2026 India Roadshow Recap: Why Enterprises Need Decision Infrastructure Before AI
Every room in Hyderabad, Mumbai, and Bengaluru had the same gap. It isn’t the data.
Decision infrastructure is the governed system of dashboards, decks, semantic models, business concepts, and AI agent sources that turns trusted data into trusted decisions, with every number traced to one agreed definition, a certified asset, and an accountable owner. It differs from data infrastructure by governing meaning and use instead of storage and movement. Data infrastructure asks whether the data is right. Decision infrastructure asks whether this is the number you decide on.
The gap between those two questions showed up in every city. Here is a composite of a story we heard in some form at all three stops. An analytics team in a Hyderabad global capability center supports a consumer goods HQ in Europe. They spent 14 months building a Databricks lakehouse with bronze, silver, and gold layers, owners, and quality checks. It is the cleanest data the company has ever had.
Then a regional VP asks the new AI assistant for Q2 net revenue in South Asia and gets one number. The Power BI board pack says another, about 3.5% lower. Both read from the same gold table. The gap lives one layer up: three versions of "net revenue" across two BI tools, one of which quietly excludes returns because a finance lead asked for it in 2021 and nobody wrote it down. The data team spends Monday proving the data is right. Nobody owns proving which number is.
Why does decision infrastructure matter in 2026?
We spent September with data leaders across India, including 150 of them over dinner at Tansen in Hyderabad and a theatre session with PepsiCo at Gartner Data & Analytics Summit India in Mumbai. One theme came up in every room. Everyone is rolling out AI and agentic workflows, and everyone is stuck on the same two things: governing the decision layer, and making sure what agents read from is reliable.
The investment pattern was just as consistent. Most enterprises we met are adopting lakehouse architectures, mostly for the medallion model and the data quality it promises. Databricks interest is climbing while legacy BI tools are being retired. Knowledge graphs came up as the answer to semantic alignment. Every one of those is data infrastructure. They improve what goes into the analytics estate, and almost none of them govern what comes out.
What comes out is not small. According to Datalogz analysis of nearly 1 million BI assets under management, analytics assets are growing 77% year over year, 79% of reports go unused, dataset reuse sits at 14%, and a third of reports still run on flat files, Excel, or CSVs. The Datalogz State of AI in BI 2026 report, based on interviews with 70+ data and analytics leaders, found that most enterprises run three or more BI tools, each with its own definitions, and that governance remains the primary blocker to enterprise AI deployment.
The bigger-warehouse trap
Picture a warehouse that takes in more goods than it ships. Aisles fill, forklifts slow down, and finding anything takes twice as long. You can change what comes in, change what goes out, or buy a bigger warehouse. What you cannot do is keep the same warehouse, change nothing, and get the value you started with.
Most of what we heard was option three. A lift-and-shift migration is a bigger warehouse. Consumption-based pricing with no governance is a bigger warehouse with a meter on the door. Teams moved off per-user licensing to stop paying for idle users, then found they were paying per query for idle reports. Under seat licensing, a dead dashboard is clutter. Under consumption pricing, a dead dashboard on a 15-minute refresh is a running bill. If 79% of reports go unused, the switch did not remove the waste. It started invoicing for it.
Platform vendors are happy to sell you either version of the bigger warehouse, because their revenue scales with your volume, not your clarity. Decision infrastructure is how you change what goes out.
Why India feels it first
The GCCs in Hyderabad and Bengaluru are where many global enterprises actually run their analytics estates. They absorb every broken refresh and every "why don't these numbers match" message from HQ, and they are the teams being asked to ship agentic workflows next quarter on top of whatever exists today.
Two more pressures are building. Recent Indian legislation has pushed banks, insurers, and public sector enterprises toward sovereign cloud and on-prem deployments, and they want governance that travels with the analytics. Their MSP partners, squeezed by outcome-based contracts and shrinking margins, need a way to prove outcomes, and "we maintain your dashboards" is not one.
How is decision infrastructure different from data infrastructure?
Your medallion architecture stops at the dashboard. Decision infrastructure starts there.
The last row is the one that matters. Data infrastructure failures are loud: a job fails, an alert fires, someone fixes it. Decision infrastructure failures are quiet. Every number is technically right, and the business still cannot agree on what happened. Two observations from our roadshow conversations explain why that is getting worse.
Sprawl travels between platforms
Analytics sprawl in one environment creates friction in the others, even the clean ones. Your new Databricks workspace can be spotless and still feel broken to users, because they cannot tell what each platform is for, or which one owns "active customer" this quarter. A clean platform inside a messy estate still delivers a messy experience. Sprawl is a property of the BI estate, not of any single tool, which is why migrating tool by tool rarely makes it go away.
AI raises the bar for every dashboard that survives
When a quick ad hoc question gets answered in a sentence, the dashboard loses its job as a lookup tool. What survives are the recurring decision surfaces: the board pack, the weekly ops review, the pricing console. Those get held to a higher standard, not a lower one, because they become the reference every AI answer is checked against. A rough chart was fine when it was the only answer. It is not fine when it disagrees with the assistant in front of the COO.
How does decision infrastructure work?
Decision infrastructure works when you can answer four questions about any number that reaches an executive or an AI agent, in minutes, without calling a meeting. If any of the four takes a meeting, that is where your gap is.
- Where does it live? Every place the number appears: dashboards, decks, semantic models, exports, and the sources your agents read from, across every BI tool and the lakehouse. Most teams can answer this for their gold tables and not for the reports built on them.
- Does anyone use it? Who opens it, how often, and how many teams have rebuilt it on their own. Usage is how you tell a decision surface from an abandoned experiment.
