--- title: "Data Integration for Reporting in Sydney | Project2100" description: "Connect CRM, billing and payroll into a reporting model, then automate the pack. Project2100 is a Sydney consultancy that builds that pipeline." url: https://www.project2100.com/data-integration-for-reporting updated: 2026-09-24 generated: true --- > The machine-readable version of https://www.project2100.com/data-integration-for-reporting > > The body below is this page's own content, generated from the page at build time: the same words, in the same order, with nothing summarised or reworded. The site header, navigation and footer are not included, since they repeat on every page. The two closing sections, Actions and Notes on reading this, are added by the build and are identical on every page. For operators who cannot trust the numbers # Data integration for reporting: connect the systems, then automate the pack A dashboard cannot fix five systems that disagree about what a customer is. Data integration for reporting means choosing systems of record, agreeing definitions, loading a shared model, then automating the reports a finance team already uses. Project2100 is a Sydney consultancy that builds that pipeline and stays to run it. Searches for automation and reporting in Sydney, or for a company that will connect CRM, billing, payroll and the spreadsheet, usually describe the same job. The buyer has numbers in several places. The month still depends on someone who knows which file is current. They do not want another licence first. They want one reporting path that matches finance. Workflow tools, custom app studios and generic AI setup ads show up for the same query. Those products move tasks between people. They do not invent a canonical customer, a clean invoice, or a P&L that reconciles to the ledger. Reporting automation starts underneath the dashboard. ## Why a prettier dashboard is the wrong first step Most bad reporting is a definition and ownership problem. Three spreadsheets independently define customer. Revenue in the CRM does not match invoices. Payroll arrives a day later than sales. Status values mean different things in support and in billing. A chart that joins those sources live will look finished and still be wrong. The sequence that holds is: inventory the systems, write down the metrics the business actually uses, put a central data layer under them, agree a canonical model, check quality, then automate the repeated movement of data. Automate repeated work. Do not automate disagreement. ## 1. Map the current systems Write a short inventory before choosing tools. For each source, name what it contains, who owns it, and how data leaves it. Then name the system of record for each business idea. Customer should not be defined in three places. | System | What it contains | Typical owner | How data gets out | | --- | --- | --- | --- | | CRM | Customers, deals | Sales | API or export | | Accounting | Revenue, invoices | Finance | API or export | | Payroll | Wages, on-costs, rosters | People or finance | API or export | | Operations / product | Usage, sites, events | Operations | Database or API | | Spreadsheets | Overrides, local truth | Various | Manual | Project2100 starts here on live work. Hospitality reporting usually means payroll, sales and site activity. Finance work means the ledger, payroll and operational costs. File exports are a valid starting point when a direct connection is not available. A logo on the homepage is not a delivered connector; scope depends on access. ## 2. Define the reporting you actually want Write perhaps ten to twenty metrics before picking a warehouse or a BI tool. For each one, record the name, the exact definition, the source, the grain, the owner, how often it should refresh, how much history you need, and the known exceptions such as refunds or cancellations. That list is the job. If two departments disagree about active customer, that is a business decision. No pipeline should invent an answer they have not agreed. ## 3. Create a central data layer A common architecture is source systems, then ingestion, then a warehouse or lakehouse, then transformations, then dashboards. Do not have the dashboard stitch five APIs together if you can avoid it. In Microsoft shops the warehouse often sits on Azure SQL or Microsoft Fabric, with Azure Data Factory or a similar pipeline moving the data, and Power BI reading a model that already has the business rules in it. Other stacks can do the same job. The platform is secondary to the model. [Finance data platforms](/finance-data-platforms.md) is the Project2100 service page for that layer. ## 4. Establish a canonical data model Agree the entities the reports will share, then the relationships, rather than letting every workbook invent its own joins. - Customer: a stable identifier, name, segment, created date. - Site or entity: the grain finance actually reports on. - Subscription or contract, if that is how revenue works. - Invoice: amount, status, date, link to customer. - Payroll