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How can you use user feedback to make better database indexing decisions in information architecture?

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Information architecture (IA) is the practice of organizing and labeling data and content in a way that makes sense to users and supports their goals. Database indexing is one of the techniques that IA uses to improve the performance and usability of data retrieval and manipulation. However, creating and maintaining indexes can be challenging, as they require careful planning, testing, and monitoring. How can you use user feedback to make better database indexing decisions in IA? Here are some tips to help you.

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Basushankar Bharadwaj

Innovator and Problem Solver - Data Strategy | Data and Analytics | Chief Data Office
Carlos Schmiedel, MSc

Founder at Predify | Business Unit Executive Manager at Neogrid

1 Understand user needs and expectations

Before you design or optimize your indexes, you need to understand what your users want and need from your data. What are their common queries, filters, and sorting preferences? How do they navigate and explore your data? How fast do they expect the results to load? How do they evaluate the relevance and quality of the data? You can use various methods to gather user feedback, such as surveys, interviews, usability tests, analytics, and observation. The more you know about your users, the better you can align your indexing strategy with their behavior and satisfaction.

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Basushankar Bharadwaj

Innovator and Problem Solver - Data Strategy | Data and Analytics | Chief Data Office
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A lot depends on the volume of data and the usage. For instance, for high volumes of structured data with heavy turnover, it may make sense to look at a columnar solution which will automatically handle column level indexes when users retrieve in bulk. Common queries can be tuned to fetch quicker or the data can even been cached if it’s not too much.

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2 Choose the right indexing methods and tools

Depending on the type, structure, and size of your data, you may need to use different indexing methods and tools to improve your IA. For example, you may use full-text indexing to enable natural language search, spatial indexing to support geographic queries, or hash indexing to speed up exact match queries. You may also use specialized tools or platforms, such as Elasticsearch, MongoDB, or Firebase, to handle complex or dynamic data indexing. You should evaluate the pros and cons of each option and choose the ones that best suit your user needs and feedback.

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3 Test and measure your indexing performance and usability

Once you have implemented your indexes, you need to test and measure how they affect your data performance and usability. You can use various metrics and indicators, such as query speed, accuracy, relevance, scalability, and reliability, to assess your indexing effectiveness. You can also use user feedback tools, such as ratings, reviews, comments, or suggestions, to collect user opinions and preferences on your data quality and accessibility. You should compare your results with your user expectations and goals and identify any gaps or issues that need improvement.

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4 Monitor and update your indexes regularly

Database indexing is not a one-time task, but a continuous process that requires regular monitoring and updating. As your data grows and changes, your indexes may become outdated, inefficient, or inconsistent. As your users evolve and change, their needs and expectations may also shift or diversify. You should keep track of your data and user feedback and make adjustments to your indexes accordingly. You should also review your indexing methods and tools periodically and explore new or better alternatives that can enhance your IA.

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5 Learn from best practices and case studies

One of the best ways to improve your database indexing decisions in IA is to learn from best practices and case studies from other projects or organizations. You can find many examples and resources online or offline that showcase how different indexing techniques and tools can improve data performance and usability in various contexts and domains. You can also join online or offline communities and networks of IA professionals and enthusiasts and exchange ideas, tips, and feedback on database indexing. You can use these sources of inspiration and learning to refine your own indexing strategy and skills.

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6 Experiment and iterate your indexing solutions

Finally, you should not be afraid to experiment and iterate your indexing solutions in IA. Database indexing is not an exact science, but a creative and dynamic art that requires trial and error, feedback and learning, and adaptation and innovation. You should test different indexing options and combinations and see how they affect your data and user outcomes. You should also solicit and incorporate user feedback and suggestions and see how they can improve your indexing decisions. You should also be open to change and update your indexing solutions as your data and user needs change.

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Carlos Schmiedel, MSc

Founder at Predify | Business Unit Executive Manager at Neogrid
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O cliclo de desenvolvimento lean do Eric Ries e amplamente difundido nas startups é extremamente importante nesse ponto. Desenvolver, testar, aprender e come?ar de novo faz com que o processo continue sempre em evolu??o. Para isso, saber colher feedbacks, filtrá-los e gerar aprendizado para o próximo ciclo é fundamental.

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7 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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How can you use user feedback to make better database indexing decisions in information architecture?

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1 Understand user needs and expectations

2 Choose the right indexing methods and tools

3 Test and measure your indexing performance and usability

4 Monitor and update your indexes regularly

5 Learn from best practices and case studies

6 Experiment and iterate your indexing solutions

7 Here’s what else to consider

Information Architecture

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How can you use user feedback to make better database indexing decisions in information architecture?

1

2

3

4

5

6

7

1 Understand user needs and expectations

2 Choose the right indexing methods and tools

3 Test and measure your indexing performance and usability

4 Monitor and update your indexes regularly

5 Learn from best practices and case studies

6 Experiment and iterate your indexing solutions

7 Here’s what else to consider

Information Architecture

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