Every Monday morning, the sales VP for the west at a global consumer goods company opens her BI dashboard. It is a maze of charts, filters and drop-downs. The next 20 minutes are spent hunting for a simple answer: “Which product lines underperformed in her region last week vis-à-vis global trends, and why?”  

Despite the dashboard’s myriad widgets, the VP exports the data into Excel to knead further, pings an analyst and hopes for a response before the weekly sales management meeting.  

This story is not unique. Frontline managers to CXOs are overwhelmed by static, one-size-fits-all reports. Dashboard fatigue is common across enterprises, as they deliver a whole lot of data, but no real insights that prompt business decisions. 

A growing disconnect between data visualization tools and user expectations has sparked a need for a simpler human-centric interface to question the data and receive contextual, real-time answers.  

Natural Language Querying 

In today’s fast-changing world, speed to actionable insights could be a competitive advantage.  Every company has loads of data, but how fast is the BI environment that converts it into contextual business information for busy executives? Waiting hours or days for periodic reports or analyst support is not acceptable.  

This is where natural language querying can prove to be transformational. It bridges the gap between technical tools and everyday business language. A much wider range of users like operations managers, marketers, line resource managers and many more are empowered to ask nuanced questions to their data. They receive answers as clear narratives, with supporting charts that are easy to comprehend. 

No longer is a degree in data science required to be data literate. No training, no dependency on data teams and no delay. As business priorities and contexts evolve, users can drive their own data analysis in real time. BI, thus, is simplified and democratized, with access to insights available for a broader audience within organizations.  

Conversational BI vs. Traditional BI 

Some decades back, BI tools were built for analysts and not business users. Analysts would curate the reports for leadership reviews, creating an unavoidable dependence. Even when dashboards were offered to end-users with personalization features using filters and widgets, they expected them to learn and adapt to the tool. They were required to know exactly what data view they were looking for. Report consistency was favored over flexibility and data exploration in the hands of end-users was unheard of. 

Conversational BI has changed this approach. It lets users think in familiar terms and ask questions intuitively, without setting any pre-defined expectations of them.  They may ask “Which suppliers caused delays last month in APAC?” or “How is our customer churn trending among high-value accounts?” without bothering about date-ranges, filters, data definitions, best-fit charts and where the data lives.  

Conversational analytics prioritizes speed, clarity and user autonomy. Teams can spend less time figuring out how to get the data and shift focus to acting on the insights it unearths.  

A Semantic Foundation  

Even though the hallmark of conversational analytics is simplicity of use, delivering easy-to-understand and accurate answers from complex enterprise data is no simple task. When a question is asked in plain language, the system must instantly understand what is being asked, which data to query, how to compute the result and how best to explain it.  

This is made possible using a semantic layer. It acts as a bridge between human language and enterprise data structures. Commonly used business terms like “revenue”, “on-time delivery rate,” or “high-value accounts” are mapped by the semantic layer to the underlying data tables along with the processing logics to calculate the KPIs using joins, filters and calculations. Hence, when someone says “churn rate”, the system knows precisely how that’s defined and derived.  

As different users access the data through a common semantic layer, business term definitions are standardized across roles and functions. A uniform way of working out a metric like “revenue” by sales, marketing or finance teams ensures that their reports are consistent and accurate, and hence, are trustworthy inputs for critical business decisions. Another key function of the semantic layer is maintaining data privacy and access rules. It ensures data governance guidelines are uniformly implemented across departments and job roles, based on need.  

Different User Roles Benefit from Conversational BI 

Conversational BI transforms how different roles interact with data, tailoring to their individual needs.  

For business leaders, it provides concise, high-level insights that help them to drive strategy. Instead of exhaustive dashboards, summaries of key metrics highlighting performance shifts, anomalies and trends in plain language are given. An instant clarity without data noise and decision-ready insights that align with their priorities is what they need.  

Operations managers, marketers, and frontline sales teams need to fine-tune their business almost daily in response to dynamic situations. Using conversational analytics, they can dig deeper into their data, ask follow-up questions to explore shifts as they happen and pivot based on evolving markets. Key for them is retaining the context and flow even while they zoom in or out of their reports.  

Data analysts on the other hand, benefit from more time available for them to focus on complex modelling. The system takes care of repetitive requests, generates KPIs, applies runtime calculations and suggests best-fit visualizations. Analysts can now innovate with data pipes to surface insights that might otherwise remain buried. 

Cross-functional teams become more collaborative as they can now pause and resume discussions, pin and share insights. This conversational continuity without insight ambiguity goes a long way in enabling joint action in strategic business areas.  

All of this is powered by a system that intelligently adapts to the user’s intent. The semantic layer also powers massive-scale querying over billions of records with optimized pipelines, maintaining scalability and accuracy.  

No-Dashboard, Just On-Demand Insight  

The intention behind dashboards was to tell a story, surface the truth and guide a decision. However, somewhere along the way, the storytelling got lost in translation – becoming a quagmire of cryptic charts and widgets that required data literacy to read and understand.  

Conversational analytics is making data storytelling a reality now. Users can now use this natural language interface to have on-demand, dynamic conversations with data. They may ask questions as they arise, follow tangents, validate hunches and move from insight to action in a matter of moments.