ERP Solutions for Business Data Analytics

What are ERP data analytics solutions and what are they for?#

ERP solutions for data analytics upgrade an existing business information system by pulling data from finance, procurement, production and sales into one consistent picture suitable for reporting and decision-making. Instead of a department head manually copying numbers into spreadsheets, the system prepares them automatically, already in a form ready for further processing. This approach makes most sense once a company grows beyond a single department or location and needs a view that connects several sources at once.

In practice this means the ERP system is no longer just a transaction ledger but also a data source for ongoing analytics, reporting and forecasting. Companies with more than a million euros in annual revenue usually already run an ERP, often alongside separate systems for HR, documents or production. An analytics solution has to connect these sources without duplicating data entry or creating yet another record nobody maintains.

Deciding how to design analytics on top of ERP data is therefore not just a technical question but an organizational one. Someone has to own each data source, decide which data must be available in real time, and which is fine as a daily or weekly summary. These decisions later shape the solution's architecture and how much effort it takes to maintain.

Where problems most often arise when connecting ERP data to analytics#

Most problems in ERP data analytics don't come from the analytics layer itself but from the state of data in the underlying system. If product, customer or cost-center codes are maintained differently across departments, a report that combines them shows a distorted picture even though it was technically built correctly. This is why any serious ERP analytics project starts with a review of data quality, not with building dashboards.

Another common problem is limited or poorly documented access to data inside the ERP system. Some systems offer an open API, others require exports through intermediate files or manual report preparation. Either way, what the system actually allows has to be verified before work starts, and checked against the client's real environment, not just against vendor documentation.

In practice, problems almost always appear at the boundary between departments, not inside a single ERP module. Sales maintains its own codes, production its own, procurement often a third set, and analytics trying to merge them first has to resolve or at least map these differences. The same issue shows up in historical data entered years ago under different rules than today. Before building any reports, we map out the circumstances that most often slow a project down:

Each of these circumstances adds work before analytics can be built, which is why we map them out during the analysis phase and break them into separate work packages. This gives the client a realistic view of the scope before committing, and gives the provider a clear starting point for the solution's architecture. Skip this step and the analytics layer is often built on unreliable data — something that only becomes obvious once management stops trusting the reports.

• Data is scattered across several modules and databases
• Codes and master records are not aligned between departments
• Historical data is incomplete or entered inconsistently
• API access to the ERP system is limited or poorly documented
• Reports are prepared manually in spreadsheets
• There is no clear ownership of the data model

How do you choose the right approach to ERP data analytics?#

The right approach to ERP data analytics depends on how many data sources you're connecting, how fresh the data needs to be, and who will actually use the reports. There is no single solution that fits everyone; a smaller company with one ERP system needs a different architecture than an organization connecting ERP, CRM and document management systems at the same time. The choice of approach therefore follows the analysis of existing systems, not the other way round.

Choosing an approach means separating reporting, which summarizes past data, from analytics that supports real-time decisions. Financial reporting is usually fine as a daily or weekly summary, while monitoring stock levels or production lines often needs near-real-time data. Mixing these two needs into one solution without thinking it through produces a system that's overbuilt for one use case and too slow for the other.

Before choosing a technical architecture, we work through a set of baseline questions with the client that need answering before any design work starts. The answers determine whether the solution should build on the ERP system's existing reporting module, a separate analytics layer, or a combination of both. The questions we most often raise at this stage are the following:

The answers to these questions determine whether it's worth upgrading the existing ERP reporting module or building a separate analytics layer that pulls data from several sources at once. In regulated sectors such as the public sector, we add questions about audit trails and access control. Only once these baseline points are clear does it make sense to talk about a concrete architecture, and about which part of the system is taken on in full and which as a separate work package.

• Which departments and systems need to be connected
• How often data changes and how quickly it's needed
• Who will use the reports and on which device
• Whether an audit trail is required for regulated data
• Whether it's a new system or an upgrade of an existing one
• What growth in data volume is expected

Which ERP data is most useful for analytics?#

The most useful data is whatever the ERP system already tracks daily and that directly reflects the state of the business: finance, inventory, orders and labor costs. This is data the company already collects as part of running the business, so it doesn't need to be set up from scratch — it just needs to be properly connected and displayed in a form suitable for management decisions.

