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Data observability & data quality

Trusted, good quality data, from source to decision

Amplifi helps organizations build the data quality and observability practices and capabilities for accurate, complete and reliable data, and to catch problems before they reach a dashboard, a decision or an AI model.

We combine decades of data quality delivery experience with modern observability practices, helping clients move from reactive firefighting to sustainable, continuously monitored data trust.

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300+

Global clients

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a critical capability

Business decisions, AI and analytics all start with trusted data

Data quality has always mattered; what’s changed is what depends on it.

As the scale of automation, analytics and AI has escalated, so have the possible consequences of bad quality data. Where before bad data might have delayed a report, the impact has now become a flawed model output, an incorrect automated decision, or an action taken by an agent without a human in the loop to catch the error.

Concurrently, data ecosystems are vast and only growing in complexity; data moves faster, across more systems, through more pipelines, with more consumers depending on it. While traditional data quality approaches can tell you if your data is right, they don’t tell you whether something has broken, drifted or gone stale before it causes damage, which is where observability takes center stage.

Observability = Visibility

Continuous monitoring of data health across its lifecycle (freshness, volume, schema, lineage and distribution), so you know when something changes, breaks or drifts, often before anyone

Quality = Outcome

The standards, rules and controls that determine whether data is accurate, complete, consistent and fit for purpose.

Quality sets the bar. Observability tells you if you're clearing it.

Together, they feed into the trusted data foundations that good decision-making, AI readiness and confident analytics all depend on.

Business challenges

How are observability and quality challenges showing up in your business?

Teams don't trust the reports, dashboards or AI outputs in front of them, and often work around issues rather than raising them. With no consistent way to demonstrate data is fit for purpose, and different teams using different definitions for the same term, confidence in shared numbers erodes.

Data problems are typically found after a report has failed, a customer has been affected, or an AI model has produced an unreliable output, as there's no visibility into pipeline health to catch silent failures early. Remediation happens reactively, after the damage is done.

Data quality issues persist because it's unclear who owns them, who should fix them, or where they should be escalated, and governance frameworks often exist on paper without being embedded into day-to-day operations. Quality improvement becomes a one-off project rather than a sustained capability, so standards slip once the project ends.

AI pilots stall or fail to scale because underlying data is inconsistent, incomplete or ungoverned, and teams can't confidently identify which datasets are safe to use for AI, analytics or automation. Investment in AI platforms and models continues to outpace investment in the data foundations needed to support them.

our clients

Data observability and quality with Amplifi

Our approach combines deep data quality heritage with modern observability practice. We work with organizations at every stage of maturity, from those establishing data quality standards for the first time, to those layering observability onto an existing quality program to support AI and analytics at scale.

We tailor every engagement to your data landscape, your priorities and your level of AI ambition; whether that's a rapid assessment to build a baseline, targeted remediation of a priority issue, or a long-term managed capability that keeps pace with a growing, more complex data estate.

Which leading brands have trusted Amplifi with their data and AI?

Guide download

5 tips for better Data Quality

Build trust in your data with 5 practical Data Quality tips to improve accuracy, engage your teams and embed sustainable data quality processes that drive better business decisions.

our approach

A best-practice approach

Data quality is not a one-time fix; it's an ongoing, cyclical process that transforms raw data into a strategic asset for your business. Our approach involves running your data against a set of rigorous standards, generating actionable insights to pinpoint areas for improvement.

What sets us apart is our commitment to continuous enhancement. Data is dynamic, and business needs are evolving as fast as data landscapes: values change, systems evolve, and new data is introduced. Our process is designed to adapt alongside your business, ensuring your data remains accurate, reliable and trustworthy.

01

Specific Issue Resolution

We identify the root cause and full extent of your data quality issues, then build a plan to fix them tailored to your business, skills and technology. A governance framework sets clear ownership and escalation throughout. Includes a defined data standard to benchmark against, data quality reports and analytics dashboards, and bespoke data quality rules built in alignment with your business rules.

02

Data Quality Capability Enablement

We give you the tools, skills and processes to remediate data quality issues yourselves. Through discovery, design and build, you gain the capability and governance structure to keep improving long after our engagement ends. Includes training and change management support, technology selection, and a governance framework for remediation and escalation.

03

Data Quality as a Service

Looking for additional support? DQaaS is a bespoke, cloud-based platform that takes the stress out of running data quality day to day, with issue reports, an analytics dashboard and KPIs to track progress, plus direct access to our experts via a ticketed service portal.

Featured Case studies

Troy: How better Data Governance and Quality helps meet business goals

Amplifi helped Troy rapidly become a company that not only understands the value of their data, but harnesses it to help achieve their company goals such as growth, efficiency and innovation.

insights

Further reading

Opinion
Chicken or Egg? A Data Paradox A decade later, the data quality debate rages on: govern first, or fix later? In this unedited reprint of a 2015 article, Stuart Squires, SVP Strategy & Consulting at Amplifi, explores the enduring paradox of data governance through a vivid, relatable analogy – and invites reflection on how far we’ve really come.

FAQs

You ask, we answer.

The questions we hear most from data and AI leaders, and how we approach them.

Data quality defines the standards a dataset needs to meet: accuracy, completeness and consistency. Data observability is how you continuously monitor whether your data and pipelines are healthy, so you know when something has changed, broken or drifted. You need both: quality sets the bar, observability tells you whether you’re clearing it.

Most likely, yes, if you’re scaling AI or analytics. A quality program without observability tends to be reactive; issues get caught after they’ve caused damage. Observability adds the continuous visibility that lets you catch and fix problems before they reach a report, a decision or an AI model.

AI amplifies the consequences of poor data quality. A flawed input can now drive an automated decision or agent action with no human check in between. Establishing trusted data quality and observability capabilities is one of the most direct ways to improve the reliability and scalability of AI initiatives.

It depends on your current stack and maturity. We start with an assessment of what you have and recommend the minimum viable path to trusted data. That might mean better use of existing tools, a new observability platform, or a phased combination of both.

Both are options. Many clients start with an assessment or a specific issue resolution engagement, then move into an ongoing managed service as their data estate and AI ambitions grow. We design the approach to fit where you are.

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