Melissa Data Advocates Big Data Quality and Big Data Blend Practices for Authoritative Big Data Analytics
PentahoWorld Presentation Details Data Blending Practices to Ensure Meaningful Intelligence
Rancho Santa Margarita, CALIF – October 1, 2014 – Melissa Data, a leading provider of contact data quality and data enrichment solutions, today advocated data quality practices as critical to maintaining the validity and power of Big Data analytics. The firm will provide details in a PentahoWorld training presentation slated for October 10 at 8:00 a.m. as part of the conference’s Big Data Today breakout session. Melissa Data’s training session will examine the essential correlation between reliable data and authoritative analytics, including the Big Data imperative of standardizing and validating distinct customer records from aggregated, unstructured data.
Analytics is the leading use case for Big Data – it capitalizes on the ability to process huge datasets quickly and economically, yet Big Data simultaneously introduces data quality challenges. If garbage in the form of bad data is fed through an enterprise’s Big Data machine, the resulting analytics and insight are flawed, outcomes are compromised and business value negated. Up to 60 percent of IT leaders report a lack of accountability for data quality, with more than 50 percent doubting the overall validity of the data itself.
“Enterprise operations rely on understanding data relationships uncovered by Big Data. Data quality processes must be applied to ensure reliability of the distinct customer records that drive these analytics – fueling improved business intelligence or fraud prevention, understanding customer sentiment or even seeking out medical cures,” said Charles Gaddy, director of global sales and alliances, Melissa Data.
Because the unstructured data used for Big Data analytics comes from a variety of sources, its supporting data quality processes are more imperative than those required to handle small relational data. By matching duplicates from multiple data sources, data mangers can create a golden record – a single version of the truth. This information can then be blended with multi-sourced reference data, for instance adding precise lat/long coordinates to a customer address, or demographic data that enriches and heightens insight. “The result is real business intelligence based on validated data, optimized for Big Data analytics and based on a 360° view of the customer,” added Gaddy.
Melissa Data’s training session is titled “Using Reference Data Sources and Data Quality Practices with Big Data.” It will briefly explore data quality processes such as entity extraction used to identify customer and other structured data points from unstructured data, and Big Data blending with authoritative customer information.