4 Types of Companies Should Address Data Quality Issues Immediately
February 13th, 2018 | Chirag Shivalker
To start with, lets answer two very basic but very important questions about data quality.
Question: What is data quality and why is it important?
Answer: Poor data quality is a problem that most of the organizations grapple with. Though corporate spending on data collection and analytics has increased significantly, confidence in the quality of data has decreased due to its quality issues.
Question: What are the characteristics of quality data?
Answer: Five data quality attributes are:
*Uniqueness is not included here, because if there is no uniqueness in the data – it is not data – it is dirty data.
We are in an era where CEOs, aware of the importance of data quality in business analytics; are worried that dirty data and questionable results will hamper their futuristic efforts in and around artificial intelligence and IoT. Improving data quality is important for every enterprise, it is mandatory that these four types of companies address the dirty data challenge with immediate effect:
1. Companies that want to put their data to work
It includes organizations who already have the data, and are in to selling or licensing of data, or the ones who are looking out to monetize data. The second segment in the same category consists of enterprises that want to digitize their operations or want to deploy analytics for insights from data visualization dashboards. Such organizations can certainly go ahead to pursue these objectives with help of data full of errors, inaccurate & incomplete data sets and surprisingly companies do. But the chances of success are as good as their data is. Remember data analytics is not for datasets full of ‘white noise’.
2. Companies sensitive towards cost
Retailers are the foremost in this particular segment, especially the ones which are competing with giants like Amazon.com. Then oil and gas companies that witness severe price fluctuations, and data quality in healthcare information systems that are required to do the best of their job at limited costs. These enterprises should focus more on cleansing bad data to reduce wastage, instead of in-discriminant layoffs to strengthen their operations.
What is healthcare data, what are the factors that contribute to poor data quality in a healthcare database, how quality of healthcare data is defined in terms of accuracy completeness and relevance, poor data quality in healthcare, healthcare data quality issues is what we will discuss in the upcoming articles.
3. Companies unsure of who is responsible for data quality
Most of the organizations confess about data quality, and claim it to be an IT territory. IT people too will admit the challenge with data quality but will say it is the business that needs to take care of it, and all these end in very unpleasant stasis result. It is time to put an end to passing the parcel game and make every employee involved responsible for the quality of business data. If the sales team is made responsible for data, the role surprisingly will be positioned in the finance team, and that individual is integrating the data from disparate systems with help of IT department.
So if everyone is involved in managing the data quality – everyone is responsible for it isn’t it? Once the responsibility is delegated to employees, mapping them with measurable goals will help your company to measure the amount of improvement in the overall data quality.
4. Companies making decisions using data they don’t even trust
Data-driven decisions, every single enterprise knows its importance. They are enthusiastic about it due to the enormous amount of data available publicly & internal data sources as well. Companies get overwhelmed on knowing how other organizations gained insights from that data; case studies and used cases about such activities usually get published in business publications and are discussed across domain and industry conferences around the world.
But the sad part is that companies do want to reap benefits of data driven decisions, but are not able to stop themselves from cutting corners when it comes to making investments for data quality initiative.
Advanced analytics and advanced analytic tools, accompanied by the maturity of data analytics experts, make the task to monitor, collect, cleanse and analyze humongous amount of data a mere convenience as compared to old days.
Big picture on data quality
By now organizations have understood well that one size fits all approach for maintaining accuracy and completeness of every type of data for every business will not help them succeed. With immense data influx and the increased focus and application of data analytics, it is becoming more than ever to tackle data quality issues head-on.
One thing that companies can do is to simply get started. With more prospects coming on-board and new markets being discovered data is always going to grow a continuous process in itself. So waiting for that right moment to tackle data quality issues would be sheer foolishness. Take out time to assess what data quality means to your company or enterprise, to create ripple-effect of improved customer service, better customer experience, high conversion ratio, and long lasting customer retention. Considering all these as returns on investment would not at all be wrong.