Data Quality: Significance and Consequences
Significance and Consequence of data quality
Most organizations are recognizing the importance of investing in a robust and scalable data management architecture to get good ROI on data spends. Whether it is via DataOps or Data Mesh, you need to identify your unique selling point by placing trust in the most important asset - DATA.
Whether you collate data, or buy it, you know your output is only as good as the confidence you have in the input - Garbage In, Garbage Out. The quality of data is quite significant in a data-driven organization, and to identify data anomalies at the foundation of the pipeline step, will ensure all downstream consumption of data will accurately provide the results that can significantly boost your business.
How do we define data quality? Simply put, is the data accurate, in the right format, reliable and consistent. Just by analyzing the condition of data on some parameters can isolate the problem areas, thus forcing you to evaluate at the origin rather than at the end of the data lifecycle. There are many ways of identifying data anomalies, thereby improving data quality, such as data monitoring and observability, and we’ve described that in great detail in our post.
Here, I would like to stress on 3 of the many reasons why quality plays a big role in a data-driven organization.
- Making sound decisions: If the data being consumed by various different organizational processes is clean, and valid, the output will then help in making critical decisions that prove to be sound and have reduced risk. For example:
- Improved customer experience and targeted marketing: With data that is correct and timely, you can interact and provide the best service to your customers with the information you have on file. How often are they reading the newsletters, what kind of material is driving them to click vs. glance through, are customers responding to your ads are just some of the important pieces of information that can help you drive more targeted marketing channels.
- Productive Data Engineering Team: Research finds that most data engineers are fighting fires with issues in data, writing static rule based scripts to catch anomalies which soon become outdated or need to be supported continuously, taking away time from other core data engineering roles. By diverting attention from identifying leaks in data to high-yielding work, the ROI is much significant.
Sure, good data does have a great impact, but it's often times taken for granted, because the opposite is more obvious - untrustworthy data can have tremendous consequences -
- Lost competitive advantage: With bad data, many profitable opportunities are simply overlooked or missed. Are you catering to the market and customers, with the right services and products that have an immediate buy-in? Are you speaking to the right audience, at the right time? If you’re not gaining insights by making good use of your assets, the competition is already ahead of the game.
- Decisions based on incorrect data affects reputation and can tend to create mistrust. Sectors with strict regulations, sanctions, trade need to be over cautious about mis-steps, sharing wrong information, overlooking fraudulent activities, or reaching out to the wrong customer base due to incorrect data. As recently as last week, a man was offered the COVID vaccine due to incorrect height and BMI calculation. Most likely, the data team hadn’t even anticipated the downstream use of such data to calculate BMI and plan vaccinations. It's more evident than before that Data Quality checks should not be done only at the level of few attributes but anomaly detection should happen for most attributes in your data set.
- Revenue loss: The bottom line for most businesses is to effectively use all the resources on hand to increase revenue. According to a Gartner research, “organizations believe poor data quality to be responsible for an average of $15 million per year in losses.” All the reasons above and many more directly impact the revenue. For instance, due to ineffective marketing, when a sales channel fails to convert, the revenue is directly impacted.
Telm.ai can help identify anomalies and inaccuracies in your data, saving you time, effort and money. It seamlessly injects into your pipeline step, becoming an integral part of your data architecture.
#dataquality #dataanomalydetection #dataobservability #datamonitoring
About the Author
Harsha Bipin, Technical Marketing @ Telm.ai, Software Engineer and a big proponent of mindful living :)
Data profiling helps organizations understand their data, identify issues and discrepancies, and improve data quality. It is an essential part of any data-related project and without it data quality could impact critical business decisions, customer trust, sales and financial opportunities.
To get started, there are four main steps in building a complete and ongoing data profiling process:
We'll explore each of these steps in detail and discuss how they contribute to the overall goal of ensuring accurate and reliable data. Before we get started, let's remind ourself of what is data profiling.
1. Data Collection
Start with data collection. Gather data from various sources and extract it into a single location for analysis. If you have multiple sources, choose a centralized data profiling tool (see our recommendation in the conclusion) that can easily connect and analyze all your data without having you do any prep work.
2. Discovery & Analysis
Now that you have collected your data for analysis, it's time to investigate it. Depending on your use case, you may need structure discovery, content discovery, relationship discovery, or all three. If data content or structure discovery is important for your use case, make sure that you collect and profile your data in its entirety and do not use samples as it will skew your results.
Use visualizations to make your discovery and analysis more understandable. It is much easier to see outliers and anomalies in your data using graphs than in a table format.
3. Documenting the Findings
Create a report or documentation outlining the results of the data profiling process, including any issues or discrepancies found.
Use this step to establish data quality rules that you may not have been aware of. For example, a United States ZIP code of 94061 could have accidentally been typed in as 94 061 with a space in the middle. Documenting this issue could help you establish new rules for the next time you profile the data.
4. Data Quality Monitoring
Now that you know what you have, the next step is to make sure you correct these issues. This may be something that you can correct or something that you need to flag for upstream data owners to fix.
After your data profiling is done and the system goes live, your data quality assurance work is not done – in fact, it's just getting started.
Data constantly changes. If unchecked, data quality defects will continue to occur, both as a result of system and user behavior changes.
Build a platform that can measure and monitor data quality on an ongoing basis.
Take Advantage of Data Observability Tools
Automated tools can help you save time and resources and ensure accuracy in the process.
Unfortunately, traditional data profiling tools offered by legacy ETL and database vendors are complex and require data engineering and technical skills. They also only handle data that is structured and ready for analysis. Semi-structured data sets, nested data formats, blob storage types, or streaming data do not have a place in those solutions.
Today organizations that deal with complex data types or large amounts of data are looking for a newer, more scalable solution.
That’s where a data observability tool like Telmai comes in. Telmai is built to handle the complexity that data profiling projects are faced with today. Some advantages include centralized profiling for all data types, a low-code no-code interface, ML insights, easy integration, and scale and performance.
Data Observability
Data Quality
Leverages ML and statistical analysis to learn from the data and identify potential issues, and can also validate data against predefined rules
Uses predefined metrics from a known set of policies to understand the health of the data
Detects, investigates the root cause of issues, and helps remediate
Detects and helps remediate.
Examples: continuous monitoring, alerting on anomalies or drifts, and operationalizing the findings into data flows
Examples: data validation, data cleansing, data standardization
Low-code / no-code to accelerate time to value and lower cost
Ongoing maintenance, tweaking, and testing data quality rules adds to its costs
Enables both business and technical teams to participate in data quality and monitoring initiatives
Designed mainly for technical teams who can implement ETL workflows or open source data validation software
Start your data observibility today
Connect your data and start generating a baseline in less than 10 minutes.
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Start your data observability today
Connect your data and start generating a baseline in less than 10 minutes.