- What does it mean? One definition per business concept, with drift caught the moment two versions of "net revenue" start to split. This is semantic intelligence: understanding meaning across the estate, not just listing metadata.
- Who answers for it? A certified owner, lineage down to the data, a clear view of what breaks if something upstream changes, and a routed action when it does.
The mess is the message
Question two hides the most useful idea we took home from India. Self-service BI was never coordinated, and it could not have been. That is exactly what makes it valuable now. Every report a business user built without asking permission is a vote about what they actually decide on, and 77% annual growth in assets is a lot of votes. The self-service consumption layer is the most honest requirements document your company will ever produce. The problem has never been the signal. It is the 79% layer of noise sitting on top of it.
This is also the answer to the knowledge graph question. A knowledge graph built only from top-down definitions encodes what the data team believes the business means. A graph built from the concepts people already use, ranked by how much they use them, reflects the company. Build it from the consumption layer up, not from a workshop down.
Four rules that make it hold
- Agents answer only from certified assets. They do not need access to more reports. They need fewer, better sources.
- Usage nominates, concepts qualify, owners certify. Promotion is earned by the business, not requested by authors.
- Migrate decisions, not inventory. Your Databricks migration scope is the set of assets that pass the four questions, not the full list of workbooks.
- Run it continuously. Decision infrastructure is not a cleanup project. Definitions drift and owners leave every week, so the four questions need live answers, not a quarterly audit.
What does decision infrastructure look like in practice?
Your data catalog knows your tables. Your quality checks watch your pipelines. Neither one watches the consumption layer, where people and agents actually make decisions. That is the gap Datalogz Control Tower was built to close: a control point for analytics, built on semantic intelligence, that answers the four questions continuously across every BI tool and team.
Each question maps to a part of Control Tower:
- Where does it live? Inventory and Dependency Lineage. A unified inventory of every report, dashboard, dataset, and semantic model across Power BI, Tableau, Qlik, and Databricks, with upstream lineage and downstream blast radius, so you know what a change touches and who to tell before it ships.
- Does anyone use it? Analytics Similarity. Detects duplicate and near-duplicate reports by data, visuals, and logic. Ten copies of the same margin report is not only waste to retire. It is the business telling you which decision surface deserves certification.
- What does it mean? Concepts & Drift Explorer. Maps business concepts like net revenue across every tool and shows where definitions have drifted apart. In the Hyderabad story, this is where the returns exclusion shows up, before the VP asks the assistant.
- Who answers for it? Monitors & Alerts and Workflows. Continuous checks across governance, cost, security, and performance, routed to the right owner through ServiceNow, Jira, Slack, or Teams, with ownership reassigned automatically when someone leaves. Your team does not log in to check. They get told what to fix.
Analytics 360 sits on top as the executive view: a live summary of analytics health, cost, risk, and trust across the estate, so the CDO can see whether decision infrastructure is improving quarter over quarter.
Across the estates Datalogz governs, Control Tower has identified more than 1.4 million optimization issues and surfaced $8.2M in avoidable BI spend, contributing to over $50M in enterprise value for customers. The goal behind those numbers is simpler: decisions, decks, and dashboards you would bet your career on, whether the one asking is human or machine.
See what your AI is about to be built on. The complimentary Datalogz Analytics Scan runs inside your own environment and answers three of the four questions across every dashboard, report, and dataset in your connected tools: what you have and who owns it, where your business definitions have drifted, and what the sprawl is costing you. Request your Analytics Scan.
Frequently Asked Questions
What is the difference between decision infrastructure and decision intelligence? Decision intelligence refers to platforms that model, recommend, or automate business decisions, a category Gartner formalized with its first Decision Intelligence Platforms Magic Quadrant in January 2026. Decision infrastructure is the governed analytics foundation those decisions draw from: the certified assets, agreed definitions, and accountable owners behind each number. Decision intelligence tools depend on decision infrastructure to produce answers that the business will trust.
Does a lakehouse or medallion architecture provide decision infrastructure? No. A medallion architecture governs data quality across bronze, silver, and gold tables inside the lakehouse. It does not govern the reports, dashboards, semantic models, and business definitions built on top of those tables, which is where most conflicting numbers originate.
How should an enterprise decide which Tableau or Power BI reports to migrate to Databricks? The most reliable criteria are sustained usage, business criticality, and whether a report encodes a business definition that exists nowhere else. Reports with no views in the last 90 days, and near-duplicates of other reports, are usually better archived than migrated. Many teams also rebuild high-value reports on the new platform instead of converting them one to one, which is an opportunity to consolidate definitions.
Is consumption-based analytics pricing cheaper than per-user licensing? It depends on the shape of the workload. Consumption pricing removes the cost of idle users but adds cost for every query, refresh, and scheduled job, including those serving unused reports. Enterprises that move to consumption models generally need usage monitoring and refresh governance to realize the expected savings.
How do knowledge graphs support semantic alignment in BI? A knowledge graph represents business entities and the relationships between them, such as customers, products, and regions, in a form both people and AI systems can query. In BI, it gives agents and analysts a shared map of what each business concept means and how concepts connect. Its accuracy depends on the definitions it is built from, so most teams first inventory and reconcile the concepts already in use across their BI tools.
Why do global capability centers in India play a central role in analytics governance? Many multinational enterprises run their analytics engineering, BI administration, and reporting operations from GCCs in cities such as Hyderabad, Bengaluru, and Chennai. These teams maintain the dashboards and pipelines headquarters relies on, which places them closest to the operational cost of sprawl, broken refreshes, and conflicting metrics. As a result, they are often the first to adopt governance practices and the best placed to run them.
