See original article on CFODive here.
Ten years ago, it would have been unusual to see a data and analytics leader reporting to the CFO. Today, that is much more common.
That is not an accident. It reflects how much the role of finance has changed.
Finance is still responsible for explaining what happened. That part has not gone away. But, increasingly, finance is also being asked to explain why it happened, what changed and what the business should do next.
The last point in particular represents a fundamental shift for finance, which historically has focused on reporting past performance with limited visibility into real-time business signals that could reshape the outlook.
Consider this example: At a manufacturing plant in Peoria, Illinois, a supplier misses a shipment of tubing needed for a packaging line. Production keeps running, but not at the rate the plan assumed. Throughput falls to 80% of forecast.
The operational problem is obvious inside the plant; the financial impact may not be obvious for weeks.
Finance will eventually see it. Revenue will come in lighter than expected. The forecast will miss. Someone at corporate will ask what happened.
The answer was sitting inside the business the entire time.
Finance has spent years getting very good at producing accurate, repeatable reporting. That work still matters. But the real value is no longer in producing another version of the same report. The value is in understanding what the numbers are telling you and how the business should respond.
Today, the need to recognize operational signals quickly is more critical than ever. Tariffs are changing cost structures. Supply disruptions are altering production plans. Shifting demand patterns are making the task of financial forecasting more difficult. A forecast that looked reasonable two weeks ago can become stale before the month is over.
The current environment gives finance a chance to rethink processes that were built for a time when every new question required another extract, another spreadsheet, another reconciliation, or another meeting. Many of those processes made sense when the cost of getting to detail was high. The question is whether they still make sense now.
This is why I think much of the conversation around artificial intelligence in finance misses the point.
AI does not magically create business insights from thin air. What it can do is help finance arrive at answers faster. But that only works if the underlying information is accessible and trusted.
It also requires judgment. That’s why I don’t get excited about someone who is great at building pivot tables. I can generate 10,000 Excel workbooks. That is not the scarce skill anymore. What I need is someone who understands the business.
The best forecasters I have worked with were not necessarily the people who spent the most time in Excel. They were the people who understood how the business actually operated.
They knew when a supplier issue was going to create a downstream problem. They knew when demand was behaving differently than it should. They knew when a number looked wrong because it did not match what they were hearing from customers, seeing in the operation or sensing from the market.
That judgment is becoming more important, not less.
The rise of AI has simply given finance more capacity to focus on higher-value work.
A lot of the routine work around data gathering, validation and report production can now be automated. That does not make finance professionals less valuable. It changes where they add value.
The differentiator is not who can build the most complex spreadsheet. The differentiator is who understands the business well enough to recognize when something is changing and what that change is likely to mean.
Finance has been very good at explaining what happened. The opportunity now is to understand why it happened while there is still time to act.
Enterprises stall on AI in production because the data reaching the model is fragmented, stale, and flattened. The record of a single order is spread across the ERP, the CRM, and the fulfillment system. Batch is the default - and batch means your agent is reasoning about last night. Flattening is the one people underrate. Pre-aggregation is where the answer to "why" quietly disappears.
Every one of these ends the same way: The AI answers with confidence about a version of the business that no longer exists.
I use three words for it: live, detailed, and connected.
Live means current at the time of the question. Fresh-ish does not count. Detailed means the lowest level, and that is a requirement. If an agent is asked why margin moved from one number to another, it can’t answer from a monthly total. Connected means the relationships between your systems are part of the context itself. Miss one, and everything above it inherits the gap.
Everyone agrees you need structured, unstructured, and real-time data together. In practice, the structured operational data is the blocker. The reason has nothing to do with size. ERP schemas were built for writing transactions; they run thousands of tables deep, and every conventional route out of them flattens the data.
Documents, tickets, logs, and clickstreams are easier to land by comparison. I'm not saying those layers are unimportant, or that they are simple. ERP is just the one that stalls the entire project, so it gates the rest.
Our systems of record are read directly and delivered to the AI layer. That middle box is Incorta: you don't have to reshape anything in advance, and you don't rebuild a pipeline every time the business adds a requirement, adds an entity, or changes a process. You’re connected right to live, detailed data from the source system.
Direct Data Mapping™ makes that work. It keeps your data at full fidelity, with the speed to query it, so you or your agent can drill to the smallest level with no lag.
