Why is data quality (still) so important? (2023)

The first thing you learn when you start studying Data & Analytics?"Garbage in, garbage out"!

Believe it or not, this is the first thing any data student would say if you called him or her in the middle of the night and asked about the most common data pitfalls.data products.
This common expression is used to clarify one of the most well-known paradigms:If you don't have the high-quality data you need, you won't get high-quality models and analytical solutions that are highly accurate and relevant to your business problem(s).

High data quality is one of the most important success factors for high-quality analytical projects. It is the basis for all further development of a use case. If you don't have good data, you don't even need to start your project. By ensuring high quality data, you have a good chance that your project will result in a high quality result.

Why is data quality more important than ever?

But when every protected already knowsBecauseIs it more important than ever to talk about this topic?

1. OTotal amount of data collectedand saved by the industrydoubles every 1.2 years
2. Every two days we create so much informationlike we did from the beginning of time until 2003
3. Oannual cost of data quality issuesin the US was estimated at $3.1 trillion in 2016
4. OCost of resolving business issues caused by incorrect datait is estimated that they represent on average 15% to 25% of a company's annual turnover.

For this reason! The topic never gets old - today the problem of assessing the quality of these huge amounts of data is even more topical than it used to be. Ignoring this can result in significant costs and downstream business issues.

What does data quality really mean?

Depending on the context and use of the dataYou may wish to define other quality criteria and requirements.. The usual criteria are:

• present:Is your data up to date and is the required period available? (for example, do you have the information available so far?)
• Completeness:Is your data complete or does it contain missing values ​​or even missing data records? (for example, is a customer's address missing?)
• Precision:Is your data correct and accurate and does it reflect reality? (for example, does a customer's zip code exist and match the location?)
• Consistency:Is your data consistent across different data sources? (for example, does the address in your CRM system match the address in your sales tracking system?)
• Validity:Is the data in the expected format? Are business rules followed? (for example, is a phone number saved with the country code?)
• Singularity:Are there duplicates in the database? (for example, duplicate customer entries due to name changes due to marriage)

There are many other criteria you might want to check in your day-to-day life, but that's about it.too subjective for your companyand probably even the business use case you want to cover. Under these circumstances, it is always difficult to define (general) quality metrics. But in the end, you really need to consider your specific needs.Monitoring and improving data quality in your organization starts with a specific definition of quality and its metrics.Combined with the right tools and technologies to assess data quality, track quality issues, and finally implement processes to mitigate those issues, it results in higher data quality!

What are the biggest challenges that will cross your path to higher data quality?

Sounds easy? Yes, right - but unfortunately there is asome challenges you will face in terms of data quality. From our point of view, the three most important are the following:

Challenge #1: Define Quality Metrics

One of the biggest challenges is certainly thatDefinition of quality indicators.What does it mean when you have a dataset with high quality data? Would you like to choose a generic assessment that says exactly which requirements were met and to what extent? Or do you prefer a flexible one that weighs quality against font features? Or even the specific requirements per use case? All sides have their pros and cons! It depends entirely on the data sources you use, the level of automation in your data collection process, and ultimately the broader topic.data officeand its implementation in your organization.

Challenge #2: Definition of quality rules

Second, defining quality rules can be challenging: defining data quality requirements as a strict set of rules makes assessing data quality a lot easier, but has some pitfalls. If there are new requirements or significant changes in the quality of the raw data, you'll have a lot of work to adapt your rules accordingly. This is certainly the way forward, but over time it makes sense to address data quality with more flexible assessments (based on statistical indicators) or evenInstall machine learning modelsto assess data quality (for example, to predict the expected value of a missing value or for verification purposes).

Challenge #3: Define a quality process

Finally,the required degree of automation of your data quality assurance processcan be difficult to define. It depends on how specific your needs are, how much data you are manipulating for your use case, and whether the data will be reused. Let's talk about oneData Quality Solution for a Fully Automated Data Platformwith gigabytes of data processed daily from multiple API-based data sources used for multiple reporting and data science use cases? Or do you want to review manually collected data from a process where you want to reduce scrap in a manufacturing pipeline?Full automation requires more development time, but can be worth it if you save a lot of time for multiple peoplein your daily work environment.

How can you ensure high data quality in your company?

AsNow can you ensure that expected standards are met in your organization?

First,Start by defining solid quality metrics that meet your needs. Combined with quality requirements, you can assess the current quality of your data.

Next,Define roles and processes around quality initiatives.You need people to take care of the data quality solution itself and the data sources and their requirements. Installing the right processes around the solution keeps everything organized and compliant.

now you readyEstablish a monitoring system to assess the quality of your data over time and track issues as quickly as possible. Finally, you're ready to mitigate quality issues and improve the quality of your data in the long run.

And to make your initiative really successful, make sure you:

• Create a flexible and transparent systemthat is open to iterative improvements of all essential aspects. Be it quality metrics, degree of automation or the intelligence level of your rule.
• involve expertsabout data governance and technical implementation, as well as data engineers and data scientists.
• And finally:Starting!It's time to reverse the common saying:"High value inside, high value outside!"

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Author

Vera M.