The Good, The Bad And The Ugly Side Of Data Automation!

Knock, knock!

Who’s there?

The King!

The King who?

The King of business AKA Data!

Data truly rules how businesses function nowadays. Every business needs data but it’s important they structure it. Possessing mountains of unstructured data is as good as buying a boat that’s at the bottom of the ocean. Sure, you could find a way to bring it up and repair it but that would cost a fortune!

At present, a large number of businesses make use of manual methods to collect, sort, manage and handle data. Unfortunately, a lot of these processes are routine and menial tasks that eat away at the true potential and productivity of the employees involved.

In fact, according to a survey by SmartSheet, more than 40% of employees spend around 25% of their weekly work on manual, repetitive tasks, while 55% of them believe the collection, uploading and syncing of data is the least productive part of manual data entry. Additionally, email and data collection takes up a large chunk of their time.

This is one of the reasons why businesses are looking towards automating data processes and integrating Artificial Intelligence, Mcahine Learning and analytics solutions to detect various data issues in real-time.

According to Jonathan S. King, Group COO, ST Telemedia Global Data Centres, "Workflow automation can help organizations achieve better governance, risk management, and compliance by providing greater visibility into processes and data."

TechDogs-"Jonathan S. King, Group COO, ST Telemedia Global Data Centres"

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However, regardless of manual or automated data processes, there remains an issue at the core that needs to be addressed - bad data.

As the number of instances of bad data keeps increasing, so does the time taken by teams to fix them. According to a survey by Monte Carlo and Wakefield Research, instance detection taking 4 or more hours was observed at 62% in 2022, which increased to 68% in 2023. Resolution time of 9 to 15 hours reached 63% according to the same report. #TimeWasted

Lior Gavish, Co-founder and CTO of Monte Carlo, says “More data plus more complexity equals more opportunities for data to break. A higher proportion of data incidents are also being caught as data is becoming more integral to the revenue-generating operations of organizations. This means business users and data consumers are more likely to catch incidents that data teams miss.”

Another issue is downtime, which nearly doubled year over year, happens due to three primary reasons: an increase in incidents, longer detection times and longer resolution times.

“Data generally follows the Pareto principle, 20% of datasets provide 80% of the business value and 20% of those datasets (not necessarily the same ones) are causing 80% of your data quality issues. Make sure you can identify those high-value and problematic datasets and be aware of when they change over time,” Gavish said.

TechDogs-"Image Of Lior Gavish, Co-founder And CTO Of Monte Carlo"

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Essentially, AI and ML automation can help reduce errors, improve delivery time and data accuracy. However, if the system possesses bad data, many of these issues will persist.

How do you think businesses can solve the bad data problem? Let us know in the comments below!

First published on Thu, May 18, 2023

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Data Automation Bad Data Artificial Intelligence Machine Learning

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The Good, The Bad And The Ugly Side Of Data Automation!

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