Business Intelligence leverages data to describe a business’s past and current state.
When you think about serving data, BI is the first application that comes to mind and the most widely used way to consume data. Every one in a company is consuming dashboards and they have become common practices. “If you can’t measure it, you can’t improve it” as the saying goes; well designed dashboards with accurate data allow business stakeholders to make sound decisions and can provide great competitive advantage.
It is of major importance to consider the full data ecosystem when it comes to Business Intelligence:
The semantic layer
As a company grows its data maturity, it will move from ad hoc data analysis to self-service analytics, allowing democratised data access to business users without needing IT to intervene. Although the idea is simple in theory, it’s tough to pull it off in practice. The main reason is that poor data quality, organisational silos, and a lack of adequate data skills often get in the way of allowing widespread use of analytics.
The semantic layer in dbt is a game changer in the sense that it allows to define a metric store next to the transformations and have all the downstream applications consume the same definition of a metric.
Open-sources versus Enterprise-grade
There are mainly two currents with large corporations going for enterprise-grade solutions and startups and SME going more and more with open-source solutions that offer similar functionalities as their closed-sources counterpart. At Astrafy it is in our culture to favor open-source tools for the many benefits that open-source offers:
- Faster time to market
- Freedom from lock-in
Those open-source technologies now always offer as well a fully-managed version for less techie teams.
Whatever the BI tool you are working with, Astrafy can support you as our data has experience with the following stack and can easily adapt to new technologies:
Garbage in, Garbage out
Having reliable dashboards that are updated in a seamless way and can be trusted by the different stakeholders is a difficult task as it requires many upstream activities to go right. The data engineering part with DataOps to automate your data pipelines need to be well-oiled to feed curated data in your datamarts. If something fails in those upstream stages, your BI dashboards are doomed to be corrupted.
BI, as a downstream application, relies totally on the different phases of the data ecosystem to be in place before getting proper insights.
- We take a holistic approach and do not jump on nice visuals that have poor foundations.
- Our expertise in the different facets of data engineering from data infrastructure, orchestration, SQL expertise, DataOps to Data Governance give us the confidence to build well integrated and resilient BI.
- We are solutions-oriented and love to get insights from the data we manipulate.