Big Data Exploration
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ESolvit can help organizations with the following:

  • » Understand your big data assets
  • » Gain faster time to insights into your business
  • » Lower cost and less reduce in project deployment by not moving, replicating or merging data
  • » Improve productivity due to greater precisions and accuracy in having single view to the end users
  • » Lower implementation costs – analyze data in sources systems
  • » Not all data need to be moved. Move the right data into deeper systems for analytics

Four value pillars for big data exploration for your business

  • 1. Leverage existing assets – eliminate data silos, leverage existing research and knowledge, Eliminate/retire unused systems, extract value from existing assets
  • 2. Improve productivity – reduce training costs, improve staff retention, improve collaboration, Capture tribal knowledge and eliminate redundant projects
  • 3. Increase Revenue – equip sales and service staff with current, accurate information. Increase up sell and cross sell, reduce sales cycle, increase customer lifetime value
  • 4. Reduce Risk and Improve Compliance – reduce time to monitor and comply. Push relevant regulatory updates /alerts, avoid penalties and maintain a single version of truth

ESolvit will help you explore and navigate your Big data assets:

Discover the data
1. Connect securely to all 3rd party repositories
2. Provide unified search and navigation interface – basic querying, what customers, what products, R&D etc


Assess the data
1. Identify the value of the data – what data needs to be moved for deeper analytics
2. Recognize the users of the data
3. Establish context of data usage


Collaborate on the data
1. Augment the data with user knowledge
2. Create personalized views of the data
3. Identify ongoing user and system integration points


Leverage the data
1. Build compelling application using all of your data LOB applications


Depending on the size of the dataset and the number of sources, ESolvit can execute this in approximately 8 weeks timeframes and in 3 phases:
Phase I: Connect to primary data sources, define metadata for faceted navigation and implement security.
Phase II: Access additional data sources, leverage information governance (ex: MDM), tune relevance and deliver single point of fusion of employees.
Phase III: Create custom views for targeted business functions and federate analytics.
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