One of my most used examples is our customer, Broadcom - they took complex data across 26 business units and consolidated it into one layer. They were able to cut data pipeline costs by 80%, and took reporting wait time from twelve weeks to nearly instantaneous. They also streamlined their SKU down to about 500 queries: about 17,000 Broadcom users log in to Incorta every day to get the data (and context) they need to do their jobs. (You can read more here)
Think about your foundation before you build the stack. Mastering and quality matter, and so does search. Each of them needs something current, clean, and connected underneath.
Watch the full session here:
Mainstream maintenance for SAP ECC 6.0 Enhancement Packages 6 through 8 ends December 31, 2027, and Enhancement Packages 0 through 5 already lost mainstream maintenance at the end of 2025. After 2027, you can pay for extended maintenance through 2030, qualify for a limited RISE-only option through 2033, or fall back to reduced support. Every path leads to the same decision: plan your move to SAP S/4HANA. This article covers the dates, your options, the migration paths, and what happens to your data along the way.
SAP ECC (ERP Central Component) is the core of SAP Business Suite 7. It has run finance, supply chain, manufacturing, and HR for thousands of companies for about two decades. SAP is ending mainstream maintenance for it on December 31, 2027.
Mainstream maintenance covers the security patches, legal and regulatory updates, and full support that keep an ERP current. SAP leadership has repeatedly said the 2025 and 2027 end of mainstream maintenance dates for ECC 6 are not changing. Rimini Street
ECC keeps running after December 31, 2027 - what changes is the support behind it. Users have three options:
1. Extended maintenance (2028 to 2030). Companies can buy optional extended maintenance through the end of 2030, which adds three years of support at two percentage points on top of the existing maintenance fee. It covers security patches and limited legal updates. It adds no new features. IBsolution
2. The SAP ERP, private edition, transition option (to 2033). This option lets a select group of large, complex customers keep running ECC under a managed cloud subscription through the end of 2033. The conditions are strict. Eligible organizations must move relevant systems to SAP ERP Private Edition on the HANA database before the end of 2030, hold a RISE with SAP contract, and purchase the option under a fee-based plan. It is not a general extension of ECC support, and most mid-size companies will not qualify. SAP ECC End of Life 2027: What CIOs Need to Know Now +2
3. Customer-specific maintenance. This is the default if you do nothing. Its scope is much smaller, with no guaranteed new security patches or legal updates, so most organizations treat it as a last resort. Qyrus
For most SAP customers, yes. Extended maintenance and the transition option add time. The move to S/4HANA stays on the roadmap either way. The real question is when you start and how you'll keep the business running during the project.
Longer than most teams expect. Most S/4HANA migrations take 12 to 24 months, and full projects run 18 to 36 months once data and integrations are included. That means a company starting in late 2026 is already working against the 2027 date. Doodex
Adoption is also behind the deadline. At the end of 2024, only about 39% of ECC customers had licensed S/4HANA (Gartner, via CIO). As 2027 gets closer, experienced migration partners get harder to book and more expensive.
There are three common approaches.
The right choice depends on how customized your ECC system is, how much history you need, and how much process change you want.
This is the part most migration plans underestimate. It shows up in two ways.
Reporting can break during the move. Your dashboards and reports are built on ECC's data model. When that model changes, reports stop updating or need to be rebuilt. For a stretch measured in quarters, finance, supply chain, and operations can lose a clear view of the business.
Historical data can become hard to reach. A greenfield S/4HANA system often starts without your full ECC history. Once ECC is turned off, years of transactions can be hard or expensive to access. That's a problem for audits, compliance, and trend analysis.
Incorta connects directly to both SAP ECC and S/4HANA, so your reporting stays live the whole way through the migration. It keeps years of ECC history available after the system is turned off. It also gives you real-time analytics on your SAP data today, so you can migrate on a timeline that works for you.
Find out where your data stands before the migration starts. Get the SAP Migration Guide here.
More frequently asked questions:
When does SAP ECC support end?
Mainstream maintenance ends December 31, 2027. Extended maintenance runs through 2030 at an added cost.
Is SAP extending the ECC deadline to 2033?
Only for select large customers who qualify for the SAP ERP, private edition, transition option under RISE with SAP. Most companies should plan around 2027 and 2030.
Will ECC stop working after 2027?
No. It keeps running, but support shrinks unless you pay for extended maintenance.
Will I lose my data when I migrate to S/4HANA?
You may lose easy access to it. Plan early to keep ECC history in a store you can still query after the system is retired.
