In today’s world of enterprise business intelligence (BI), multiple platforms and tools are used to analyze and report on data. This can lead to a siloed approach to data management, resulting in inconsistencies and inefficiencies. However, SCIKIQ has been working to overcome this challenge by implementing a BI fabric approach that rationalizes multiple enterprise BI platforms for better reporting.
Transforming BI with Innovative Reporting and Data Visualization: SCIKIQ’s approach enhances reporting by providing a unified, efficient system for data management. By creating a common semantic layer and data catalog, data discrepancies are reduced and access to quality, approved data is improved. The incorporation of a virtualization engine consolidates data, further aiding swift, accurate reporting. The result is a simpler, more efficient, and trusted reporting process.
SCIKIQ’s approach to rationalizing multiple enterprise BI platforms with BI fabric is an exemplary approach that can lead to a single version of the truth and a single trusted source of data. By implementing a common semantic layer, virtualization or cubing engine, data catalog, and business metrics and metadata integration, enterprises can overcome the challenge of siloed data management and achieve greater consistency and efficiency in their BI efforts.
SCIKIQ got a mention in the Forrester report on how innovatively we are solving the issue
Check the Best Practice report by By Boris Evelson with Team.
Rationalize Multiple Enterprise Business intelligence Platforms With BI Fabric
Download the report here: https://www.forrester.com/report/rationalize-multiple-enterprise-bi-platforms-with-bi-fabric/RES179134
SCIKIQ’s approach starts by creating a common semantic layer for multiple Enterprise BI platforms. This semantic layer separates BI developers and users from the complexities of underlying physical database structures. By implementing a common semantic layer, each platform uses the same business glossary and metrics, leading to a single version of the truth and a single trusted source of data.
SCIKIQ also utilizes a virtualization or cubing engine as a common semantic layer. With this approach, all data for BI applications are in one physical or virtual place, even if it’s stored in multiple databases. This ensures that all BI platforms tap into common data sources that live within the virtualization/cubing layer.
In addition to a common semantic layer, SCIKIQ emphasizes the importance of a common data catalog. Data catalogs are a single place to catalog all data sources and maintain a registry of logical and semantic data models and schemas used by multiple enterprise BI platforms. Data catalogs are also a single place for data governance, with tagging of data sources for levels of data quality and approved use cases.
SCIKIQ’s approach also includes business metrics and metadata integration. While some enterprises may not be ready to invest in platforms like data virtualization or data catalogs, they can synchronize BI platforms via import/export using an exchange standard. However, there is an emerging trend to reverse-engineer business metrics created in tools like Tableau and Power BI into a united “metrics store.” SCIKIQ’s business data fabric is one such example, allowing for central management of these metrics going forward.
By taking a phased approach to BI (Business intelligence) fabric implementation, SCIKIQ has seen success in rationalizing multiple enterprise BI platforms. This approach includes starting with BI on BI to uncover and decommission BI shelfware, sunsetting legacy apps, and creating guidelines for use cases appropriate for each BI platform. SCIKIQ recommends that enterprises concurrently proceed with implementing Enterprise BI fabric architecture and platforms, starting with a common BI portal and eventually reaching for the stars with headless BI.
In other words, it is Next-Level BI: SCIKIQ Redefines Reporting and Visualization Standards. SCIKIQ’s approach starts by creating a common semantic layer for multiple BI platforms. This semantic layer separates BI developers and users from the complexities of underlying physical database structures. By implementing a common semantic layer, each platform uses the same business glossary and metrics, leading to a single version of the truth and a single trusted source of data.
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