“We BI” for Collaborative Intelligence

Author: | Category: Industry Trends, General BI | Tags: We BI, Me BI, collaboration, governance, agility, self-service | Published: 11/13/2015

“We BI” for Collaborative Intelligence

Recently, I talked to Virginia Backaitis as part of an article that appeared in CMSWire — Do Self-Service Analytics Wreak Havoc in the Enterprise?. This article presented viewpoints from a variety of business intelligence (BI) leaders, who spoke about their approaches to enterprise BI. Renowned consultant Wayne Eckerson also provided his perspective. Among the diverse vendors represented in the article were:

  • Mega vendors that historically have been more IT-centric.
  • Vendors that offer desktop and personal productivity tools for self-service BI, in a model of “Me BI.”
  • Pyramid Analytics, which offers a hybrid model in a single platform— governed, scalable BI, yet where users can self-serve, be agile, and collaborate for fast business benefits, in a model of “We BI” rather than “Me BI”.

With the desktop, personal productivity tools in “Me BI,” users can independently download these tools and take full control of the data without the IT department’s involvement or knowledge. For many users eager to have total self-sufficiency and to dive into the data sea immediately, this might seem optimal, since there’s no dependency on an IT department. Nonetheless, as I talked about in an earlier article — "We BI," Not "Me BI" — this individuality leads to data silos and fragmented data, since there’s no governance or common standard:

“The issue, however, is that this more individualistic approach to BI and analytics—Me BI—leads to disjointed, inconsistent, siloed data. It misses out on all the benefits that IT makes possible in an enterprise-ready BI solution that has broad reach across an organization: for instance, security, governance, scalability, and the ability to collaborate and share data that you can trust — We BI.”

As opposed to the “Me BI” approach, where data models sit on individuals’ desktop machines with possible security and access breaches, the “We BI” model is built upon the notion of collaborating and sharing governed, trusted content, sophisticated business logic, data models, and queries. There are centralized, shared content repositories to ensure content integrity, consistency, and transparency, as well as to prevent data silos. All processing of the data models that users create is maintained on a central server. This ensures security, redundancy, and backup, and fosters nimble collaboration.

In “We BI,” users still have an abundance of agility, but thanks to the IT department, they can be assured that the foundation underlying the data is secure, and that the data is consistent.

“We BI” generates exciting possibilities because there are more people in an organization who are “in the know.” A wide variety of users can analyze, monitor, and report on data. It’s far easier to have true collaborative intelligence, impactful insights, and far-reaching success when there aren’t numerous versions of reports, business logic, and data models scattered unknowingly throughout an organization. This, in turn, gives more accountability and ownership to all levels of an organization, with clear visibility into how the data is being used, and who’s making decisions with the data.

Indeed, the new direction will be for enterprises to invest in an integrated BI and analytics platform, to have more consistent views of cross-departmental, cross-application, and even cross-enterprise analytics.

In short, with a “We BI” model, enterprises get the best of both worlds: sophistication and agility that business and IT users want, with governance and security that the enterprise needs. BI directors can scale BI adoption and impact for truly collaborative intelligence.

Related resources

  1. Do Self-Service Analytics Wreak Havoc in the Enterprise?
  2. "We BI," Not "Me BI"
  3. Making Peace with Tableau (blog post from Wayne Eckerson)
  4. What the Microsoft and Pyramid Analytics' Strategic Alliance Means for Customers (Blog Post)
  5. Follow Pyramid Analytics on LinkedIn
  6. Follow @PyramidAnalytics on Twitter
  7. Subscribe to our blog's feed.

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