data (big data) it has become a focal point of operations for a reason. Yet an alarming number of organizations don’t implement a data quality initiative for the reasons below [according to SAS]:
- No business unit or department feels it is responsible for the problem.
- It requires cross-functional cooperation.
- It requires the organization to recognize that it has significant problems.
- It requires discipline.
- It requires an investment of financial and human resources.
- It is perceived to be extremely manpower-intensive.
- The return on investment is often difficult to quantify.
It may seem like the challenges outweigh the benefits. So we want to give you three reasons why data quality should be your top priority this year. Then you might think twice about not implementing a formal data quality strategy.
According to the Data Warehousing Insititue, poor data quality costs American organizations six hundred billion dollars annually. That’s enough to put a serious dent in the U.S. deficit! Bad data is the leading cause of the failure of IT projects and one of the driving factors behind customer attrition.
Poor quality data carries with it significant legal and reputational risk. Most governmental regulations now have sections addressing data quality. Data quality allows organizations to report to auditors and inspectors who will make judgements about our performance and governance. Here are just some of the ways that poor data quality can affect compliance:
- Inability to access full credit history leads to incorrect risk assessment
- Missing data leads to inaccurate credit risk
- Regulatory compliance violations
- Privacy violations
Having good data quality means having accurate and timely information to manage products and services from R&D through to the sale. It also means ensuring all employees are accountable. It also allows organizations to prioritise and ensure the best use of resources. Poor data quality can lead to the wrong insight and therefore the wrong decisions. If you are given the wrong information which which to make decisions with, your decisions will ultimately suffer. This in turn can affect a broad range of things from time to market to brand representation to product development.
You don’t want to be caught out having poor data quality in this day and age, because there is just too much to lose. Particularly when there are cost effective solutions out there that are ready to help you with your data quality issues. While there is no fully automated data quality/data governance solution out there at the moment, there are plenty of niche players ready to address your organization’s data need.
Take our product Observato for example. Observato is an audit log application that tracks and archives your data making easier to ensure data quality while keeping you compliant with several governmental regulations. Watch our 90 second explainer video more to find out more.
This blog post used excerpts from the following publications:
Image courtesy of blog.finetik.com