September 14, 2021

Navigating the Complexity of Data Quality

Today, all businesses are built on data. Whether it’s customer insights, performance metrics, or industry surveys, all of these data points come together to provide a clear picture of what your business is doing, how it’s doing it, and what it can do better. To get this right, you need high quality data that isn’t impaired by inaccuracies or lagging reports. 

Quality data leads to better decision making, improved customer targeting and customization, more effective marketing campaigns, and so much more that can build your competitive advantage. 

In this post, we’ll talk about what high quality data looks like, how it can be compromised, and the steps you can take to ensure the quality of your data. 

Let’s dive in. 

What do we mean by high quality data?

Data quality can be measured through a number of different factors: 

  • Completeness: All the essential fields in your database are filled. 
  • Consistency: All iterations of a piece of data are measured in the same way.
  • Accuracy: The data values are correct and closely reflect the reality of any results. 
  • Format: Data entry and category formats are consistent. 
  • Timeliness: The data is current and supports decision making in near real time.
  • Validity: The dataset follows any rules and standards set to allow for pattern recognition. 

All of these elements help ensure that your data is optimally supporting your decision making and planning when it comes to crafting strategies, building products, or communicating with your customers. 

Today’s data quality challenges are more complex than ever

While the parameters for high quality data are clear, the path to succeeding in this area has many obstacles. 

  1. Data explosion: As more and more transactions and interactions happen online, there is a massive volume of data being produced every second. This can be hard to sort through and interpret. 
  2. Data diversity: Evolving data types and formats across different deployment platforms lead to ever-changing data trust demands. Companies also need to be able to quickly adapt to changes from service providers. 
  3. Diversity in data use cases: Today’s data must satisfy a variety of different use cases, and that means that it’s collected from various sources. Establishing data trust is a key element here, and that’s not always easy to do.
  4. Data consumption: With more companies and organizations than ever before relying on data, there are much higher demands for high quality data. 
  5. Data speed: Data trust needs to be established at a faster rate to accommodate the speed of data collection across the information supply chain. Assessing data quality at a given point in time is no longer sufficient. 

To solve these challenges, there are third-party data classification and data quality solutions that can take the burden off of your teams and equip your decision makers with the best possible data.

Set yourself up for success with high quality data

At Data Sentinel, we offer a number of solutions that equip our customers with high quality data. These include the following: 

Data classification and inventory

As a first step, our solution scans the available data and classifies it into critical domains. It then assigns sensitivity classifications to determine the appropriate data security, access, and usage parameters. Once that’s done, it identifies data risk factors that are based on data classification, data jurisdictions, and any corresponding privacy laws and regulations. Lastly, it produces an accessible data inventory that includes the associated data type classifications and sensitivity. 

Data assessment and discovery for reference/master data quality

The scope of this solution includes leveraging high-speed data profiling and analysis processes to automatically identify and create data quality rules for both free-form text data as well as functional business rules from transactional data. The available rules include: 

  • Data standardization
  • Avoiding duplicates (i.e. both exact and fuzzy duplicates)
  • Flagging multiplicity (e.g. if multiple users have the same social security number)
  • Reasonability (e.g. identifying critical data value inconsistencies)
  • Completeness and structure of data elements

It also includes automatically generated data quality rules that can be implemented for on-going monitoring and remediation.

These tools and processes are designed to resolve data quality issues, enabling users to increase productivity and cut costs as a result. 

Data assessment and discovery for transactional data quality

In this space, our data quality processes pinpoint trends, patterns, and dependencies in data, revealing behavioral insights. This information then equips our customers to optimize their productivity and reduce risk. 

Our solution performs rapid discovery of nine primary classes of essential transactional data quality checks, without the need to write any code. These checks include: 

  1. Orphan and newborn check: Automatically discovers lists for each relevant column in the dataset and flags deviations from historical patterns.
  2. Anomalies: Automatically identifies natural microsegments of data and tracks anomalous records based on historical trends. 
  3. Record count reasonability: Checks whether the number of records in each microsegment is as expected, and whether the volume deviates from historical trends.
  4. Valid inter-column relationships: Automatically discovers business rules (e.g. IF statements) by using historical patterns of acceptable multi-column relationships. 
  5. Date consistency check: Determines whether the format of date column aligns with historical entries. 
  6. Bad data check: Uncovers the expected nature of each column and tracks if they are populated with an inconsistent data type. 
  7. Length check: Automatically learns string patterns and lengths in columns based on historical data. 
  8. Null check: Tracks patterns of nulls in every microsegment of data and identifies trends based on historical data to highlight outliers. 
  9. Custom rules: Includes customizable rule sets that meet specific customer requirements and use cases. 

Ensure the quality of your data

Keeping track of all the different data requirements can be a tough task. With Data Sentinel, you can deploy our solutions quickly and efficiently, without disrupting your operations or adding more burden to your teams. In fact, we can help free up time and costs, allowing you to focus on your core competencies. 


