How to Automate Data Monitoring: 3 Key Areas to Streamline

For 62% of businesses, a lack of agility in data processes hurt their response to changing business needs. A practical solution to this challenge is to automate data monitoring to help companies improve the customer experience and employee productivity. 

But how can a business convert its current data monitoring method to an automated one? 

Understanding the advantages of automated data monitoring and the simplicity of its integration can lead a business to become entirely data-driven. We’ll take a look at the areas a company can implement automation.

 But first, let’s understand the benefits of automated data monitoring.

Key Takeaways

  • The benefits of automated data monitoring include fewer mistakes, which affect the customer experience, employee productivity, and the business’s reputation.
  • Other benefits include identifying and handling outliers as well as structuring data expediently.
  • At the time of data entry, an automated data check verifies the integrity of the data for completeness, uniqueness, and conformity.
  • When transferring the data, an automated data check verifies the quality of the data, namely its timeliness, consistency, and accuracy.
  • Even when there is no event affecting the data, software solutions can continuously monitor the integrity and quality of the data. 

Why Automate Data Monitoring?

There are plenty of reasons to automate your data monitoring. 

Data Comes in Quick and Large Quantities

Perhaps there was a time when a few employees could handle processing new data. However, since technology has simplified data entry, companies quickly receive a lot of data and the increased workload leads to more likelihood of human error.

Mistakes Cost Money

One-third of businesses acknowledge that poor data quality negatively affects customer experience, customer trust, and the company’s reputation. 

Business Standards Change

Even if new data is correct, business rules for data can evolve, and someone will have to adjust the data to the new standard retroactively. 

There are Statistical Outliers

Workers may not detect hidden data quality issues such as transaction outliers. A transaction outlier may pass the data integrity test, but it is statistically different from the transaction set.

Outliers are problematic because they can cause analytics to miss noteworthy findings or distort actual results. Such scenarios require advanced logic for identification and handling.

Automation Saves Time

The process of understanding and assigning business rules to data sets can be time-consuming. Since automation uses artificial intelligence and machine learning (AI/ML) and requires minimal or no coding, time spent on data processing is significantly reduced. One estimate shows that automation can reduce a nine to twelve-month data entry process to one to two weeks.

statistics showing areas where poor data quality negatively impacts a business

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Areas to Automate Data Monitoring

We covered the first part of leveraging automated data monitoring: understanding its value. Next, let’s break down the points in your company’s data monitoring process to streamline.

1. At the Time of Data Entry

Companies can enter their data on-premises or in the cloud. While we will use the cloud example in this section, the principles also apply to on-premises handling.

Data added to the cloud can reside in data lakes. Data lakes are handy for storing large quantities of data (structured or unstructured) so businesses can apply machine learning analytics against them. The analytics, in turn, help companies make data-driven decisions. 

What kind of automation can occur at the time of data entry? Here are three automated checks:

  • Completeness – Are there fields with missing or incomplete values? For instance, some users may not provide their full last name out of privacy issues. Also, are email entries in the correct format (e.g., missing “@” sign or domain extensions)? 
  • Uniqueness – Are there duplicate entries in the database? Duplicate entries include completely identical rows and partial duplicate values from different rows. 
  • Conformity – Is the data type, size, or format correct? For example, there may be a weight field. Is the weight in kilos or pounds? If the field uses the decimal format, how many places can the value go (e.g., 208.1 vs. 208.13)? 
expedient lake-level validation is a driver to automate data monitoring

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2. When Transferring the Data

When data resides in the cloud, there is a point where it exits the data lake as unstructured data and enters the data warehouse as a structured one. Here are four types of checks to execute:

  • Fundamental data integrity – Repeat the checks discussed in step one, namely completeness, uniqueness, and conformity.
  • Timeliness – Is the data expired? Is there newer data available? Timeliness is a vital characteristic since lagging data can result in people making wrong decisions.
  • Consistency –The data must be in sync for the business to function properly. Returning to the weight entry example, the AI finds the weight field reads 80 for a six-foot-tall male. Other weight entries for men of similar height show a range of 160-320 lbs. The 80 is an outlier and the AI can determine if it should convert the number from kilo to pounds or perform another action.
  • Accuracy – Are there incorrect spellings of people’s names or addresses? These issues can affect operational and advanced analytical functions.
application-level validation includes timeliness, consistency, and accuracy

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The same checks apply to on-premises data storage. If there is no data transfer, automated data monitoring can still execute all the checks here at the time of data entry.

3. Throughout the Data’s Lifecycle

Even when there is no event affecting the data, software solutions can continuously monitor the integrity and quality of the data. Business rules can change, and the data needs to reflect those updates. By constantly maintaining all the data quality characteristics, a company can trust that its information is clean and leverageable.

Automate Your Data Monitoring with FirstEigen

Manual approaches for ensuring big data quality encounter operational challenges, and poor data quality often causes erroneous decisions.

FirstEigen can help companies transition to a fully automated data quality solution. DataBuck, our unique offering, is an automated data monitoring and validation software that leverages AI/ML to enhance the way you do business.

Would you like to know more? Contact us today.

Check out these articles on Data Trustability, Observability, and Data Quality. 

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