Last updated on Oct 31, 2024

How do you manage unstructured data?

Unstructured data is any data that does not have a predefined format, such as text, images, audio, video, or social media posts. It can be a valuable source of insights for data analytics, but it also poses many challenges for data management. How do you handle unstructured data effectively and efficiently? Here are some tips to help you.

1 Identify your data sources and goals

Before you can manage unstructured data, you need to know where it comes from and what you want to do with it. Depending on your data sources, you may have different types of unstructured data, such as emails, web pages, PDFs, tweets, or videos. You also need to define your data analysis goals, such as sentiment analysis, topic modeling, image recognition, or speech transcription. These goals will help you determine what kind of data you need and how to process it.

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2 Extract relevant information

Unstructured data often contains a lot of noise and irrelevant information that can affect the quality and accuracy of your data analysis. To reduce the noise and extract the information that matters, you need to apply various techniques, such as text parsing, optical character recognition, natural language processing, or computer vision. These techniques can help you convert unstructured data into structured or semi-structured data, such as tables, graphs, or keywords, that can be easier to analyze.

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Merijn Campsteyn

Chief Technology Officer @ Deloitte BE A&A | Data and AI enthousiast
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Unstructured data is everywhere: in documents, images, audio files, and videos. But how can we make sense of it and use it for other applications? One way is to use AI to turn unstructured data into structured data. This means applying techniques such as text analysis, optical character recognition, voice recognition, and machine vision to extract relevant information from different data sources. The extracted information can then be organized into a standardized format that can be easily searched, analyzed, and processed by AI or other data analysis applications. For example, Microsoft’s Azure AI uses a mixture of these techniques to make sense of an unstructured collection of files and build a language-aware index of the data.

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3 Store and organize your data

Unstructured data can be large and complex, so you need to store and organize it in a way that facilitates access and retrieval. Depending on your data volume and variety, you may need to use different storage options, such as relational databases, NoSQL databases, data lakes, or cloud platforms. You also need to implement a data governance strategy that defines the roles, responsibilities, and rules for managing your data. This can help you ensure the security, quality, and consistency of your data.

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James “J. Ben” Greisman

Senior Associate at The Strategic Counsel
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Be ready to have back up plans in case your data silo linkages get hacked or in case of server breakages, etc. Sometimes whole telecoms companies lose service, or hackers hold companies ransom for their data.

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Whitney Post

Inside Sales @ Hammerspace | Making data an instantly accessible resource for users, applications, and compute clusters, no matter where they are located or how the data is stored.
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One of the major challenges is getting unstructured data where it needs to be when it needs to be there. Your storage and infrastructure choices impact this. Look into infrastructure choices that allow for future flexibility and avoid vendor lock in. A product like Hammerspace enables global seamless data access, movement, & automated tiering ( orchestration ) without siloing you into a proprietary system. Be sure to take into account TCO - how much custom work and staff will it take to manage this long term? Things like Lustre, Vast or Weka are very complicated and manpower intensive to manage and or lock you in to their hardware / cloud. Going with a vendor agnostic system like Hammerspace allows for future flexibility.

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4 Analyze and visualize your data

Once you have stored and organized your data, you can start analyzing and visualizing it to gain insights and make decisions. Depending on your data analysis goals, you may need to use different tools and methods, such as statistical analysis, machine learning, data mining, or text analytics. You also need to choose the appropriate visualization techniques, such as charts, maps, dashboards, or word clouds, that can help you communicate your findings and recommendations.

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5 Update and maintain your data

Unstructured data is dynamic and constantly changing, so you need to update and maintain your data regularly to ensure its relevance and accuracy. You need to monitor your data sources and pipelines for any changes or errors that may affect your data quality and analysis. You also need to review your data analysis goals and methods periodically to ensure they are aligned with your business needs and expectations.

Unstructured data can be a powerful asset for data analytics, but it also requires careful management. By following these tips, you can handle unstructured data more effectively and efficiently.

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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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How do you manage unstructured data?

1

2

3

4

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1 Identify your data sources and goals

2 Extract relevant information

3 Store and organize your data

4 Analyze and visualize your data

5 Update and maintain your data

6 Here’s what else to consider

Data Analytics

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How do you manage unstructured data?

1

2

3

4

5

6

1 Identify your data sources and goals

2 Extract relevant information

3 Store and organize your data

4 Analyze and visualize your data

5 Update and maintain your data

6 Here’s what else to consider

Data Analytics

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