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Data governance is the set of rules and responsibilities that decides where each piece of data is held, who owns it, how it is defined and which quality rules it must meet. Data quality work puts that into practice: records are measured, duplicates and errors are cleaned up, and entry rules are added so the same mistakes do not return.
The same customer appears under three different names, one product carries different codes in the warehouse and in sales, and marketing and finance each have their own count of "active customers". A new reporting tool or ERP will not fix that; it simply shows the wrong answer faster and more attractively. Meanwhile invoices go to the wrong account, stock is counted twice and the same person receives a campaign three times.
Digital Bridge maps your data, measures its quality, cleans up the faulty records and gives every data set an owner. We are not here to sell you another piece of software. The goal is to make the data in the systems you already have reliable, and to put entry rules, regular measurement and clear responsibilities in place so it stays that way. The result: invoices go to the right account, stock is counted once and reports can be trusted again. To find out where your data stands today, request a data quality assessment.
Where each piece of data is created, where it is copied to and which reports it feeds is mapped end to end. Spreadsheets and side lists kept outside the official systems come to light as well.
Terms such as "active customer", "net sales" or "stock on hand" get a written, organisation-wide definition, so every department answers the same question with the same number.
Each data domain, such as customers, products or employees, gets an owner and a steward. Requests to create, correct or access records follow a defined approval flow.
Spelling variants, missing fields and duplicate records are identified, and merge decisions are taken together with the business team. Company names, addresses and tax numbers are brought into one standard format.
Completeness, uniqueness, validity and consistency are measured and reported at regular intervals. When a field drops below the agreed level, the person responsible for that data is alerted.
Which system holds which personal data, for what purpose and for how long, documented in line with Turkey's data protection law (KVKK). For the full compliance programme, see data protection compliance.
We review your systems, data flows and the reports you rely on, and list the priority data domains and the problems your teams already know about.
We measure the selected domains and put numbers on it: how many records are faulty and what that means for invoicing, stock or reporting.
We clean the data, then add mandatory fields, format checks and duplicate warnings so the same errors do not creep back in.
Data stewards are appointed, a regular quality report is set up and the process is handed over to your in-house team.
Picture a company getting ready to move to a new ERP. The same customer sits in the old system as "ABC Ltd.", "ABC Limited" and a third account with no tax number, and the balance is split across all three. Migrate the data as it is, and the new ERP shows three separate customers and the wrong balance from day one. The project finds the duplicates by tax number, name and address, decides with the finance team which record survives, and merges the balances. Clean data goes into the new system, and because the tax number becomes a mandatory field, the same error does not reappear in new records.
Before a BI, demand forecasting or AI integration project, we recommend measuring your data quality. A model trained on bad data automates bad decisions, and because the model gets the blame, the real cause can go unnoticed for a long time. Clean data is a solid starting point for a data warehouse and BI dashboards.
Further reading: how to set up a data governance framework, how to clean up duplicate records, a guide to master data management (MDM) and data catalogs and data dictionaries. Browse every article in our data and analytics guides.
Since 2013, Digital Bridge has built ERP, CRM, warehouse and field service software for businesses, so we know from the developer's side how data goes wrong inside these systems. Every project starts with a review of your systems and data flows, and scope, phases and cost are agreed in a written proposal. To move clean data correctly into a new system or between systems, see our API integration service.
Data governance is the set of rules and responsibilities that decides where each piece of data is held, who owns it, how it is defined and which quality rules it must meet. In practice it means a data inventory, a data dictionary with shared definitions, an owner and steward for each data domain, and a defined approval flow for creating, correcting or accessing records.
Data quality is measured with indicators such as completeness, uniqueness, validity and consistency. For the selected data domains, we put numbers on how many records are faulty and what that means for invoicing, stock or reporting. Measurement is repeated at regular intervals, and when a field drops below the agreed level, the person responsible for that data is alerted.
Spelling variants, missing fields and duplicate records are identified, and merge decisions are taken together with the business team. Company names, addresses and tax numbers are brought into one standard format. After the clean-up, mandatory fields, format checks and duplicate warnings are added to the system so the same errors do not creep back into new records.
Typical triggers are starting a BI or reporting project, preparing for an ERP migration, bringing data together after a merger, and deduplicating customer records in the CRM. Preparing data for AI or demand forecasting, aligning branch and dealer data to one set of rules, and getting ready for audits or statutory reporting are also common starting points.
We work in four steps: a review of your systems, data flows and reports; quality measurement and a report on the selected domains; cleansing plus entry rules added to the system; and finally appointing data stewards, setting up a regular quality report and handing the process over to your in-house team. The aim is reliable data in the systems you already have, not new software.
A personal data inventory is part of data governance. The work documents which system holds which personal data, for what purpose and for how long, in line with Turkey's data protection law (KVKK). For the rest of the compliance programme, such as privacy notices and retention policies, it can be combined with our data protection compliance service.
In most cases, no. Measurement and cleansing work on the data already in your ERP, CRM and spreadsheets, and entry rules such as mandatory fields, format checks and duplicate warnings are added to the systems you already use. Whether a separate master data management tool is worthwhile is decided together at the end of the assessment, based on your data volumes and number of systems.
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