BI platforms, or Business Intelligence tools, strive to introduce agility to analytics. Platforms such as Tableau, Qlik, and Looker do this by scaling and sharing data visualizations in a single tool. Others, such as Microsoft Power BI, are cloud-based apps and services that help organizations report on, visualize, and analyze data from a variety of sources. All these tools are meant to be decentralized and user-friendly wherein users generate and share clear and useful snapshots of what’s happening within their business. In addition, BI platform vendors each have their own respective ecosystems.
BI tools are typically used by data analysts within their respective business units; these are professionals providing analytical solutions to improve business performance. Stated another way, these professionals see data as a means to an answer.
BI platforms are used by companies to retrieve, analyze, & transform data into useful business insights. As mentioned earlier, common usage examples of BI tools include creating data visualizations, business dashboards, and periodical reporting. Technical features within a tool can facilitate this by automatically applying best practices in visual perception, presenting findings in a story-like sequence, and combining rich formatting and infographics. The tools typically pull from internal data that business produces.
Along with their additional agility, users of BI platforms tend to improve upon their data literacy. Data storytelling requires a more data-literate workforce to be able to present data in a way that offers insights faster and leads to desirable business actions. Since BI platforms make users more agile, they have greater access to data through new analytics projects.
This can also present a challenge. As data literacy improves, the questions that both BI platform users and their leadership ask are more sophisticated. Analytics projects are harnessing multiple data sources and menu-driven advanced analytics. This puts users in new territory where data sources are not only becoming non-relational but also forcing business analytics teams to decide where to best store and model all these data. The latter, concerning architecture and governance is something business users prefer to avoid and where the current capabilities in BI platforms fall short.
Neebo makes it easy for data analysts to find the right asset, trust it, build on it and collaborate with it. That covers any type of data, insights and algorithms, with full context and transparency, while continuing to ensure data governance. Not only does Neebo make BI platforms faster and more accessible, but Neebo also lets a data analyst use their BI platform of choice so there is no need to retrain analysts on a new tool and they are not constrained to one ecosystem.
Neebo is your analytics hub for any BI platform.
Check out this use case centered on building community to understand how Neebo is a cornerstone for analytics process improvement.