As enterprises rationalize their path to conversational analytics it inevitably repeats traditional Business intelligence process, challenges and need for an additional context layer.
Context layer is used and defined many different ways in technology circles.
- Data semantic layers
- Business semantics & glossaries
- Ontologies
- Taxonomies
- Metrics
BI adoption has always remained a challenge becuase of the breakdown of semantics and context through the build and deployment process.
As BI builders, we understood the path to conversational intelligence requires extracting context from all types of analytics. Tableau is a gold mine for your context layer, which is mostly untapped. The noise to signal ratio is substantial. At BIChart we have transformed our Tableau Assessment into something much more than a migration planning tool.
The Most Important Context Missing is the Objective
Before lineage, definitions, and ontology, the context that matters most in BI is your goal / objective. What are you solving for? Every dashboard that survived more than a quarter exists because someone was trying to describe, predict, or prescribe a specific outcome.
That objective is rarely stated in the workbook. It is implied by everything around it.
- Measure someone pinned to the top left.
- Filters that quietly exclude nuanced segments.
- A parameter that lets a VP toggle between fiscal and calendar timelines.
- Three worksheets that all compute revenue slightly differently because three teams were solving for three different things.
That is context and it is easily reconciled by people in the know or a system that properly surfaces context. Regularly, technology, data and business professionals speak past each other seeking a “single version” or “single source of truth.” That requires a governance discipline that should be baked into your conversational analytics initiative.
Tableau Is a Gold Mine for a Context Layer
Data platforms like Databricks and Snowflake process Tableau and Power BI projects to help produce semantic models. That is not enough to deliver production grade conversational analytics. It’s also not quite enough to deploy a viable semantic layer for conversational analytics.
As long-time Tableau practitioners, we have always known that a mature Tableau environment holds more institutional knowledge and context more powerful than any data dictionary.
- LOD expressions declare grain.
- Filters and sets encode business rules.
- Dashboard meta data provides detailed context aligned to the consistent “where are we?”
These definitions are encoded and raw information that can add depth to your context layer. It’s already technically encoded on your BI platform and often treated as exhaust during your migration to Power BI and Fabric.
The Noise to Signal Problem in Tableau and Power BI
Your Tableau likely holds signals that can accelerate your context layer, but it’s also masked with a lot of noise. Self service analytics created a system of truth. Because governance was human-led and painfully manual. Quick and disposable analytics took priority and created a long trail of ambiguity behind. The age old multiple versions of truth problem have compounded without a way to explain or reconcile semantic disconnects.
BIChart partners with top Tableau partners that specialize in Analytics Governance to help.
BIChart Assessment Delivers a Strong Context Layer Solution
To migrate Tableau to Power BI and Microsoft Fabric at scale, the same questions need to be answered for extracting context as data, and we deliver with BIChart Assessment
- Which dashboards matter
- What each calculated field means
- Which calculations are the same metric under different names, and which only look the same.
- What grain, filters, and parameters each metric assumes.
- Which metrics feed which decisions, traced through the dashboards that consume them.
BIChart Migration assessment provides a simple rationalize and reconcile function for all meta data before converting to Power BI and Fabric automatically. Everything in your Tableau estate has a purpose beyond literal translation to Power BI and other tech platforms.
BIChart entered the Microsoft ecosystem to help large enterprises migrate at scale. We understand analytics you build today is the input for conversational analytics your business stakeholders want.
To deliver conversational analytics with a high degree of accuracy, the objective is to shorten the distance between the question and declarative output.
If you are planning conversational analytics initiative on top of Fabric, we can provide a foundation that helps with BIChart.