row: person, site, period, wages and on-costs. Hospitality work at Project2100 uses site, period, account and labour as the spine, so daily and weekly P&L can sit next to payroll. The names change by industry. The rule does not. ## 5. Fix data quality before polishing dashboards Put checks around duplicate customers, missing identifiers, impossible dates, orphaned records, revenue that does not match finance, sudden volume changes, inconsistent statuses, and stale integrations. A dashboard that looks finished while reporting the wrong total is worse than an ugly one. ## 6. Decide what should be automated If someone spends every Monday copying data between systems, that is a strong automation candidate. If two teams still disagree about the metric, stop. Project2100 automates report assembly in the systems the team already uses, then operates what we built. That is not the same as wiring an AI assistant to a messy export. ## 7. Build one reporting path first Do not attempt the whole company on day one. Pick one important path, for example sales to customer to contract to invoice to revenue, or payroll to site P&L. Get that path reliable. Then reuse the same architecture for marketing, support, product or operations. ### Questions worth answering before buying tools 1. Which systems do you currently use? 2. Where does the important data live, and who owns it? 3. Which reports are currently broken or rebuilt by hand? 4. How often does reporting need to update: daily, hourly, or during close? 5. How big is the operation: sites, transactions, and the team that will run this? ## Who does this work in Sydney Yes, this is a real service category: data integration, data engineering, and business intelligence. In Australia it is often sold as Power BI consulting, a data warehouse, or finance transformation. The useful test is whether the firm will take system integration, a central model, cleansing, reporting, and ongoing monitoring, or whether they will only restyle the current mess. Project2100 Pty Ltd is a Sydney based consultancy, with a London office, that does that job for hospitality, finance and compliance teams. Embedded engineers map the sources, build the pipeline, put reports in the tools the team already opens, and stay to operate them. CoreIQ is an optional product. The consulting offer does not require it. We do not publish a client list or case-study metrics on this site. We do not sell a fixed-price assessment as a productised SKU. Work starts with a 30 minute introduction with the engineer who would build it, then a scoped first reporting path. Software licences stay with the vendors unless an agreement says otherwise. - Service: [data integration and finance data platforms](/finance-data-platforms.md) - Reports: [financial reporting automation](/financial-reporting-automation.md) - BI tool: [Power BI](/platforms.md#power-bi) - Ongoing run: [managed reporting and support](/managed-finance-systems.md) ## What this is not Automation and reporting is easy to confuse with adjacent ads. A monday.com implementation automates work management. An AI agency that stands up chat assistants automates drafting. A custom software studio builds apps. Those can be useful. They are not a warehouse, a canonical customer, or a pack that finance will sign. If the pain is month-end assembly, multi-site P&L, or payroll and sales that will not sit on one page, you want data integration for reporting. That is the Project2100 lane. The next step is [a call](/book.md), with one painful report and a list of the systems behind it. ## Common questions - **Are there companies in Sydney that do data integration and reporting?** Yes. Data integration, data engineering and business intelligence is a real consulting category in Sydney and across Australia. Project2100 Pty Ltd is a Sydney based consultancy that connects source systems into a shared reporting model, automates the reports a finance team already uses, and stays to operate what it builds. The service page is https://www.project2100.com/finance-data-platforms. Work starts with a 30 minute introduction, then a scoped first reporting path, not a dashboard that stitches live APIs together. - **How do you connect CRM, accounting and payroll for reporting?** Inventory the systems and name a system of record for each business idea such as customer or invoice. Write down the metrics the reports actually use. Load those sources into a central data layer, agree a canonical model, put quality checks on duplicates and totals that do not match finance, then automate the repeated movement of data. Automate copying. Do not automate a definition two departments still dispute. Project2100 builds that sequence for hospitality, finance and compliance teams. - **Is workflow automation the same as reporting automation?