How do you Define Quality Data

The beauty of data is not for its own sake, but to capture the customer’s insight or the business’s foresight from it.

Corporate IT is the steward of the business information system. And information System (IS) is "a system" that deals with Information Management: collecting, storing, and processing data and delivering information, knowledge, and digital products. Too many businesses are data rich but information poor. Or they spend so much time and energy collecting data for that "just in case" scenario. Information Management quality means quality data, quality management, and quality measurement. What are further perspectives about Data Quality?

By “quality data” – it means clean, organized, actionable data from which to extract relevant information and insight: Data quality does not end with managing the incorrect entry of information, but the logic of data has to be taken into account too. You can walk through all the various dimensions of data quality such as accuracy, consistency, clarity, etc, but business context is indeed a very important perspective. Data can be accurate, consistent, timely, but data can also be shared among many different business groups, it can be transformed, aggregated, derived for various business needs, each with possibly their own views on what the expected definition and quality of the data should be.

Data Quality doesn’t mean perfect data but means “good enough” data: Data cleaning and data management has a deep business purpose to turning data into information, the business side of making sense of the raw data, adding value and augmenting business systems. This is where the organizations that understand this true nature will really begin to see huge value gains. In short, Data Quality doesn't mean you pursue the perfect data, but the good enough data being transformed into useful information, business insight, and human wisdom.

Cleansing the data is often the most difficult and time-consuming part of data science: "Data" is scattered and needs cleansing and improvement. Data cleansing, transformation, and sorting are vital in the data world because it helps to put things in perspective for business to read between the lines with accuracy and clarity of information that is needed for making effective decisions. This can be a major challenge at times depending on the size of the data. Everybody talks about the tools, but nobody asked about the data cleaning/formatting/calculations processes you want to perform. What kind of data cleaning/formatting/calculations do you want to perform? The issue of data quality is much more complex than just algorithms for cleaning up data. One has to consider how error/uncertainty is going to propagate through your analysis models and how the output is fit for making decisions under the specific context of your application.

Quality data is like the Holy Grail, businesses all want to achieve it; but not sure if it’s very doable. Businesses operate in the real world, and the real world is muddy and chaotic. Organizations need tools that deal with muddy and chaotic data, not a focus on making the data adapt to somewhat weaker tools. All data, from wherever it comes, is legitimate and reflective of the systems that provide it. As such, the quality data reveals deep and essential truths about not only the business domain it covers but also about the systems that capture it. it, You will not be able to understand the performance of your company or measure it without good quality data. The beauty of data is not for its own sake, but to capture the customer’s insight or the business’s foresight from it.