Beyond these core areas, it's worth including supplier, delivery-time and quality data, since these often explain deviations that financial or sales figures alone don't reveal. Manufacturing companies typically add machine-capacity and shift data, while public institutions add service-delivery and deadline data they must track for reporting to oversight bodies anyway.

Regardless of industry, designing ERP analytics for a company often works best by starting with a limited set of data areas and expanding only once the first part of the analytics is reliable and in actual use. The first phase most commonly covers the following data areas, since they give management the fastest overview of the business:

Once this first set is in place and management trusts it, we expand it with data specific to the industry or department, such as quality data in manufacturing or service-delivery data in a public institution. Expanding gradually is more manageable than trying to connect every available data source in one step, since errors in codes and master data are easier to spot and fix at a smaller scale.

• Financial data: revenue, costs, liquidity
• Inventory and procurement data
• Production and capacity data
• Sales and order data
• HR and labor-cost data
• Supplier and delivery-time data

The role of AI and automation in ERP data analytics#

AI and automation in ERP analytics take over tasks that used to require manual report preparation, such as pulling data out of documents, spotting deviations, and sending alerts when a value crosses an agreed threshold. Instead of staff rebuilding the same spreadsheet report every week, the system prepares the data automatically and only flags a deviation worth looking at.

For companies that process large numbers of documents — invoices, delivery notes, contracts — AI is also used to extract data from those documents and feed it into the ERP system without manual entry. This cuts down on manual input and, with it, the errors that later complicate analytics, since incorrectly entered data shows up in reports as unexplained deviations.

Another common use is semantic search across internal documents and records, letting staff quickly find an answer without digging through several systems at once. It matters that the AI component works on data that is already clean and accessible through an API — otherwise it simply inherits existing errors in master data instead of fixing them.

Automating reporting and alerting is worth adding only once the underlying analytics is stable and the data reliable. Bolt an AI component onto an environment with poorly maintained codes too early, and the result isn't faster decisions — it's faster spread of wrong conclusions. That's why the AI part of a project normally follows data cleanup, not the other way round.

Data security and compliance in ERP analytics#

Data security in ERP analytics is fundamentally an access-control question, since the analytics layer often merges data that used to sit in separate systems accessible only to a limited group of staff. Once financial, HR and production data land in a single report, you have to redefine who can see all of it and who only sees the part relevant to their department.

In the public sector and regulated industries, this is sharpened further by the requirement for an audit trail: you need to know who changed a piece of data, when, and why. That's why we keep development, test and production environments separate, and never use real personal data in test environments — doing otherwise would needlessly increase the risk of data leaking outside the tightly controlled production system.

Every change to an analytics solution goes through review and testing before it reaches production, including changes that affect which data is visible to which user role. This matters especially in ERP systems, where a misconfigured access right can expose financial or HR data to staff who should never see it.

Source code and data remain the client's property, while documentation, access credentials and passwords are handed over at the end of the project or of a given work package. This lets the client keep control over who has access to the analytics environment even after the engagement with the provider ends — a key question whenever the data involved includes financial or personal information.

How does the rollout of an ERP analytics solution work?#

Rolling out an ERP analytics solution happens step by step, from analyzing existing data and systems to handing over documentation and agreeing on ongoing support. We can take on the project in full or just a single work package — for example, only connecting the ERP system to one analytics report — depending on what the client already has in place and what's missing.

On larger projects we start by naming a project lead, defining each team's responsibilities, and agreeing on how progress is communicated and reported. This matters especially when several of the client's departments are involved, since everyone needs to know who owns which part of the data and who signs off that a given work package is ready for the next phase.

When a client already has a partially built or unfinished analytics system, we can take it over after reviewing the source code, architecture, infrastructure and existing data. This is often faster than starting from scratch, since it preserves work already done, but it requires a careful review so that errors from the earlier phase don't carry over into the new analytics layer.

Whether it's a new project or taking over an existing, partially built system, the work follows a similar sequence of steps, ensuring no phase proceeds without the client's oversight and that each step can be verified before moving to the next. The full rollout of an ERP analytics solution can be summarized in the following steps:

After handover, the client receives all documentation, access and passwords, so they aren't dependent on a single provider for future changes to the system. This ensures that the data and source code, which belong to the client, can be moved to another provider if needed, without losing the work already invested.

