Unveiling Insight through Unstructured Data Management

Every organization has to deal with a large volume of unstructured data in the form of emails, texts, files, business docs, audio, video, files, web pages, social media, etc. Instead of keeping this unstructured data and paying the cost of storage, there is a need to turn this unstructured data into valuable insights. Indeed, unstructured data management is a big challenge, and today I am going to help you overcome this challenge. You need to rely on some analytics tools to extract meaningful insight from unstructured data. 

But before I start unlocking different analytic tools, I would like to unveil the benefits and challenges of unstructured data management. Let’s get started.

Benefits of Unveiling Insight through A.I. Analytics Tool 

Artificial Intelligence has revolutionized the digital industry to a great extent. These days, Machine Learning and AI tools are widespread. They serve as the best-unstructured data management solutions. Here are some benefits an organization can gain by getting insight from unstructured data through AI tools:

Real-Time Analysis:

Deciding on time is crucial in these days. When your business relies on the latest trends, A. I analytics tools come to serve you. They offer real-time analysis of unstructured data. As a result, it becomes pretty simple for you to make decisions before time and get a competitive edge over your competitors. 

Accuracy:

It’s hard for human beings to deal with huge volumes of unstructured data as every second millions of data appear on the digital surface. Even if humans handle this data, they need time and resources and regardless of how careful they remain, errors and mistakes are unavoidable. 

On the flip side, when you rely on AI-based unstructured data management tools for analytics, they offer accurate analysis as you set some algorithms for them. 

Your business can’t afford to make mistakes based on inaccurate data, as it will cost you reputation and investment. Thereby, you need to make the most of current-age analytics tools for accurate information.

Time-Saving:

Instead of hiring some human resources for unstructured data management and analysis, you can rely on some A. I tools. They will speed up the whole process and allow you to make important decisions without wasting any crucial time. Indeed, time is money in this digital world and if you want to stand out in the crowd then you need to rely on machine learning and  AI-based tools.

Cost Effective:

Large organizations can manage huge volumes of unstructured data. However medium and small-scale organizations can’t invest that much time and money in getting insight from unstructured data. In that scenario, AI tools are available for business.These tools are real game changers as they save money and allow small organizations to compete well with large-scale ones.

Challenges of Analyzing Unstructured Data 

Although AI-based analysis helps organizations in many different ways and allows them to attain multiple benefits, some concerns and challenges are always there. The biggest challenge is to look for advanced tools for unstructured data management. You have a wide variety of tools but not all of them are perfect and well-aligned with your organization’s requirements.

When you analyze data with AI tools, you always need to keep up with the latest rules and regulations of your industry. It’s always tricky to keep up with governance compliance. Another concern is to reduce the bias as AI algorithms can be biased toward certain groups. Alone analysis won’t benefit your organization unless you make insight from unstructured data more accessible for everyone in the organization. Especially stakeholders and decision-makers must have ready access to analysis.

Getting Insights from Unstructured Data Management 

When it comes to getting some valuable information from unstructured data, then you need to understand different types of analytics you can perform and also some tools that will make text analysis handy for you.

Different Kinds of Text Analysis

Here are various forms of analysis you can perform on your unstructured data.

Sentiment Analysis:

Every organization has social media accounts where it regularly posts and gets comments and likes from its potential and existing customers. Tons of social media posts are out there. And when it comes to analyzing these posts, you can opt for sentiment analysis.

 It will help you understand the opinions expressed and sentiments in every piece of text, comment, review, and survey. During this analysis, linguistic rule-based methods, machine learning and lexical analytical tools are employed. 

The purpose of this analysis is to know your customers better and if there are some negative comments then your experts can make necessary changes to turn negative into positive. 

Named Entity Recognition (NER):

Another common analysis to get some insight from unstructured data is to utilize NER. This analysis makes it easy for your organization to identify and classify name entities. With the means of this analysis, your business can find crucial data and information about specific locations, key stakeholders, potential customers, suppliers, etc.

Topic Modeling:

It is the most popular technique to identify meaningful topics from the collection of documents without prior knowledge of the content within this document. Latent Dirichlet Allocation LDA is an algorithm utilized for this specific analysis. 

When your organization has a large volume of documents and files, you can organize, classify and analyze it quickly through topical analysis.

Text Classification:

If you want to well categorize all the unstructured data of your organization, then the best analysis option is available in the form of text classifications. You can create different categories such as email spam filters, news, content recommendations, sentiments, etc. Machine learning and deep learning algorithms are employed for this classification purpose.

Text Analytics Tools:

Here are some common analysis tools to make the most of unstructured data.

Natural Language Toolkit (NLTK):

You can find a wide variety of tools and resources for unstructured data analysis and NL in NLTK, a Python Library. You can find a comprehensive tool kit for text analysis such as corpora, pre-trained models, and lexical resources.

Stanford CoreNLP:

Natural Language Processing allows you to perform in-depth text analysis and it’s where this Java-based toolkit comes in handy. From entity recognition to tokenization and part-of-speech tagging, you can find all the features and functions in this toolkit.

IBM Watson Natural Language Understanding:

IBM always offers some great analytics tools to organizations and when it comes to unstructured data management and analysis, you can opt for this cloud-based NLP service. It is one of the best cloud-structured management services available in the market. It unlocks multiple functionalities such as entity recognition, emotion analysis, entity recognition, etc. This service is based on machine learning and linguistic analysis to get accurate and meaningful insight from unstructured data.

RapidMiner:

Do you want an efficient data science platform? If yes, then the best option comes from RapidMiner. It unlocks a visual interface that allows you to build and create text-mining workflows. You can make the most of the different functionalities of the platform, when it comes to data analysis, such as text classifications, topic modeling, and sentiment analysis. 

Wrap up

No matter what text analysis tools you employ, you need to store your unstructured data in one place. It’s good to rely on a cloud-based archive platform such as sharearchive. It’s a cost-saving option and lets you access data as and when you need it. Once you store your unstructured data in this platform, the next step is to get insight from this data through reliable tools and techniques. Choose one or multiple tools and get all the information you need to grow your business exponentially.