QUALITY Data is needed, the imperfect, but good enough data to transform into business insight and bring value for business growth and customer delight.
Big Data is both an opportunity and risk for organizations. Aimless gathering of data has no benefit, data analysis has to add value in business decisions. Data Quality becomes one of the significant challenges for businesses to overcome in order to abstract real insight based on quality data. So is there such thing as “bad data,” and is your Big Data good or bad?
Good data comes from how do you understand the business process. It is impossible to ask the data collection system to provide what data you want. You have to reorganize and clean the data before your named big data analytics. Enterprises do have big data. However, most of them do not collect the big data or they do not know how to collect big data. Problems need to be identify and resolve at the source. Take into consideration validation rules and applied appropriately. “Bad Data” exists due to lack of knowledge in business problem solving, clear business goals, mission performance tracking and resulted bad data fail to solve the problem by integrating into strategic decision analysis for problem solving and value creation.
One of the big problems is different mind and goals of business and IT. So they are working like together, but really separated. Wherever you go, you need to understand your data. It's on you to deal with it and extract the value. Your success is a reflection of your skills. Same data can be good or bad, it all depends on what question you want to answer. Business context is indeed a very important perspective. You can walk through all the various dimensions of data quality such as accuracy, consistency 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.
There is quality data, but there’s no “perfect” data: By “quality data,” it means clean, organized, actionable data from which to extract relevant information and insight. No matter how "clean" your data is, it suffers from the limitations of chaos theory on its accuracy and applicability to the "real world." You might have very clean data on your customer profiles, but necessarily that data is incomplete. The accuracy and compromise will continue to coexist across the span of information management. Hence, Big Data Quality efforts need to be defined more as profiling and standards versus cleansing. This is better aligned to how big data is managed and processed.
So QUALITY Data is needed, the imperfect, but good enough data to transform into business insight and bring value for business growth and customer delight. It is also important to leverage data quality and cost/benefit analysis, keep the end (business goals) in mind in order to achieve the value of analytics and build smart digital businesses.