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5 Steps to Collect High-quality Data

Obtaining good quality data can be a tough task. An organization may face quality issues when integrating data sets from various applications or departments or when entering data manually.

Here are some of the things a company can do to improve the quality of the information it collects:

1. Data Governance plan

A good data governance plan should not only talk about ownership, classifications, sharing, and sensitivity levels plus also follows in detail with procedural details that outline your data quality goals. It should also have the details of all the personnel involved in the process and each of their roles and more importantly a process to resolve/work through issues.

Data governance can be thought of as the process of ensuring that there are data curators who are looking at the information being ingested into the organization and that there are processes in place to keep that data internally consistent, making it easier for consumers of that data to get access to it in the forms that they need.

2. Data Quality Guidance

You should also have a clear guide to use when separating good data from bad data. You will have to calibrate your automated data quality system with this information, so you need to have it laid out beforehand. This step also involves validating the data so that, before it can be further processed, there is a level of surety about the data and an estimate about how much work it will take to make sure that data meets minimal standards.

3. Data Cleansing Process

Data correction is the whole point of looking for flaws in your datasets. Organizations need to provide guidance on what to do with specific forms of bad data and identifying what’s critical and common across all organizational data silos. Implementing a data cleansing manually is cumbersome as the business shifts, strategies dictate the change in data and the underlying process. Data quality guidance and data cleansing are frequently done together, not only to make sure that the data is consistent but also to raise flags when data is inadequate to the needs of the organization.

4. Clear Data Lineage

With data flowing in from different departments and digital systems, you need to have a clear understanding of data lineage – how an attribute is transformed from system to system interactions and provide the ability to build trust and confidence. Data lineage (also known as provenance) is metadata that indicates where the data was from, how it has been transformed over time, and who, ultimately, is responsible for that data.

5. Data Catalog and Documentation

Improving data quality is a long-term process that you can streamline using both anticipations and past findings. By documenting every problem that is detected and associated data quality score to the data catalog, you reduce the risk of mistake repetition and solidify your data quality enhancement regime with time. Data catalogs are also increasingly tied into semantification, the process of extracting meaning, relationships, and dimensional analysis from the incoming (or ingested) data. 


As stated above, there is just too much data out there to incorporate into your business intelligence strategy. The data volumes are building up even more with the introduction of new digital systems and the increasing spread of the internet. For any organization that wants to keep up with the times, that translates to a need for more personnel, from data curators and data stewards to data scientists and data engineers. Luckily, today’s technology and AI/ML innovation allow for even the least tech-savvy individuals to contribute to data management at the east. Organizations should leverage these analytics augmented data quality and data management platforms to recognize immediate ROI and longer cycles of implementation.