We all know that data is the lifeblood of an organization. And for IT operations, data is the key to keep things going. However you could have all the data in the world, but if it’s poorly managed, it’ll be useless to you and your organization. Consider this from a recent IDC report:

“IDC believes that effective data management and analysis optimizes service delivery by consolidating an ever-expanding universe of data types; unifying the data so that it is of value across the enterprise; fostering an evidence-based service delivery environment; and safeguarding privacy and security at the enterprise level.”

– Alison Brooks, IDC research vice president

So, what happens when your data is poorly managed and messy? Poor data quality can lead to inaccurate analysis, misaligned strategies, and lost opportunities. Therefore, it is essential to define data quality and follow data quality best practices to ensure that the data is accurate, complete, and consistent. In this blog post, we will explain data quality best practices and how to define data quality.

IT Operations Data Quality Best Practices

Data quality best practices are guidelines that IT operations leaders and businesses can follow to ensure that their data is accurate, complete, and consistent. Take a look at these top five best practices:

1. Data Profiling: Data profiling involves analyzing data from different sources and identifying any errors, inconsistencies, or missing data that could affect the quality of insights drawn from it. Data profiling helps identify patterns and relationships in the data, enabling businesses to understand their customers and business better.
2. Data Cleansing: Data cleansing involves identifying and correcting errors, inconsistencies, and missing data. Data cleansing ensures that the data is accurate, complete, and reliable. By removing incorrect or incomplete data, businesses can prevent it from influencing their analysis and decision-making.
3. Data Governance: Data governance involves managing the availability, usability, integrity, and security of data. Data governance establishes policies, procedures, and controls to ensure data is used appropriately and protected from unauthorized access or use. Data governance also establishes roles and responsibilities for data management and ensures that the organization complies with relevant regulations and standards.
4. Data Integration: Data integration involves combining data from different sources to create a unified view of the data. Data integration ensures that data is consistent and up-to-date across different systems. By creating a standard data model, businesses can analyze data more effectively and make better-informed decisions.
5. Data Quality Metrics: Data quality metrics are used to measure the data quality. These metrics should align with the data quality requirements and be measurable, achievable, and relevant. By establishing data quality metrics, businesses can monitor the effectiveness of their data quality program and make continuous improvements.

How to Define Data Quality

Businesses need to identify the key data elements and their characteristics to define data quality. The four key attributes of data quality are:

1. Accuracy: Accuracy refers to the degree to which data reflects the actual state of affairs. Accurate data is free from errors, inconsistencies, and biases.
2. Completeness: Completeness refers to the degree to which data is complete and includes all necessary information. Complete data is free from missing or incomplete data.
3. Consistency: Consistency refers to the degree to which data is consistent across different sources. Consistent data uses standard formats, field names, and data entry conventions.
4. Relevance: Relevance refers to the degree to which data is relevant for its intended use. Relevant data is aligned with business objectives and is useful for making informed decisions.

By defining these key characteristics, businesses can establish clear expectations for data quality and ensure that everyone in the organization understands the importance of data quality.

Getting started with data quality

Messy data won’t do anyone any good. Data quality is crucial for businesses and IT operations leaders to make informed decisions, improve operational efficiency, and enhance customer satisfaction.

For IT ops leaders, data quality best practices include data profiling, data cleansing, data governance, data integration, and data quality metrics. By following these best practices, businesses and IT Ops pros can ensure that their data is accurate, complete, and consistent.

Here’s the other big piece of advice. Do not wait until your data quality starts to be impacted. Once you start noticing problems in the data engine, you might already be fighting an upstream battle. TFORM’s data experts know the difference between clean and usable data and data sets requiring fine-tuning. Reach out to our team today to ensure your business and IT operations people have the highest quality data to operate in an optimal environment.