Data Quality

Data quality is the degree to which an organization's data is fit for the use it is meant to serve, whether that is making a decision, triggering a process or feeding a model. It is not an absolute property of the data, but a relationship between the data and its purpose: a customer master list may be good enough to send an email campaign and, at the same time, useless for an accounting reconciliation.

It is assessed through a set of measurable dimensions, such as accuracy, completeness, consistency across systems and timeliness. Good-quality data describes reality well, is complete where it matters, doesn't contradict itself from one application to another and is up to date at the moment it is needed. When any of those dimensions fails, dashboards show figures nobody fully believes, and every department shows up to the meeting with its own version of the truth.

Sustaining it is not a project you finish once, but an ongoing discipline of measurement and correction. An analytics layer like Metrix helps expose these problems: by consolidating sales, inventory and customers in one place, it surfaces duplicate records, gaps and contradictions before they contaminate a decision.

Unlike data governance, which defines the policies and responsibilities around data, data quality is the operational result those policies are meant to ensure.

Data quality starts from an uncomfortable but necessary idea: most organizations make decisions on data that nobody has truly audited. As long as data flows in without friction, it is assumed to be fine, until a number doesn't add up in a meeting and the usual question comes up: which system is right? Treating quality as a measurable property, and not as an act of faith, is what turns data into a reliable asset.

The dimensions that make it up

Quality is not measured with a single number, but by breaking it down into dimensions that are assessed separately. The most widely used are:

• Accuracy: the data reflects the real-world value. A phone number that exists and gets answered, a tax ID (CUIT, in Argentina) that matches the company it claims to belong to.
• Completeness: the fields the process needs are filled in. A customer without an email address breaks an email campaign, no matter how correct the rest of the record is.
• Consistency: the same data matches across systems. If the ERP says an order has been invoiced and the CRM shows it as pending, one of the two is wrong.
• Timeliness: the data is up to date at the moment it is queried. A price list from six months ago is technically data, but it leads to the wrong decision.
• Uniqueness: each real-world entity appears only once. Duplicates inflate counts and cause the same customer to receive the same message three times.
• Validity: the data follows the expected format and rules. An impossible date or a code that doesn't exist in the master table is invalid data, even if the field isn't empty.

How it is measured and maintained

The first step is data profiling: scanning a dataset and pulling statistics for each field, such as how many null values there are, how many duplicates, which formats coexist and which ranges look suspicious. Based on that diagnosis, you define validation rules and an acceptable threshold for each dimension, because perfect quality doesn't exist and isn't worth chasing: you set a level that is good enough for the use. From there, the discipline becomes continuous, with deduplication, standardization and monitoring that alerts you when a quality metric degrades. Many of these problems are fixed upstream, in the ETL processes that move data between systems, not by patching the final dashboard.

Concrete example (Argentina, LATAM)

A wholesale distributor in Argentina builds its quarterly forecast by combining data from the ERP, the CRM and the sales reps' spreadsheets. When it consolidates them, it discovers that a large share of its customers are duplicated (Sociedad A appears as "Soc. A", "Sociedad A SRL" and "SOCIEDAD A"), that payment terms are missing for a good part of the portfolio and that several CUITs (Argentina's tax ID) were mistyped. Until that is fixed, any analysis of sales by customer or of inventory turnover starts from a false base. The data quality work (deduplicating, validating each CUIT against the tax registry and filling in payment terms) is what makes everything that comes after it usable.

Why it matters to a company in LATAM

In the region, it is common for a single company to run on a local ERP, standalone spreadsheets, a CRM and WhatsApp conversations, each with its own copy of the customer. Without a quality discipline, those copies drift apart and nobody knows which one to use. The cost is not abstract: campaigns that bounce because of mistyped email addresses, inventory that shows as available but isn't there, commissions calculated on duplicate sales and, above all, the erosion of trust in the company's own reports. When the team stops believing the numbers, it goes back to deciding by gut feeling and the entire investment in analytics goes to waste. That is why data quality is the foundation of a single source of truth and of any dashboard that expects to be taken seriously.

Data quality vs. related terms

Three concepts are often confused, but they answer different questions and complement each other:

In short, data governance sets the rules and the people responsible, integrity ensures the data isn't corrupted along the way, and quality measures whether the end result is fit for decision-making. Good governance is the condition for sustaining quality over time, not a synonym for it.

Common mistakes

The most frequent one is treating quality as a one-time cleanup: a major cleansing effort is made, everyone celebrates, and six months later the data has degraded again, because the root cause at the point of entry was never addressed. Another mistake is chasing perfection in every field equally, when most of the value lies in a few critical entities. And the third is measuring quality only when something breaks, instead of monitoring it continuously, which is the only way to catch degradation before it reaches a decision.