G2 publishes its reports four times a year. Placement comes from verified reviews written by people who use the product at work, scored on satisfaction and market presence. Vendors cannot buy a badge.
Here are the headline recognitions from this quarter:
Fifteen badges in total, spanning four data and analytics categories.
The three usability badges came from the same category and the same segment: Enterprise Data Preparation, scored by reviewers at companies with more than 1,000 employees. Those are the environments where data platforms usually get hard. Dozens of source systems. Years of customizations in ERP. Security models that took a decade to build.
Enterprise data prep has a reputation for long deployments and heavy administration. Teams budget quarters for a rollout and then staff a small group to keep the pipelines healthy. Winning Easiest Setup, Easiest Admin, and Easiest To Use in that segment says the day-to-day experience holds up under real enterprise conditions.
The reason traces back to how the platform works. Incorta reads data directly from source systems and keeps the original detail intact, so teams skip the modeling and transformation layers that make traditional stacks slow to stand up and expensive to maintain. Fewer moving parts means a shorter path from connection to first dashboard, and less work to keep everything running after go-live.
The badges land in four categories that most vendors treat as separate products: Data Preparation, Analytics Platforms, Data Fabric, and Data Science and Machine Learning Platforms.
That spread matters for buyers. A typical modern data stack stitches together an ingestion tool, a warehouse, a transformation layer, a BI tool, and a separate environment for data science. Every handoff adds cost, latency, and one more place for numbers to drift apart. Being recognized in all four categories at once reflects a platform that covers that ground in one place, on live data, with the detail preserved end to end.
For teams building AI on top of their data, the same point applies. Models and agents are only as good as the data underneath them. A platform that already handles preparation, delivery, and analysis gives AI a single, governed source of context instead of five partial ones.
The Momentum Leader badge in Data Preparation and Analytics Platforms measures growth. G2 calculates it from review velocity, satisfaction trends, and market signals over time, which makes it a read on where a product is heading rather than where it has been.
Two Momentum Leader badges alongside four High Performer badges tells a consistent story: customer satisfaction is high, and more customers keep arriving.
G2 badges come from verified user reviews. Reviewers confirm their identity, describe their company size and role, and answer detailed questions about the product.
Because scoring resets every quarter and recent reviews carry more weight, badges reflect how the product performs now.
Analyst reports tell you what a firm thinks of a vendor's strategy. G2 tells you what the people running the software say about it after go-live. Both are useful, and they answer different questions.
If your shortlist includes platforms that promise to unify data preparation, analytics, and AI, three questions are worth asking every vendor: how long until the first working dashboard, how many people it takes to administer once it is live, and whether business users can answer their own questions without filing a ticket. The Fall 2026 badges are Incorta customers answering those three questions.
How many G2 badges did Incorta earn in the Fall 2026 reports?Incorta earned 15 badges in the G2 Fall 2026 reports, across Data Preparation, Analytics Platforms, Data Fabric, and Data Science and Machine Learning Platforms.
What does the G2 Easiest Setup badge mean?Easiest Setup is awarded based on implementation scores from verified user reviews. Reviewers rate how long deployment took, how complex it was, and how well it met expectations. Incorta earned it in Enterprise Data Preparation for Fall 2026.
What is a G2 Momentum Leader?A Momentum Leader is a product with the strongest growth signals in its category, calculated from review velocity, satisfaction trends, and market presence over time. Incorta is a Momentum Leader in Data Preparation and Analytics Platforms for Fall 2026.
What is the difference between a G2 Leader and a High Performer?Both reflect high customer satisfaction. Leaders also have large market presence, measured by review volume, company size, and web presence. High Performers earn top satisfaction scores with a smaller market footprint.
How does G2 define the enterprise segment?G2 assigns reviews to segments by the reviewer's company size. The enterprise segment covers companies with more than 1,000 employees.
Which G2 categories is Incorta recognized in?Incorta holds Fall 2026 badges in Data Preparation, Enterprise Data Preparation, Analytics Platforms, Data Fabric, and Data Science and Machine Learning Platforms.
Where can I read Incorta reviews on G2?Verified customer reviews are published on Incorta's G2 profile at g2.com.
Read the reviews behind the badges on Incorta's G2 profile, or book a demo to see how fast a live enterprise data environment comes together.
Most enterprise data platforms were designed to answer a question: What happened?
But AI is increasingly being asked a different one: What should happen next?
Take a simple example. A dashboard can tell you inventory is running low. An AI-powered workflow might recommend a reorder, identify at-risk suppliers, and surface downstream impacts before they affect the business. Those outcomes depend on access to the operational data that reflects what is happening across the business right now.
