The concept that, more than any other variable, has put the “big” in Big Data, has to be the notion of uncontexted, unstructured, or non-traditional data and the potential it represents. The term “non-traditional” when applied to data generally refers to data that does not easily lend itself to be captured in spreadsheets, tables, or relational databases. Some examples of this type of data include non-relational database data, such as documents, email, instant messaging(IM)/texting, and sensor data, and “signal” data like blogs and social media.
But non-traditional data typically can’t be captured with the same old tools or analyzed with the same old methods. Applying MDM to non-traditional data raises a different set of challenges than when dealing with traditional data. Although you will be asking some of the same questions as you would with traditional data, you may need to use a different approach or involve a completely new perspective to realize Big Data’s potential. Continue reading 5 Best Practices for Applying MDM to Non-Traditional Data
Nowadays, it’s hard to find an organization that isn’t using big data in some form or another. But while some organizations are capable of leveraging big data at the highest level of advanced analytics, others are struggling to evolve their big data initiatives into something more useful and competitive than basic reporting and proofs-of-concepts (POCs).
We at Knowledgent believe that advancing big data capabilities is critical for organizations to get the most value out of their data. After all, the higher your level of big data capability, the more effectively you are able to use data to your competitive advantage, and the greater your ROI on your big data investments.
Here are five tips to help you grow and improve your big data capability: Continue reading 5 Tips for Growing Your Big Data Capability
2013 was arguably a banner year for Big Data, but the reality was that, beneath the hype, many organizations struggled to understand the value of Big Data. Now that 2014 is underway, more organizations are learning how to realize the potential of Big Data. Continue reading Four Big Data Trends to Watch in 2014
More organizations are turning to Hadoop for their Big Data analytics needs, specifically storing and processing large datasets. Hadoop and its collection of components – HDFS, MapReduce, Pig, Hive, Zookeeper, etc. – provide a platform to store a large quantity of data and batch process this data for analysis. Continue reading Top 10 questions for choosing the best SQL-on-Hadoop solution