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In today’s data-driven world, businesses in all industries need data to make better and more informed decisions.

However, the actionability of data is determined by its quality.

Poor data can lead to inaccurate modelling and then conclusions, wasted resources, and missed opportunities.

Investing in data providers and the quality of their data is therefore a must for all organisations looking to leverage data for business growth and success.

What is Data Quality?

Data quality is an evaluation of the extent to which a data set is appropriate for fulfilling its intended objective in terms of factors such as accuracy, completeness, consistency, and relevancy.

High-quality data meets these parameters.

Poor data quality doesn’t.

High-quality data is data that meets its intended purpose.
Poor-quality data is usually a result of duplicated data, incomplete data, inaccurate or ambiguous data, or simply human error.

Low quality data can often be turned into high quality ones by applying proper techniques – often statistical methods – that can lower the probability to deduce inaccurate or incorrect facts.

Creating projects, actions, and investments start with data. So laying the foundations of these decisions on top data empowers businesses with the right, and accurate information and insights they need to make effective and efficent decisions.

On the other hand, basing your decisions on poor-quality data can be a recipe for failure — making data quality assessments vital for businesses across all industries.

Why is Measuring Quality Important?

Quality analysis helps businesses understand whether their insights are steady enough to build their decisions on — helping you identify any shortcomings, issues, or mistakes. Wrong data or poor-quality data will, more often than not, lead to incorrect analysis, inaccurate predictions, and ultimately, bad decision-making.

Take the finance industry as an example. Inaccurate data can have disastrous effects, leading to terrible investment decisions or miscalculations of risk.

In retail, poor quality data can lead to incorrect inventory management, or ineffective marketing campaigns, both of which result in lost sales opportunities.

In the tourism industry, flawed data can result in negative customer experiences, a decrease in customer sentiment, and budgeting mistakes.

All the departments of the company or organisation can benefit from valuable data: management, sales, marketing, logistics, and more.

How To Ensure Data Quality: 4 Best Practices for Collecting High-Quality Data

Improving data quality is an ongoing process that requires a structured approach and common company guidelines.

The following are some of the recommended steps you can take to guarantee top quality data:

  • Define data quality standards and policies: Establish clear guidelines for data quality that align with the organisation’s objectives and requirements.
  • Implement data quality checks: Develop a data quality monitoring system to identify and correct data errors. This can include automated data validation tools, data profiling, and data cleansing.
  • Ensure data security: Data security is a critical aspect of data quality. Protect data from unauthorised access, manipulation, or theft through robust security measures.
  • Provide data governance: Ensure that there is a system in place for managing data across the organisation. This includes data ownership, data stewardship, and data lineage.

How The Data Appeal Company Structured Its Data Quality Process

The Data Appeal Company provides POIs and location-based KPIs to financial institutions, consumer goods brands, retail and real estate companies, and tourism destinations.

Our data supports institutions and organisations in getting actionable insights, making the right decisions, and investing strategically.

At Data Appeal, we know quality data matters. This is why we’ve set up our own data quality process, which enables us to deliver up-to-date, accurate, and reliable data by:

  1. Excluding or regulating data that is not consistent with the statistical trend of their historical series, i.e. identification of outliers attributable to ingestion errors
  2. Harmonising data of various natures to place them on the same reference system, both temporally and spatially
  3. Eliminating data that relates to points of interest which are not accurately geolocalized, to exclude problems of spatial grouping
  4. Removing duplicate or incomplete data, something that’s highly likely when ingesting large quantities of data

In conclusion, in the words of our data team at The Data Appeal, “Data quality is what makes a database remarkable and reliable.”

Don’t let poor-quality data hinder your business decisions. Make sure your data is clean, accurate, and up-to-date.

Explore Our POI & Location Data

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