The Top 7 Problems With Data Quality

Modern technology and AI are essential for data-driven enterprises to maximize the value of their data assets. But they continually face problems with data quality. Data that is erroneous or incomplete, security issues, hidden data—the list goes on and on. Numerous surveys demonstrate the scope of financial losses caused by data quality issues across numerous industries.

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What are the most typical problems with data quality?

The main threat to the broad and successful application of machine learning is poor data quality. Data quality must be your top priority if you want to make technologies like machine learning work for you. Let's talk about some of the most prevalent data quality problems in this blog article and how to fix them.

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1- Duplicate data

Local databases, cloud data lakes, and streaming data are just a few of the sources of data that modern enterprises must contend with. They might also have application and system silos. These sources are likely to duplicate and overlap each other quite a bit. For instance, duplicate contact information has a substantial impact on customer experience. If certain prospects are ignored while others are engaged repeatedly, marketing campaigns suffer. The likelihood of biassed analytical outcomes increases when duplicate data are present. It can also result in ML models with biassed training data.

Duplicate and overlapping records can be controlled with the aid of rule-based data quality management. Predictive DQ automatically generates rules that are then continually refined by learning from the data itself. Predictive DQ helps supply continuous data quality across all applications by identifying ambiguous and exactly similar data, quantifying it into a likelihood score for duplicates.

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2- Inaccurate data

For highly regulated businesses like healthcare, data accuracy is crucial. Given the current experience, it is more important than ever to increase the data quality for COVID-19 and later pandemics. Inaccurate information does not provide you a true picture of the situation and cannot be used to plan the best course of action. Personalized customer experiences and marketing strategies underperform if your customer data is inaccurate.

Data inaccuracies can be attributed to a number of things, including human mistake, data drift, and data degradation. According to Gartner, worldwide data decay occurs at a rate of about 3% per month, which is quite concerning. Data integrity can be compromised while being transferred between different systems, and data quality might deteriorate with time. You can partially automate data management, but solutions specifically designed for data quality can give data that is considerably more accurate.

You may find data quality problems early in the data lifecycle and proactively correct them to fuel trustworthy analytics by using predictive, continuous, and self-service DQ.

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3- Ambiguous data

Even with thorough oversight, some errors can still occur in massive databases or data lakes. For data streaming at a fast speed, the issue becomes more overwhelming. Spelling mistakes can go unnoticed, formatting difficulties can occur, and column heads might be deceptive. These unclear data might cause a number of problems for reporting and analytics.

Predictive DQ reduces ambiguity by locating problems as soon as they appear by continuously monitoring with autogenerated rules. It provides top-notch data pipelines for trustworthy results and real-time analytics.

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4- Hidden data

The majority of businesses only utilise a portion of their data, with the remainder sometimes being lost in data silos or discarded in data graveyards. For example, customer data available with sales may not get shared with the customer service team, losing an opportunity to create more accurate and complete customer profiles. Missing out on possibilities to develop novel products, enhance services, and streamline procedures is caused by hidden data.

Trust predictive DQ for auto-discovery as well as the capability to uncover hidden associations (such as cross-column anomalies and "unknown unknowns"") in your data if hidden data is a data quality concern for your firm. Think about purchasing a Data catalogue solution as well. According to a study, best-in-class organisations are 30% more likely to have a specific data catalogue solution. concludes a recent survey.

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5- Inconsistent data

When working with various data sources, it's conceivable that the same information will have discrepancies between sources. The differences could be in formats, units, or occasionally spellings. The introduction of inconsistent data might also occur during firm mergers or relocations. Inconsistencies in data have a tendency to accumulate and reduce the value of data if they are not continually resolved. Organizations who are heavily focused on data consistency do so because they only want reliable data to support their analytics.

When data changes, the continuous DQ automatically profiles datasets and highlights the quality problems. A thorough dashboard assists DataOps in swiftly prioritising triage by impact ranking. Data pipelines only deliver reliable data, and adaptive rules continuously learn from the data to ensure that inconsistencies are resolved at the source.

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6- Too much data

While we emphasize data-driven analytics and its advantages, a data quality problem with excessive data does not appear to exist. Though it is. There is a risk of getting lost in an abundance of data when searching for information pertinent to your analytical efforts. Business users, data analysts, and data scientists devote 80% of their work to finding and organizing the appropriate data. With an increase in data volume, other problems with data quality become more serious, particularly when dealing with streaming data and big files or databases.

We offer the solution if you are having trouble making sense of the enormous volume and variety of data flowing from numerous sources. The predictive DQ can scale up easily and deliver continuous data quality across many sources without moving or extracting any data. You don't need to worry about too much data thanks to fully automatic profiling, outlier detection, schema change detection, and pattern analysis.

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7- Data?Downtime

Data is the driving force behind the decisions and operations of data-driven businesses. However, there may be brief periods when their data is unreliable or unprepared (especially during events like M&A, reorganizations, infrastructure upgrades, and migrations). Customer complaints and subpar analytical outcomes are only two ways that this data unavailability can have a significant impact on businesses. The research found that data engineer spends roughly 80% of their time updating, maintaining, and guaranteeing the integrity of the data pipeline. In order to ask the next business question, there is a high marginal cost due to the lengthy operational lead time from data capture to insight.

Schema modifications and migration problems are just two examples of the causes of data downtime. Data pipelines can be difficult due to their size and complexity. Continuous data downtime monitoring and automated ways to reduce it are crucial.

Organizations also suffer from unstructured data, incorrect data, redundancy in data, and data transformation errors in addition to the aforementioned problems.