Enterprises are rapidly adopting AI-assisted development and conversational analytics to enhance or replace traditional BI use cases. Regardless of the mode of consumption, the next waves of analytics will increasingly exist as input and output for AI. The linear software build, deploy, and maintain cycle will still exist. Advancements in AI is already extending the reach and speed of data-informed decisions.
Enterprises are making large moves to consolidate and simplify their existing Business Intelligence estates and Micrisoft is clearly an attractive ecosystem due to Azure, vertical integration with the productivity, and a gradually improving AI story.
BI Optimized for Build and Human Consumption
BI platforms still need to be maintained by technical experts, but the barrier and speed are dramatically lowered with AI. Similarly, conversational analytics is working and improving at an accelerating rate.
Tabular reporting and Power BI dashboards have always been easy to read but hard to interrogate. Descriptive statistics tells you what happened but rarely explains “why” performance is improving or declining. The BI we know has diminishing returns for many enterprises because too many layers of people, process, data, and technology are required.
For consumers of BI dashboards and reports, the cognitive load and data literacy needed to truly extract the value of a dashboard is capped. The promise of conversational analytics is crystal clear but the reality of governance and enterprise leaders are stuck with many reporting and data viz artifacts. .
What Self-Service Was Actually Solving For
Self-service BI has always been delivered as a solution to widen adoption of data-informed decisions and action. BI tech like Tableau and Power BI reduced the friction for labeling, transforming, and dashboarding data for descriptive statistics.
Self-service data exploration continues to deliver value, but the professionally built dashboard continues to draw speculation because of the turn time between BI builders and analysts and end users who can technically self service with AI.
Information consumers, however, have a finite number of actions that are hard to understand and rapidly changing due to business dynamics.
More decisions are data-influenced today than at any point in BI’s history, because adoption widened and tech keeps improving.
Descriptive, Predictive, Prescriptive
Anyone in the business of analytics has seen these terms strung together. LLM output can facilitate all 3 phases today. Enterprises that approach conversational analytics with the same ground-up BI-building blueprint are likely to run into scaling and maitnence problems once the excitement of a chatbot wears off.
Where the Opportunity Actually Sits
Knowledge retention, working memory, structured distribution, reconciling ambiguity, and other governance functions remain a challenge for conversational analytics.
At BIChart, built our business around indexing, analyzing and moving years of acquired knowledge artifacts. We started with Tableau. The first step, is to chronicle not only the technical makeup of your BI platform but all of the knowledge and ambiguity within.
We get to assess, compress, and deliver powerful knowledge artifacts through BIChart Tableau Assessment where the blueprint for how you measure and manage fascets of your business reside.
BI as Input to Your AI BI Agents
Our founding team’s work and R&D in the world of BI semantics and context curation pre-dates the BIChart product itself. When you run BIChart Assessment, we are not simply looking at Tableau dashboards and connections as blobs of metadata to simply migrate (though we do migration automation).
Every certified dataset, every calculated field, every measure is something someone may have deliberated in a formal or informal process. BIChart is helping enterprises ensure the right definition survives. The edge cases distorting the truth should be left behind or tagged for deprecation in the future.
Consolidation and Migration
We started BIChart to solve a specific, expensive problem: Consolidating and migrating BI tools; most often from Tableau into Power BI and Microsoft Fabric.
Analytics has always been a knowledge-seeking and knowledge-distribution activity. Migrations are already overwhelmingly complicated and time-consuming. Our analytics-first approach to migration is motion you need to establish when you move from traditional BI builds to conversational analytics.
BIChart Innovation
The teams that get the most out of AI in analytics will be the ones that treat their analytics artifacts with the same level of rigor as the semantic layer itself. Our team is avaialble to arm you with tools to unpack the value stored in Tableau for what comes next!
Setup a call with BIChart to add data driven decisions into your migration initaitive