• Analysis of existing systems and data
• Naming a project lead and defining team responsibilities
• Separate development, test and production environments
• Testing and acceptance checks before release to production
• Handover of documentation, access and passwords
• Agreement on ongoing support and maintenance

Maintenance and support after an ERP analytics rollout#

After an ERP analytics rollout, it's worth agreeing on ongoing support, since the need for changes only becomes clear once a wider group of staff actually starts using the system. We can take on monitoring, bug fixing, technical and security updates, and further development, with the scope set according to how critical the system is to the client's day-to-day business.

Faults and change requests are sorted into classes, so that whatever actually stops work gets handled before minor improvements. A full outage of the system or a key part of the analytics gets the highest priority, a disruption affecting a single function a lower one, and a minor fault with no business impact is scheduled into the next regular release.

For post-rollout support, it helps to agree in advance which class a given issue falls into, avoiding arguments about priority at the moment the system is already disrupted and management is waiting for a report. The classification we use for ERP analytics support covers the following four request classes:

The response time for each class is agreed in the support contract and depends on how critical the system is to the client's business, so we don't generalize it here. What matters is that the classification is known in advance, so that reporting an issue doesn't first require a debate about how urgent it is — work on fixing it can start right away.

• Outage: the system or a key part does not work, handled with top priority
• Disruption: the system works, a specific function does not
• Fault: a minor issue with no business impact, scheduled into the next release
• Request: a change or upgrade, scope assessed and timing agreed

How do you choose a provider for ERP data analytics?#

You choose a provider for ERP data analytics based on whether they can actually work with the client's existing system, not just build a new one from scratch, and on how clearly they define responsibilities, timelines and reporting before work even starts. On projects touching several departments, it matters just as much that the provider understands the organizational consequences of the project, not only the technical ones.

Check whether the provider keeps development, test and production environments separate, and whether they use real personal data in test environments — an unacceptable risk when financial or HR data is involved. The same goes for asking who owns the source code and data once the project ends; with a serious provider the answer is always the client, with documentation and access handed over at closure.

When choosing a provider for an ERP analytics project, it's worth going through a few concrete questions, since the answers show whether the provider actually understands the scope such a project requires, rather than just promising quick results without ever looking at the existing systems. Before committing to a provider, it's worth checking the following points:

The answers to these questions say more than generic reference showcases, since an ERP analytics project largely depends on how carefully the provider works with the client's existing systems. A provider who can't answer these questions clearly is unlikely to have clear answers to the questions that come up along the way during the project either.

• Whether the provider reviews existing systems and data before quoting
• How they define responsibilities, timelines and progress reporting
• Whether they separate development, test and production environments
• What happens to source code and data after the project ends
• Whether they offer support and an SLA after rollout

What determines the price of an ERP data analytics solution?#

The price of an ERP data analytics solution isn't fixed — it depends on the scope of functionality, the number of connected systems, and the state of the data that needs cleaning up before analytics can be built. That's why we don't publish prices or price ranges upfront; instead, we suggest a short analysis that breaks the scope into work packages, so each one can be estimated and delivered separately.

The scope of work is most affected by how many existing systems need to be connected and what their APIs look like, and by whether it's a new system or taking over and upgrading an existing one. Taking over an existing, partially built system requires a dedicated review of the source code and architecture before the remaining analytics work can be estimated.

When estimating the scope of an ERP data analytics project, we weigh several factors at once, since only their combination reveals how demanding a given project is and how much time data preparation will take before reports can even be built. Among the factors we consider are the following:

Only after this kind of analysis can a realistic estimate of scope and timeline be given, which is why we publish pricing only after reviewing a specific client's actual requirements. This avoids both underpriced quotes that lead to extra costs later, and flat estimates that ignore the real state of the existing systems and data.

• Scope of functionality and number of user roles
• How many existing systems need connecting and what their APIs look like
• Whether it's a new system or an upgrade of an existing one
• The volume and state of data that needs cleaning up or migrating
• Security, audit-trail and compliance requirements
• Scope of testing and the level of support after rollout