There's no doubt that when compiling massive clusters of data to analyze in an effort to gain greater wisdom about their industries, companies must emphasize accuracy. There's no substitute for superior data quality - if corporations use address management tools to ensure that their information is updated and verified, they can proceed with confidence toward decisions based on solid business intelligence.
Data quality isnt the only concern. According to Information Management, theres another equally important value for companies to consider when working with large volumes of information: data velocity.
Jim Harris of the OCDQ Blog writes that in addition to data scientists who are able to quickly process and find trends in information, its also important to have data philosophers, critical thinkers who are able to find true meaning in big data, finding lessons that companies can really act upon.
By gathering information on individuals and using software tools to verify its accuracy, firms of all kinds can glean valuable insights from big data. But perhaps nowhere is data quality more applicable than in healthcare, where it has the potential to save lives.
Mining data on individuals can have a profound effect for companies in all sectors. Whether in retail, marketing, finance or any one of countless other industries, it can be enormously beneficial to compile information on potential customers and use it to find optimized business solutions.
By collecting data on consumers and performing due diligence to verify its accuracy, companies across all sectors can find meaningful statistical trends to help them predict future activity. Perhaps the most notable area where data quality can make a difference is finance, where banks can collect information from social media and use it to find valuable insights about how people allocate their money.
In recent years, marketers have come to greatly appreciate the value of mining big data to collect information on their customers that will help shape future sales strategies. Their data collection processes help in another way, too - they can help prevent fraud. By monitoring consumers spending habits and looking for abnormalities in purchases, companies can easily detect when their customers financial information has been stolen and abused.