Data Vs. Information, Defining Data Quality and Value
Questions about how to assess data quality and transform end-user data into insights are among the first steps when considering rolling out a digital transformation initiative. Do we already collect the data we need? You may wonder. There are three basic questions you can follow up with to determine your organization’s data preparedness:
Are you collecting data…
- of sufficient quality to inform decision-making?
- that helps you easily understand end-user experience and performance KPIs in your environment?
- at the source of digital experience: the endpoint?
If you answered “Yes!” to all three: congratulations! You’re either ready for or well on your way to experiencing the benefits of digital transformation. If not, continue reading to learn how to evaluate and select high-quality data that will deliver actionable information about your end-user computing environment.
How to Determine Data Quality
Anyone with a rudimentary knowledge of statistics knows that not all data is created equal. However, it can be challenging to define and identify what constitutes “high-quality data,” as requirements vary by intended use. For example, when monitoring key services, how real-time data is may be a more important factor in data quality than whether the dataset is complete.
You may be familiar with the “four Vs” of big data (velocity, variety, volume, and veracity), which define the parameters of a big-date initiative. “Veracity” speaks to data quality and the trustworthiness of the data source. Frequently, data quality is broken down further into characteristics to make assessment easier, including aforementioned timeliness and completeness along with accuracy, validity, consistency, and availability. While not exhaustive, these qualities are useful for evaluating the utility of data collected by a monitoring solution.
Read the entire article here, Data Vs. Information | Defining Data Quality and Value
Via the fine folks at Lakeside Software