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Conventional Enterprise Intelligence instruments require customers to drag information from numerous sources inside the group to create personalized dashboards for particular enterprise objectives. This may be complicated as information lives in a number of repositories, requiring information analysts to make use of connectors or write sophisticated SQL queries for information extraction. Subsequent, the info is aggregated into siloed workbooks, every representing completely different subsets of the general dataset. In the end, this info is remodeled into numerous dashboards and stories, tailor-made for consumption by enterprise customers.
This presents two challenges:
- First, it creates a bottleneck within the course of. Each time new info, like adjustments to metric definitions, must be included, enterprise customers are required to interact the info group. This leads to a time-consuming cycle of addressing quite a few ad-hoc requests from a number of stakeholders.
- Second, it may result in what some name “information chaos.” When the info group makes changes, whether or not including, eradicating, or updating info, it is finished individually for every workbook and report. The dearth of consistency in how enterprise logic is outlined undermines the reliability of our dashboards.
Looker successfully addresses these two challenges by offering a single supply of fact for information. This eliminates the necessity for information analysts to be the bottleneck for information entry, as everybody can entry the required information instantly from Looker. Moreover, Looker affords standardized definitions of enterprise metrics, eliminating the confusion that may come up when completely different individuals are utilizing completely different definitions of the identical metrics.
Underneath the hood
Find out how to get real-time insights with out transferring your information
To know Looker’s real-time information evaluation, it is important to grasp its interplay along with your information. Enterprise analysts typically discover enterprise intelligence complicated as a result of they view ETL(Extract-Rework-Load) as a time-consuming and error-prone course of. ETL is the method of transferring information from one supply to a different, whereas reworking it right into a format that can be utilized by the goal system.
With Looker’s in-database structure, enterprise analysts can question the uncooked information proper from the supply, versus the extra time consuming information loading strategies of conventional BI instruments. This additionally permits dashboards to be real-time and dependable.
Looker helps over 50 SQL-compliant database dialects and continues to increase its protection.
Looker’s semantic modeling layer: a single supply of fact for BI metrics
Knowledge groups inside startups steadily face challenges stemming from information inconsistencies. This arises from the absence of shared definitions, conflicting enterprise logics throughout completely different stakeholder groups and outdated information extracts. For instance, when groups don’t align on metrics reminiscent of each day lively customers or pipeline progress, there’s a poor information high quality concern. This results in inaccurate evaluation and reporting, which may adversely have an effect on decision-making.
Looker’s semantic modeling layer sits between uncooked information and the customers, simplifying complicated information for finish customers by provisioning a curated catalog, predefined enterprise metrics and built-in transformation. Consider this as a dictionary of types, offering a typical vocabulary for customers to work together with. By bringing ruled information instantly to finish customers in instruments they’re already aware of, this may democratize entry to trusted information throughout organizations.
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