Big Data
Tricentis targets the unexplored risks of data integrity failures
Thursday, July 6, 2017
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Richard Harris |
Automated software testing platform Tricentis Tosca announced to provide better features for data integrity in the next update.
Tricentis has announced a major expansion of its automated software testing platform, Tricentis Tosca. Tricentis Tosca now incorporates a new approach for testing two critical components of enterprise infrastructure: Business Intelligence (BI) and Data Warehouses (DW). Because data can be compromised when it is created, integrated, moved, or transformed, Tricentis Tosca delivers a full lifecycle approach to BI and DW testing - fostering data integrity.
Businesses trust their BI tools to drive strategy and process optimization decisions, but the power of those tools depends upon a complex infrastructure accurately collecting and transforming critical data sources. The slightest data integrity error can have catastrophic impacts that ripple throughout the organization. Not only can companies make decisions on faulty data, but they can run afoul of industry regulations - creating millions of dollars of potential exposure and damaging corporate reputation. Because Tricentis provides deep visibility into the most complex distributed and heterogeneous Business Intelligence and Data Warehousing environments, such business risks can be significantly diminished.
“We are on the verge of a data explosion which requires the ability to makes faster decisions with a broader array of data,” said Wolfgang Platz, Founder and CPO of Tricentis. “If you don’t have data integrity under control now, there will be severe impacts as the amount of data increases significantly. Exacerbating the problem, the prevailing method of protecting data integrity is primarily visual validation. This less-than-optimal approach to ensuring data integrity exposes the organization to significant data quality and compliance risks. “
Application data integrity issues often go undetected unless a domain expert or data analyst discovers a discrepancy in a report. At this late stage, however, it’s difficult and time-consuming to unravel the problem, find its source, and remediate the problem. Without a data testing strategy or critical infrastructure-wide visibility into risk areas (including the Business Intelligence and Data Warehousing layers), companies remain exposed to preventable risks.
- Identify data acquisition (ETL) errors
- Reduce test creation and maintenance time
- Expose defects earlier - when they’re faster and easier to resolve
- Eliminate delays from manual testing
- Comply with regulatory requirements (e.g., BCBS 239, Basel III, SOX, etc.)
- Reduce the “testing burden” on business users
Businesses trust their BI tools to drive strategy and process optimization decisions, but the power of those tools depends upon a complex infrastructure accurately collecting and transforming critical data sources. The slightest data integrity error can have catastrophic impacts that ripple throughout the organization. Not only can companies make decisions on faulty data, but they can run afoul of industry regulations - creating millions of dollars of potential exposure and damaging corporate reputation. Because Tricentis provides deep visibility into the most complex distributed and heterogeneous Business Intelligence and Data Warehousing environments, such business risks can be significantly diminished.
“We are on the verge of a data explosion which requires the ability to makes faster decisions with a broader array of data,” said Wolfgang Platz, Founder and CPO of Tricentis. “If you don’t have data integrity under control now, there will be severe impacts as the amount of data increases significantly. Exacerbating the problem, the prevailing method of protecting data integrity is primarily visual validation. This less-than-optimal approach to ensuring data integrity exposes the organization to significant data quality and compliance risks. “
Application data integrity issues often go undetected unless a domain expert or data analyst discovers a discrepancy in a report. At this late stage, however, it’s difficult and time-consuming to unravel the problem, find its source, and remediate the problem. Without a data testing strategy or critical infrastructure-wide visibility into risk areas (including the Business Intelligence and Data Warehousing layers), companies remain exposed to preventable risks.
Key features
- Identify data acquisition (ETL) errors
- Reduce test creation and maintenance time
- Expose defects earlier - when they’re faster and easier to resolve
- Eliminate delays from manual testing
- Comply with regulatory requirements (e.g., BCBS 239, Basel III, SOX, etc.)
- Reduce the “testing burden” on business users
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