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5 Steps to Data Quality - Blazent

Accurate, reliable data is at the center of any IT strategy. Blazent delivers the highest IT data quality so critical to both IT and business decisions. Our new Data Intelligence Platform is powered by a unique Big Data Engine and employs our patented 5-Step Data Evolution process.

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5 Steps to Data Quality - Blazent

  1. 1. 5 Steps to IT Data Quality September 2015
  2. 2. The Truth About Enterprise IT Poor IT data quality is the primary reason for 40% of all initiatives failing to achieve their targeted benefits. Gartner 85% of Companies Fail at Creating a CMDB Due to Bad or Missing Data. Forbes 30% of physical servers sit vacant in data centers. Anthesis Group 40% of any enterprise’s IT data any moment is wrong or missing. Blazent Inaccurate baseline data and lack of transparency the primary reasons outsourcing relationships fail. Gartner 2
  3. 3. The Blazent Formula for IT Data Intelligence Identify, Access and Organize Process and Purify Analysis and Insight Predict, Prescribe and Optimize 1. 2. 3. 4. 3
  4. 4. The Data Evolution Process Historicity Purification Relationship Analysis Identity Management Data Atomization 4
  5. 5. The Data Evolution Process Analyzes each incoming data source and breaks it down to its most granular level for processing. Breaks down and compares differing field values across multiple data sources. 5 DATA ATOMIZATION
  6. 6. The Data Evolution Process Master Data Management techniques applied, allowing algorithms to align entities across multiple data sources with disparate representations. 6 IDENTITY MANAGEMENT
  7. 7. The Data Evolution Process Analyzes all forms of incoming data for representations of relationships between entities. Adds pivotal context to data as relationships are also stored as entities and maintained as changes occur within the environment. 7 RELATIONSHIP ANALYSIS
  8. 8. PURIFICATION The Data Evolution Process Singular record produced for each entity by combining the most accurate elements from all data sources that house knowledge of that entity. Applies advanced rules-based algorithms which considers: Data Source • Quality and Reliability Element Types and Source Occurrences • Currency and Weightings 8
  9. 9. HISTORICITY The Data Evolution Process History of each entity, from each source, across time, including its mutations from the canonical flow, is maintained forming the foundation for machine learning sets fueling: Predictive Analytics Prescriptive Modeling 9
  10. 10. 5 Steps to Data Quality • For more, read the white paper 5 Steps to Data Quality (LINK TO COME) 10
  11. 11. Blazent for IT Data Intelligence • Watch to learn about the Data Evolution Process • Request a demo of the Blazent solution • Follow us at @blazent 11 www.blazent.com

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  • HudsonJanie

    Nov. 13, 2015

Accurate, reliable data is at the center of any IT strategy. Blazent delivers the highest IT data quality so critical to both IT and business decisions. Our new Data Intelligence Platform is powered by a unique Big Data Engine and employs our patented 5-Step Data Evolution process.

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