As organizations move from reporting to AI-driven workflows, operational systems are becoming more than systems of record. ERP, finance, supply chain, and other operational systems contain the signals that reflect what is happening across the business right now. Making that data available to analytics and analytics and decision-support workflows is becoming critical as organizations look to respond to changing business conditions in real time.
Incorta helps organizations bring live ERP and operational data from systems such as SAP, Oracle, Workday, and NetSuite into BigQuery while preserving the relationships, hierarchies, and business rules embedded within those systems. Using Direct Data Mapping™, Incorta maintains the structure of source applications, reducing the need for extensive transformation and modeling before data can be used across reporting, machine learning, and AI initiatives.
On Google Cloud, BigQuery becomes an operational foundation where Gemini and AI-driven workflows can work from the same trusted business data. Instead of relying on separate data pipelines for reporting, forecasting, and operational workflows, teams can work from a common foundation that reflects current business conditions and keeps decision-support systems aligned.
That allows AI to operate on current business signals rather than historical snapshots. It can help identify potential supply chain disruptions as conditions change, recommend inventory actions based on current demand signals, surface financial risks before they escalate, or initiate next-step actions directly within business workflows.
The value of operational data extends beyond reporting and visibility. It creates the foundation for systems that can increasingly support decisions and actions using the same trusted business context that powers analytics today.
For data and AI leaders, the opportunity is not simply faster reporting. It is the ability to operationalize AI on top of the systems that already run the business. Instead of creating separate data architectures for analytics, AI, and future initiatives, organizations can build on a shared operational foundation that supports all three.
As AI adoption continues to accelerate, the organizations that move fastest may not be the ones with the most data. They may be the ones that can turn operational data into action.
Learn more: Explore Incorta on the Google Cloud Marketplace to see how Incorta and Google Cloud help operationalize AI on top of trusted enterprise data.
Enterprise data contains the relationships, workflows, and business logic that make organizations run. A shipment, for example, is more than just a record. It connects suppliers, inventory, forecasts, fulfillment timelines, and customer commitments. An invoice is tied to contracts, approvals, and payment terms.
As organizations deploy AI more broadly, those connections become increasingly important because AI needs to understand how the business actually works, not just what each record says. Yet the relationships that connect data across the business can become harder to preserve as information moves between operational systems, warehouses, and analytics environments. When AI can only see fragments of the underlying business process, recommendations become less reliable and harder to operationalize.
Incorta and Google Cloud help make enterprise systems usable for AI without requiring organizations to reconstruct the relationships and business logic embedded in their data from scratch.
Incorta helps organizations bring complex ERP and operational data into BigQuery without stripping away the relationships and rules that give it meaning. Using Direct Data Mapping™ and its semantic layer, Incorta preserves the business structure already embedded in source systems, so teams can bring ERP and operational data into BigQuery without losing the relationships, hierarchies, and business rules that make it useful for analytics and AI.
On Google Cloud, that contextualized data becomes available in BigQuery, where it can support analytics, Gemini, and other AI-driven workloads. Instead of starting with raw replicated records, organizations start with data that retains the relationships, hierarchies, and business rules needed to make data truly AI-ready.
Together, Incorta and Google Cloud help establish a shared foundation for analytics and AI, reducing the effort required to reconstruct business logic every time a new use case emerges.
When enterprise relationships, business rules, and operational context remain intact, the same foundation can support analytics, AI, and emerging agentic workflows without requiring teams to rebuild business logic for every use case.
A supply chain model can work from the same operational context used by an AI agent. A financial forecast can incorporate the same business relationships already used for reporting and analysis. Rather than creating separate pipelines, semantic definitions, and data models for every new initiative, organizations can extend a trusted business structure across analytics and AI.
For data and AI leaders, that operational foundation creates a more scalable path to operationalizing AI. New analytics, AI, and agentic use cases can build on the same trusted data foundation rather than requiring separate pipelines, semantic definitions, or data models for every initiative.
As enterprises move beyond experimentation, helping AI understand how the business works is becoming just as important as giving it access to the data in the first place. That understanding is what enables organizations to move from reporting on the past to acting in the present.
Learn more: Download Onramp to the Integrated Data Superhighway for AI Agents to explore how Incorta and Google Cloud help create a trusted foundation for enterprise AI. Or find Incorta on the Google Cloud Marketplace to get started.
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