Big data analytics took the spotlight last year as companies learned they could use the strategy to find those needles in the haystack that would help them make better decisions, at a faster rate, to increase profitability. However, they may just end up digging through piles of structured customer information and unstructured social network comments without ever having that "Eureka" moment if they don't have the necessary data quality tools.
Acquire the right implements
Jim Harris, data management enterprise thought leader, recently wrote an article for Pervasive, explaining that big data is different than previous business intelligence tactics and requires new tools that can perform multiple functions at once. As analysts look to get rid of the data heaps that aren't applicable and grab the content that resonates with their goals, they may need an all-purpose tool similar to a spork.
"Although it's occasionally a good idea to engage in a sporking free-for-all with reckless abandon, more often it's a better idea to start with some sense of what we're looking to find. This might allow us to filter out potential noise or irrelevant signal ahead of time," Harris writes.
Improve results, reduce errors
If businesses don't have the necessary authentication processes in place, they may not know if the information they are sorting through is even accurate or complete. This can translate to wasted time, resources and even result in damaging mistakes.
Businesses that are engaging in big data strategies and handling large volumes of data will find that they need to invest in data quality tools if they want to avoid accuracy issues and reduce risks, according to a blog post by infoTrellis. Perhaps more importantly, companies must recognize that this isn't a one-step process. Data quality verification must be ongoing and receive regular attention if it's going to be effective because useful information does expire. Customers move and change their names, while companies' departments shift and engage embark on new goals that require different information. If the data isn't assessed and cleansed, it might contain errors that make it untrustworthy.
Companies that implement better data management plans will have more valid information that enables them to earn additional revenue and insight, reports Destination CRM. A firm with strong data quality tends to have 90,000 usable records for every 100,000 stored in its database, 15,000 more than a company with average practices.
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