5 Steps to Data Quality - Blazent
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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. 5 Steps to IT Data Quality September 2015
- 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. 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. The Data Evolution Process Historicity Purification Relationship Analysis Identity Management Data Atomization 4
- 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. 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. 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. 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. 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. 5 Steps to Data Quality • For more, read the white paper 5 Steps to Data Quality (LINK TO COME) 10
- 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
Notes de l'éditeur
- Enterprises today are facing growing pressures in management of compliance, risk, security. In addition, enterprises are migrating to the cloud or data centers which increases the need for complete and accurate data to make sounds business decisions. --------------------------------------------------- As the IT world moves into the era of Big Data, the “3 V’s of Data” (volume, variety and velocity) have never been more important—or intimidating. But with Big Data (Data 2.0) a reality and The Internet of Things (Data 3.0) right around the corner, enterprises are realizing the need for a fourth V: Validation. Because if you can’t trust the quality and integrity of your data, the other 3 V’s are meaningless. Validating Your IT Data Through Data Evolution Achieving true ‘data validation’ has never been more critical—or difficult. Studies show that, at any moment, up to 40% of an enterprise’s data is missing, inaccurate or tucked away in silos that IT can’t access. Blazent validates your IT data—ALL of your data—by gathering and then ‘evolving’ it to the point that it is not only accurate but actionable. With the Blazent Data Intelligence Platform—employing our unique Data Evolution Process and powered by our Big Data Engine—you can reduce that 40% inaccuracy to zero, guaranteeing that your IT (and business) decisions are based on 100% accurate IT data.
- The realities facing today’s enterprise IT organizations outstrip the multitude of existing data tools, sources and silo’ed information that exist today. Inaccurate or incomplete data is at the root of many IT and enterprises problems today.
- The Blazent formula for IT data intelligence begins with the collection and identification of all types of IT data. We gather this data from all types of data sources—including purchasing and HR—then bring it into our Platform, where it then undergoes what we call the Data Evolution process. The result is not only 100% accurate IT data but data that can now be utilized to not only diagnose and describe current IT issues but predict and prevent them in the future.
- Blazent takes your data through our patented five-step Data Evolution process, transforming your IT data to strategic business asset.
- Data Atomization: Blazant analyzes each incoming data source and breaks it down to its most granular level for processing. We then take singular incoming fields around common items like Product Models, Operating Systems and Locations and break each field apart, enabling us to compare differing values across multiple data sources. Take a field like an operating system. In many systems this can be seen as a single string or as two strings – one with the manufacturer and one with the identity of the OS itself. But when Blazent looks at operating system, we look at it much more granularly, capturing the manufacturer, version, edition, architecture and service pack. By breaking everything down to its most granular level we set it up for the evolutionary processes to come.
- Identity Management: Blazent applies Master Data Management techniques to primary and secondary entity identifiers, allowing our algorithms to align entities across multiple data sources with disparate representations. In the case of a physical asset, that’s around an asset tag, serial number or host name. But there are a series of secondary identifiers that we use that are more important now as host names and IP addresses are being replicated across VPNs.
- Relationship Analysis: Blazent analyzes all forms of incoming data for representations of relationships between entities. These relationships are then stored as entities themselves and are maintained as changes occur within the environment.
- Purification: What is commonly called data cleansing and normalization begins in the identity management process step and is completed in the purification step of the Blazent Data Evolution Process. Blazent employs multiple patented algorithms in this purification step. We take all of the representations from all of the data sources that we capture and boil it down into a single record. The result is the most accurate and widest representation of what that particular entity is at that given moment. We produce a singular record for each entity by combining the most accurate elements from all data sources with knowledge of that entity. Element population is driven by rules-based algorithms, which take into consideration the quality and reliability of incoming data source types, source currency, and weightings based on data element types and source occurrences. The concept of a data neighborhood is applied in this step as we are able to process these different data sources as a “data neighborhood.” We can process all of this different types and sources of data at the same time by knowing all of the things they are related to in order to establish complete and accurate data quality.
- Historicity: A history of each entity, from each source, across time, including its mutations from the canonical flow, is maintained in the Hadoop file system within the Blazent Platform to form the foundation for machine learning sets to fuel predictive analytics and prescriptive modeling for enterprise customers. The bottom line: Blazent’s patented data evolution process renders the most complete and accurate enterprise data available on the market today.