Customer Data Integration (CDI):

Revenue growth through a consolidated customer view

Revenue growth through a consolidated customer view

Customer data is among an enterprise’s most valuable assets, often holding the key to improved sales, retention, and customer service.

Unfortunately, the systems used to capture customer information are often dedicated to single functions or geographies. This creates silos of data that are difficult to integrate cleanly with other sources and a large bottleneck for downstream analytics. Traditional ‘top-down’ approaches of standardizing data quickly become insufficient when dealing with the scale of data variety found in today’s enterprise.

Tamr’s Customer Data Integration solution radically simplifies an enterprise’s construction of a comprehensive, 360-degree view of its customers. Machine learning algorithms automatically unify up to 90% of internal and external customer data. When human intervention is necessary, Tamr generates questions for data experts, aggregates responses, and feeds them back into the system. RESTful APIs then deliver a consolidated view of customer information wherever your analysts need it: from spreadsheets to business intelligence platforms and next generation analytic tools.

Benefits:
  • Discover hidden opportunities to improve upsell / cross-sell, reduce churn, and identify key opinion leaders (KOL) via enhanced segmentation / targeting
  • Let machine learning automate 90% of data matching tasks
  • Leverage expertise in your company to guide data matching

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The systems used to capture customer information are often dedicated to single functions or geographies. This creates silos of data that are difficult to integrate cleanly with other sources and a large bottleneck for downstream analytics.

A new era of Big Data integration

A new era of Big Data integration

Data preparation involving the integration, cleansing and enrichment of data so that it is ready for analysis is a tedious and time-consuming process, and it can occupy up to 80% of an analytical development effort. Because it is vital to analytics, given the ‘garbage in garbage out’ maxim, data preparation can’t be circumvented. But it can be made easier, more efficient and indeed faster. In this webinar with Jason Stamper, Analyst from 451 Research, we explore new solutions to these problems.