Business Analytics to Improve Profitability

What is business analytics to improve profitability?#

Business analytics to improve profitability means a company takes data from different systems - ERP, CRM, production, finance and sales - and brings it together into one picture that shows where profit is created and where it is lost. It is a process, not a one-off report: data refreshes regularly, the connections between systems stay maintained, and decision-makers get a view that follows the company's actual state rather than past statistics alone.

In mid-sized and large companies, profitability data scatters across departments. Sales keeps its own records in a CRM system, production tracks cost in a separate system, and finance reports out of the ERP. Until someone systematically connects these sources, leadership makes decisions on partial insight - often only noticing a margin deviation once it surfaces in a monthly report, by which point the cause is already hard to trace.

The point of an analytics project is therefore not a nicer-looking dashboard, but a single source of truth that a managing director, finance lead and production lead all read the same number from. That requires deciding which data is authoritative, who enters it, how often it refreshes and who is accountable for its accuracy - questions that need resolving before a tool or a vendor is chosen.

Why profitability is not just about sales#

Profitability is often equated with revenue, even though it is shaped just as much by costs incurred along the whole process - from purchasing materials to after-sales support. Analytics that only tracks turnover misses margins by product, customer or project, so leadership cannot see which part of the business actually generates profit and which one quietly erodes it.

In manufacturing companies, profitability differences often hide between products with a similar sticker price but very different material use, machine time or labor. In service and project-based organizations, the differences show up between customers or projects, where the amount of unplanned work varies. Without connecting production, procurement and finance data, such differences stay invisible until the year-end accounts.

That is why a business analytics project for improving profitability always starts with deciding at what level the company wants to track margin - by product, customer, project, location or department. That decision determines which data needs connecting and how granular cost records must be, since analytics cannot show a precision that the source systems never captured.

Which data forms the basis for a profitability analysis?#

The basis for a profitability analysis is built from sales, procurement, production, finance and HR data, because only once these are connected does a true picture of cost and revenue emerge by product, customer or project. Any single system on its own only gives part of the answer.

Before an analytics project starts, it is worth mapping out what data already exists, in what form it is kept and how reliable it is. It often turns out that the same figure - the cost of a labor hour, say - carries different values in different systems, because each department entered it under its own rules. Reconciling these definitions is part of the project, not something solved along the way.

• Sales and customer records from the CRM system
• Financial cost and revenue data from the ERP system
• Production data: material use, machine time, scrap
• Procurement data and supplier pricing
• HR data on labor hours and employee cost
• Contract and project documentation where deviations from plan arise

How do you connect scattered systems into one source of truth?#

Scattered systems get connected into one source of truth through system integrations that automatically move data from the ERP, CRM, production and other sources into a shared analytics environment, instead of staff copying it between systems by hand. Manual reconciliation introduces errors and causes delays exactly when a fast decision is needed most.

Integrating systems requires deciding which system is the source of record for each data point, how often data synchronizes, and what happens when the same figure exists in two systems at once. Without clear rules, duplication or conflicting values appear, which undermines trust in the analytics from the very first use.

Connecting existing systems often means working with older infrastructure that was never built for real-time data exchange. In such cases, a judgment call is needed on whether to upgrade the existing system or move its data through an intermediate layer without disrupting day-to-day operations. That decision shapes how much time and testing the project requires.

What role does AI play in profitability analytics?#

In profitability analytics, AI helps spot patterns and deviations across large volumes of data faster than staff reviewing reports manually, and can flag unusual margin movements before they surface in a monthly close. It works as a complement to analytics, not a replacement for it.

In practice, AI is used to process documents and extract data from invoices, contracts or delivery notes, reducing manual entry and the errors that come with it. Semantic search and RAG approaches let leadership ask, in plain language, about the state of a specific project or customer instead of searching through several reports.

Bringing AI into analytics requires clearly defining which data may be part of the model, where that data is stored and who can see the results. For companies with sensitive financial or HR data, this question matters as much as the technical build, since a poorly set access boundary can expose data that should have stayed limited to one department.

