By Priya Krishnan, Product Management, IBM Infosphere Master Data Management

So I have all my data in Hadoop, now what?

Data has always been a critical wealth asset to enterprises. With the surge in Big data, the ability to process this data and the potential to derive insights has led to companies beginning to invest heavily in this area. However, there is also a realization that making sense of the underlying data , associating that to what really drives revenue for my company is critical to the success in this Big Data world. With IBM’s Big Match Technology, you can now analyze your most critical data across a variety of data sources such as structured and unstructured by eliminating the fluff and getting to the data that matters to you the most.

Big Match is IBM’s industry-leading Probabilistic Matching Engine that runs natively on Hadoop. This is very important, as 50 to 75 percent of today’s big data use cases, or those envisioned for the near future, are seeking to address the customer perspective of unstructured data. These customer-centric use cases involve different data based on the particular industry perspective, including consumer, business, patient, member, suspect, prospect, provider and beneficiary to name a few.

The customer-oriented big data use cases
The customer-oriented big data use cases

The customer-oriented big data use cases may include:
Identifying customers in unstructured data (such as social media, text and call center data) and associating the customer data view to mastered customer data to deliver better service
Identifying prospects from purchased lists and third party data, and applying levels of confidence to these records based upon the intended use
Reporting for compliance or regulatory purposes when addressing legal or customer inquiries
Reducing bottlenecks in data parsing and integration (from transactional databases) by loading all data in the Hadoop environment where analysis and integration can be faster, and without constraints
Evaluating data warehouse data that may be constrained due to volume, response time or historical warehouse limitations
High-speed demographic matching for data sets containing 100s of millions (or billions) of records
Matching Customer profiles from Twitter with your own customers to proactively engage with them , capture new markets and create upsell opportunities

Significant efficiency can be gained when using Big Match, including:

Reuse of the PME matching algorithms already proven in traditional MDM (master data management) deployments
Gaining technical efficiencies since the IT staff will already be familiar with IBM’s MDM and PME
Readily configuring new data sources and thresholds, if appropriate, for data linking using the skills already incumbent in the IT staff
Knowledge transfer, which can become a continuous priority, with self-sufficiency as the ultimate goal
Support for unstructured and social data for proactive insights
Knowledge graphs that allow for significantly better visualization of connected entities

Are you interested in learning more about Big Match can help solve some of your critical customer challenges in Hadoop? Come and see us show a demo of how Big Match brings together data in the Hadoop word to create context in chaos!

To learn more about Big Match, join us at Strata + Hadoop World in San Jose IBM’s booth (booth #1115) and see the these technologies in action. Live demonstrations will be conducted at the IBM booth Thursday Feb 19th at 12:00 PM and Friday Feb 20th at 12:00 PM. #Strataconf

Please join the on-going conversation using #HadoopNext”

1 comment on"Big Match – Fun Name, Fantastic Solution"

  1. I arrived here by clicking the Developer Works link from the BM home page. I know what BM can do and this article repeats that, but where are the developer resources / community links / training / forums?

    This is an ad, not a resource.

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