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What do you do if automation in data management using new technology is causing more problems than it solves?

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Automation in data management using new technology can be a powerful way to streamline workflows, improve efficiency, and reduce errors. However, it can also introduce new challenges, such as compatibility issues, data quality problems, security risks, and user resistance. How can you address these issues and make sure your automation projects are successful and beneficial? Here are some tips to help you.

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Jon Andoni Baranda

Technical Program Manager II @ Microsoft - Azure | AI Cloud/Data Infrastructure Engineering | Driving Technical…

1 Assess your needs

Before you implement any new technology for data management, you need to have a clear understanding of your business goals, data requirements, and current processes. What are the pain points you want to solve with automation? What are the benefits you expect to gain? How will you measure the outcomes? How will the new technology fit into your existing data architecture and governance? By answering these questions, you can define the scope, objectives, and criteria for your automation projects.

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Jon Andoni Baranda

Technical Program Manager II @ Microsoft - Azure | AI Cloud/Data Infrastructure Engineering | Driving Technical Projects to Success | AWS, Microsoft and Google Certified
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If automation in data management using new technology is causing more problems than it solves, the first step is to assess your needs thoroughly. Before implementing any new technology, it's crucial to have a clear understanding of your business goals, data requirements, and current processes. Identify the pain points you aim to address with automation and define the expected benefits. Determine how you will measure the outcomes and assess how the new technology aligns with your existing data architecture and governance. By addressing these questions, you can define the scope, objectives, and criteria for your automation projects, ensuring that they effectively address your needs and contribute positively to your data management efforts.

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2 Choose the right tools

There are many options for data management automation, such as cloud-based platforms, artificial intelligence, machine learning, robotic process automation, and more. Each of these tools has its own strengths, limitations, and use cases. You need to evaluate the features, functions, and costs of each tool and compare them with your needs and budget. You also need to consider the compatibility, scalability, and security of each tool and how it will interact with your other data sources and systems.

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3 Test and validate

Before you deploy any new technology for data management automation, you need to test and validate its performance, accuracy, and reliability. You need to create test scenarios, use cases, and data sets that reflect your real-world situations and expectations. You need to check for any errors, bugs, or anomalies that could affect your data quality, integrity, or availability. You also need to verify that the new technology complies with your data standards, policies, and regulations.

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4 Train and support

One of the most common reasons for automation failure is user resistance. Many people are reluctant to adopt new technology for data management because they fear it will replace their jobs, change their roles, or reduce their control. To overcome this barrier, you need to train and support your users and stakeholders. You need to explain the benefits and value of automation, demonstrate how it works, and address any concerns or questions. You also need to provide ongoing support, feedback, and updates to ensure user satisfaction and engagement.

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5 Monitor and optimize

Automation in data management is not a one-time event, but a continuous process. You need to monitor and optimize your automation projects to ensure they are delivering the desired results and meeting the changing needs of your business and data. You need to collect and analyze data on the performance, efficiency, and quality of your automation processes. You also need to identify and implement any improvements, enhancements, or adjustments that could optimize your automation outcomes.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Mohammed Irfan M.Tech, CDMP, PSMII

Data architect with expertise in data management, data modeling, data quality, data governance, integration, ETL and database management. Bigdata | Teradata | Ab initio.
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In the realm of data management, the allure of automation was undeniable, promising efficiency and speed. * However, the wise team encountered unforeseen challenges as new technology brought more problems than solutions. * They paused and reassessed, scrutinizing the implementation and its impacts. * Through careful analysis and collaboration, they identified areas for improvement and adapted their approach. With resilience and innovation, they navigated the complexities, ensuring automation served as a boon rather than a burden in their quest for data management excellence.

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What do you do if automation in data management using new technology is causing more problems than it solves?

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3

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1 Assess your needs

2 Choose the right tools

3 Test and validate

4 Train and support

5 Monitor and optimize

6 Here’s what else to consider

Data Management

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What do you do if automation in data management using new technology is causing more problems than it solves?

1

2

3

4

5

6

1 Assess your needs

2 Choose the right tools

3 Test and validate

4 Train and support

5 Monitor and optimize

6 Here’s what else to consider

Data Management

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