To learn more about how Data Sentinel can help your organization, book your demo today


September 14, 2021

Navigating the Complexity of Data Quality

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Today, all businesses are built on data. Whether it’s customer insights, performance metrics, or industry surveys, all of these data points come together to provide a clear picture of what your business is doing, how it’s doing it, and what it can do better. To get this right, you need high quality data that isn’t impaired by inaccuracies or lagging reports. 

Quality data leads to better decision making, improved customer targeting and customization, more effective marketing campaigns, and so much more that can build your competitive advantage. 

In this post, we’ll talk about what high quality data looks like, how it can be compromised, and the steps you can take to ensure the quality of your data. 

Let’s dive in. 

What do we mean by high quality data?

Data quality can be measured through a number of different factors: 

  • Completeness: All the essential fields in your database are filled. 
  • Consistency: All iterations of a piece of data are measured in the same way.
  • Accuracy: The data values are correct and closely reflect the reality of any results. 
  • Format: Data entry and category formats are consistent. 
  • Timeliness: The data is current and supports decision making in near real time.
  • Validity: The dataset follows any rules and standards set to allow for pattern recognition. 

All of these elements help ensure that your data is optimally supporting your decision making and planning when it comes to crafting strategies, building products, or communicating with your customers. 

Today’s data quality challenges are more complex than ever

While the parameters for high quality data are clear, the path to succeeding in this area has many obstacles. 

  1. Data explosion: As more and more transactions and interactions happen online, there is a massive volume of data being produced every second. This can be hard to sort through and interpret. 
  2. Data diversity: Evolving data types and formats across different deployment platforms lead to ever-changing data trust demands. Companies also need to be able to quickly adapt to changes from service providers. 
  3. Diversity in data use cases: Today’s data must satisfy a variety of different use cases, and that means that it’s collected from various sources. Establishing data trust is a key element here, and that’s not always easy to do.
  4. Data consumption: With more companies and organizations than ever before relying on data, there are much higher demands for high quality data. 
  5. Data speed: Data trust needs to be established at a faster rate to accommodate the speed of data collection across the information supply chain. Assessing data quality at a given point in time is no longer sufficient. 

To solve these challenges, there are third-party data classification and data quality solutions that can take the burden off of your teams and equip your decision makers with the best possible data.

Set yourself up for success with high quality data

At Data Sentinel, we offer a number of solutions that equip our customers with high quality data. These include the following: 

Data classification and inventory

As a first step, our solution scans the available data and classifies it into critical domains. It then assigns sensitivity classifications to determine the appropriate data security, access, and usage parameters. Once that’s done, it identifies data risk factors that are based on data classification, data jurisdictions, and any corresponding privacy laws and regulations. Lastly, it produces an accessible data inventory that includes the associated data type classifications and sensitivity. 

Data assessment and discovery for reference/master data quality

The scope of this solution includes leveraging high-speed data profiling and analysis processes to automatically identify and create data quality rules for both free-form text data as well as functional business rules from transactional data. The available rules include: 

  • Data standardization
  • Avoiding duplicates (i.e. both exact and fuzzy duplicates)
  • Flagging multiplicity (e.g. if multiple users have the same social security number)
  • Reasonability (e.g. identifying critical data value inconsistencies)
  • Completeness and structure of data elements

It also includes automatically generated data quality rules that can be implemented for on-going monitoring and remediation.

These tools and processes are designed to resolve data quality issues, enabling users to increase productivity and cut costs as a result. 

Data assessment and discovery for transactional data quality

In this space, our data quality processes pinpoint trends, patterns, and dependencies in data, revealing behavioral insights. This information then equips our customers to optimize their productivity and reduce risk. 

Our solution performs rapid discovery of nine primary classes of essential transactional data quality checks, without the need to write any code. These checks include: 

  1. Orphan and newborn check: Automatically discovers lists for each relevant column in the dataset and flags deviations from historical patterns.
  2. Anomalies: Automatically identifies natural microsegments of data and tracks anomalous records based on historical trends. 
  3. Record count reasonability: Checks whether the number of records in each microsegment is as expected, and whether the volume deviates from historical trends.
  4. Valid inter-column relationships: Automatically discovers business rules (e.g. IF statements) by using historical patterns of acceptable multi-column relationships. 
  5. Date consistency check: Determines whether the format of date column aligns with historical entries. 
  6. Bad data check: Uncovers the expected nature of each column and tracks if they are populated with an inconsistent data type. 
  7. Length check: Automatically learns string patterns and lengths in columns based on historical data. 
  8. Null check: Tracks patterns of nulls in every microsegment of data and identifies trends based on historical data to highlight outliers. 
  9. Custom rules: Includes customizable rule sets that meet specific customer requirements and use cases. 

Ensure the quality of your data

Keeping track of all the different data requirements can be a tough task. With Data Sentinel, you can deploy our solutions quickly and efficiently, without disrupting your operations or adding more burden to your teams. In fact, we can help free up time and costs, allowing you to focus on your core competencies. 


To learn more about how Data Sentinel can help your organization, book your demo today


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