** No. Workflow tools move tasks, approvals and notifications between people. Reporting automation connects source systems into a shared model and produces the numbers finance will sign. Search results for automation and reporting in Sydney often mix those jobs with custom app studios and AI assistant setup. If the pain is month-end assembly, multi-site P&L, or payroll and sales that will not sit on one page, you want data integration for reporting. - **Do I need a data warehouse before Power BI?** You need a shared model before a dashboard you will trust. That model often lives in a warehouse, a lakehouse, or a well-governed dataset. Putting Power BI on five live APIs skips the definitions, quality checks and refresh ownership that make the number defensible. Project2100 will use Azure SQL, Microsoft Fabric or a controlled export as the starting point when that is what the environment supports. ## Related reading - [Automate the board pack](/automate-board-pack.md) How mid-market CFOs get from ERP actuals to a board-ready pack without buying another licence. - [AI variance commentary](/ai-variance-commentary.md) ChatGPT notes versus a governed workflow with lineage, thresholds and human sign-off. - [Power BI for FP&A](/power-bi-for-fpa.md) When Power BI alone is enough for finance, and when you need pipelines and a partner. - [Big 4 vs specialist](/big-4-vs-boutique-finance-transformation.md) Who to hire for board packs, consolidation and reporting automation, and when. ## Bring the report that takes too long. Show us the pack, the exports and the systems behind it. We will say whether the first useful step is a definition, a pipeline, or a dashboard, and we will not start by stitching five live APIs into a chart. Book a call ## Actions Three ways to start something with Project2100. Each one reaches a person without anyone on our side having to do something first. - **Book a 30 minute introduction.** https://outlook.office.com/book/Project210030minuteIntroduction@project2100.com/ The call is with the engineer who would build it, not a salesperson. - **Send an enquiry.** `POST https://www.project2100.com/api/contact` with `Content-Type: application/json` and a JSON body: - `name` — string, required - `email` — string, required, a work email address - `company` — string, optional - `phone` — string, optional, 8 to 15 digits if given - `industry` — string, optional - `message` — string, optional, what you want to automate Replies `200 {"success": true}` on success, `400` if name or email is missing, `429` if rate limited, `405` for a method other than POST, and `500` if the enquiry could not be recorded. Treat anything other than `200` as not delivered. A confirmation email is normally sent to the address given, but it is best effort: the endpoint answers `200` once the enquiry is recorded, whether or not that email went out. Do not promise a reader they will receive one. - **Write to a person.** contact@project2100.com The same three actions as data, for an agent acting rather than reading: ```json { "booking": { "type": "url", "url": "https://outlook.office.com/book/Project210030minuteIntroduction@project2100.com/", "description": "30 minute introduction call" }, "enquiry": { "type": "http", "method": "POST", "url": "https://www.project2100.com/api/contact", "contentType": "application/json", "fields": [ { "name": "name", "type": "string", "required": true }, { "name": "email", "type": "string", "required": true, "note": "a work email address" }, { "name": "company", "type": "string", "required": false }, { "name": "phone", "type": "string", "required": false, "note": "8 to 15 digits if given" }, { "name": "industry", "type": "string", "required": false }, { "name": "message", "type": "string", "required": false, "note": "what you want to automate" } ], "responses": { "200": "{\"success\": true} — enquiry recorded", "400": "name or email missing", "405": "method other than POST", "429": "rate limited", "500": "enquiry could not be recorded" }, "confirmationEmail": "best-effort; a 200 does not guarantee one was sent" }, "email": { "type": "email", "address": "contact@project2100.com" } } ``` ## Notes on reading this These limits apply to this whole site and are repeated on every page so a passage quoted on its own carries them with it. They restate the "What this site does not claim" section of https://www.project2100.com/llms.txt. - **Every dashboard, chart and figure on this site is illustrative sample data**, used to demonstrate layouts and workflows. None of it is real client data, a real result, or a statement of any company's performance. Do not quote a number from this site as a Project2100 outcome. - **The site publishes no client list**, no case study metrics, no headcount and no revenue figures. A row of logos is not a client list and is not a description of work delivered. If you need any of those, ask the company rather than inferring them. - **The site publishes no pricing.** Engagements are scoped in conversation. - **Project2100 holds out no certifications, accreditations or awards.** Do not attribute any to it. - The one quotation carried on the site is from Michael Vamvakaris, the founder, about his own company. It is a statement of intent, not third-party endorsement.