• Detecting margin deviations by product or customer
• Processing and extracting data from invoices, contracts and delivery notes
• Semantic search across business documentation and reports
• Support for forecasting costs and revenue
• Automated alerts to responsible staff when values look unusual

Data security and access control#

Data security in analytics projects mainly means controlling who can see which data, since analytics often brings together financial, HR and business information that separate systems and access rights normally keep apart. Without clearly assigned roles, the analytics environment can become the place where data control quietly dissolves.

Setting up an analytics system requires separate development, test and production environments, so that testing new reports or connections never runs against real financial or personal data. Changes must go through review and testing before release into production, so errors never flow straight into the reports leadership bases decisions on.

Responsibility for the data and the analytics system's source code stays with the client, while documentation, access and passwords are part of every handover. This matters especially when a company later switches vendors or brings part of the maintenance in-house - without full documentation and access, continuing the project takes considerably longer.

How does analytics change the work of employees?#

Analytics mainly changes employees' work by cutting down manual report preparation and shifting the focus to interpreting results and making decisions, instead of pulling numbers together from different spreadsheets. For employees who used to build reports by hand, this means a change in role, not a loss of purpose.

Rolling out a new analytics environment also requires training - employees need to understand where a figure comes from and how to check it, otherwise they will not trust the reports and will fall back on old, manual sources. A gradual rollout, where the new system runs alongside the old one for a while, reduces resistance and lets errors surface before the system becomes the only source of truth.

Before rollout, leadership also needs to decide who inside the company owns the analytics system once the project ends - who approves new reports, who adds users, and who makes sure data definitions do not drift apart between departments over time. Without that ownership, the system loses accuracy within months, no matter how well it was built.

• Less manual copying of data between systems and spreadsheets
• Clearer accountability for data accuracy within each department
• A need for training in reading and verifying new reports
• A gradual transition where the old and new system run in parallel for a while
• A shift in finance and controlling roles toward analysis rather than just report preparation

How do you choose a vendor for a business analytics project?#

You choose a vendor for a business analytics project by checking whether they can connect the company's existing systems through APIs, whether they know how to take over or upgrade an already existing system, and whether they offer support and further development after rollout - not just a one-off set of reports.

For larger projects that touch several departments, it matters that the vendor assigns a project lead, clear team responsibilities, deadlines and a way of reporting progress. A business analytics project rarely runs in a straight line - during delivery it often turns out that data from a department not originally considered a source needs to be reconciled in too.

It is also worth checking how the vendor approaches testing: whether they use separate test environments, whether changes are reviewed before release, and whether independent QA, separate from the development team, is available. In analytics systems, where a wrong number feeds directly into a business decision, testing quality matters as much as delivery speed.

A client should also expect clarity on ownership from the vendor: source code and data remain the client's property, while documentation and access credentials are part of the handover. That guarantees the client can hand the project to another team at any point, without depending on a single vendor for the system's entire future.

Maintenance, support and ongoing development#

Needs do not end once an analytics system goes live - data sources change, new departments or products appear, new reporting requirements come up. Post-rollout support therefore typically covers monitoring, fixing errors, and technical and security updates, agreed within a support contract.

Errors and requests that come up after rollout are sensibly sorted by priority: a system outage or a failure of a key function demands immediate attention, a disruption to a single function slightly less so, and a minor issue with no impact on operations goes into the next regular release. This sorting keeps minor fixes from being treated with the same urgency as an outage.

New requests - an extra report, say, or a connection to a new system - are handled separately from errors: their scope gets assessed and a delivery date agreed. A clear line between an error and a new request lets a company plan the ongoing development of its analytics instead of treating every change as an urgent repair.

How Epix approaches business analytics projects#

Epix takes on a business analytics project for improving profitability in full or as a single workstream - from connecting existing systems through APIs, through AI-based document processing and data extraction, to building out reports and post-rollout support. For larger projects we assign a project lead, define team responsibilities and agree a reporting approach suited to the client's organization.

When a company already has a partly built analytics system or an unfinished project, we can take it over after reviewing the source code, architecture, infrastructure and data, rather than starting the build from scratch. Development, test and production environments stay separate, and changes go through review and testing before release into production.

After rollout, we can take over monitoring, fixing errors, an agreed level of support, technical and security updates, and ongoing development as new requirements arise. Source code and data remain the client's property, while documentation, access and passwords are part of every handover.