Data Cleansing & Normalisation Services by StableLogic

Firstcom

telecommunications

Building the next generation of unified communications with Firstcom Europe

Firstcom Europe have grown rapidly in recent years, partly through acquisition.

As a result, data was held in multiple different databases, in different countries, and in completely different formats.

StableLogic delivered a project to give Firstcom insight into their business, their customers and their services.

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FMCG

Managing one of the biggest data network infrastructure roll-outs in the world with Danone

Danone engaged StableLogic to support the roll-out of their new data network infrastructure to over 700 locations globally.

Our data team developed advanced dashboards to manage the rollout, the business case commercials and the network performance before and after the project. As a result, Danone had detailed insight into their project and the positive impact it had across the organisation.

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Medical

Extracting the secrets to enterprise-level cost savings from years of data with Boston Scientific

Boston Scientific engaged StableLogic to develop insights into their internal IT corporate devices.

StableLogic built detailed analysis, using multiple data sources, to provide visualisations to the client.

As a direct result of those dashboards, Boston Scientific were able to realise tens of thousands of dollars in savings.

1. Data Audit & Profiling

We start by profiling datasets to understand their current state—accuracy, completeness, consistency, and structure. This step reveals duplicate records, missing values, and inconsistencies. By documenting strengths and weaknesses, we create a roadmap for cleansing that is targeted and measurable.

2. Duplicate Detection & Resolution

Duplicate entries distort reporting and increase operational costs. We apply rule-based and algorithmic matching techniques to detect duplicates, then merge or resolve them according to governance rules. This creates cleaner, leaner datasets with reduced redundancy.

3. Standardisation

Different systems often record values in inconsistent ways (e.g. dates, addresses, product names). We enforce uniform standards, ensuring data fields are aligned across platforms. This reduces confusion and makes integration seamless, so reports and analysis become more accurate.

4. Error Correction

Errors creep in through manual entry, system migrations, or integration failures. We detect anomalies and outliers, validate against trusted sources, and correct or flag them for review. The goal is to create datasets that are both accurate and transparent.

5. Normalisation

We re-structure data into standard formats, ensuring it can be easily integrated across different systems. This includes normalising text, numeric values, and categorical fields so that all records “speak the same language.” This enables interoperability and consistency at scale.

6. Validation & Governance

After cleansing, we implement checks to maintain quality. This includes validation rules, audit trails, and governance frameworks. By embedding controls into workflows, organisations ensure data remains clean and reliable long after the initial project is complete.

What are the risks of unclean data?

Poor-quality data leads to inaccurate reporting, flawed decision-making, wasted resources, and even compliance risks. It also damages trust in analytics and reduces efficiency across operations.

How often should data cleansing be performed?

Cleansing isn’t a one-off exercise. While a major clean-up may happen once, ongoing processes should be in place to monitor and maintain quality. Many organisations schedule quarterly or annual reviews.

Can cleansing be automated?

Yes. We use automation for tasks like duplicate detection, standardisation, and validation. However, some complex cases still require human review. Automation ensures scalability and consistency while humans handle edge cases.

What’s the difference between cleansing and normalisation?

Cleansing fixes errors and inconsistencies, while normalisation ensures data is structured and standardised for integration. Together, they produce datasets that are accurate and interoperable.

How do you maintain data quality over time?

By implementing governance frameworks, validation rules, and monitoring processes. This ensures new data entering the system remains consistent with established standards.

BIG DATA

Data Cleansing & Normalisation

Unclean data is a silent barrier to progress, causing errors, delays, and flawed decisions.

Duplicate records, missing fields, and inconsistent formats undermine trust in reporting and analytics.

FEATURED INSIGHT

4 Benefits of Bespoke Business Intelligence Software

Client Results (Update)

telecommunications

Building the next generation of unified communications with Firstcom Europe

Firstcom Europe have grown rapidly in recent years, partly through acquisition.

FMCG

Managing one of the biggest data network infrastructure roll-outs in the world with Danone

Danone engaged StableLogic to support the roll-out of their new data network infrastructure to over 700 locations globally.

Medical

Extracting the secrets to enterprise-level cost savings from years of data with Boston Scientific

Boston Scientific engaged StableLogic to develop insights into their internal IT corporate devices.

StableLogic approaches data quality systematically.

“These projects are a pleasure because clients see immediate results: fewer errors, smoother workflows, and a new sense of trust in their systems.”

Connect with the StableLogic Data Cleansing & Normalisation team.

Oscar Zhang

Anas Aslam

Sajid Rasool

Ash Gittens

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