Big Data Testing – Big Data QA
Big data has big value, but it’s often a challenge for companies to find the link between the sheer volume of data they handle and the actionable insight they want. Consider this: While experts peg the big data market at more than $130 billion per year, recent research suggests that “bad data” is costing companies $3.1 trillion annually.
If that number seems high, it is. But here’s the hard truth: It’s not unreasonable. Dealing with data that doesn’t make sense, isn’t properly formatted and contains point-of-collection errors costs time and money — managers, data scientists, frontline staff and third-party collaborators have to account for, adjust, and correct this data (if possible). Every. Single. Day.
This is why you need better big data testing. A better way to profile, prepare and validate your data so you’re able to actively — and reliably — leverage this resource.
XBOSoft can help.
The 100 Percent Problem
Anything less than 100 percent isn’t good enough when it comes to data accuracy and validity. Why? Because you’re depending on this resource to inform both current initiatives and deliver ongoing big data business intelligence.
So, what’s wrong with your big data? Why isn’t it living up to expectations? Common causes of “bad data” include:
- Variable sourcing. Your data comes from everywhere. Emails, text messages, images, e-commerce transactions, social media sites and spreadsheets. The challenge? Format varies and most of this data is unstructured. If you can’t brid