The integration of AI and NLP in data warehousing has opened new horizons for organizations, enabling them to unlock the hidden patterns, trends, and correlations within their data that were previously inaccessible. AI, with its cognitive computing capabilities, empowers data warehousing systems to learn from vast datasets, recognize complex patterns, and make predictions and recommendations with unprecedented accuracy. NLP, on the other hand, enables data warehousing systems to understand, analyze, and respond to human language, making it possible to derive insights from non-formatted data sources such as social media posts, customer reviews, and textual data.

The importance of AI and NLP in data warehousing cannot be overstated. These technologies are transforming the landscape of data warehousing in profound ways, offering organizations unparalleled opportunities to drive innovation, optimize operations, and gain a competitive edge in today’s data-driven business landscape.

Challenges Faced by C-Level Executives

Despite the immense potential of AI and NLP in data warehousing, C-level executives face unique challenges when it comes to implementing and leveraging these technologies. Some of the key challenges include:

• Data Complexity: The sheer volume, variety, and velocity of data generated by organizations pose a significant challenge in terms of data complexity. AI and NLP technologies need to be able to handle diverse data types, formats, and sources, and transform them into actionable insights.

• Data Quality and Accuracy: The accuracy and quality of data are critical to the success of AI and NLP in data warehousing. Ensuring data accuracy, consistency, and integrity across different data sources can be a daunting task, requiring robust data governance practices.

• Talent and Skills Gap: Organizations face a shortage of skilled professionals who possess the expertise in AI and NLP, making it challenging to implement and manage these technologies effectively. C-level executives need to invest in building a skilled workforce to leverage the full potential of AI and NLP in data warehousing.

• Ethical and Legal Considerations: The ethical and legal implications of using AI and NLP in data warehousing cannot be ignored. Organizations need to adhere to data privacy regulations, ensure transparency, and establish ethical guidelines for the use of AI and NLP to avoid potential risks and liabilities.

Also check out our Success Story on Product Categorization Using Machine Learning To Boost Conversion Rates.

The Current State of Data Warehousing

• Increasing Data Complexity: In today’s data-driven world, organizations are grappling with vast amounts of data coming from various sources such as social media, IoT devices, and customer interactions. This has led to data warehousing becoming more complex and challenging to manage.

• Manual Data Processing: Traditional data warehousing involves manual data processing, which is labor-intensive and time-consuming. Data analysts spend hours sifting through data, which can result in delays and increased chances of human error.

• Limited Insights: Conventional data warehousing provides limited insights, as it relies on predefined queries and reports, making it difficult to discover hidden patterns and insights buried in the data.

• Language Barriers: Data warehousing often faces language barriers, as data is generated in various languages, making it challenging to process and analyze non-English data.

The Future of Data Warehousing

• Augmented Data Management: AI and NLP are transforming data warehousing with augmented data management capabilities, including automated data integration, data profiling, data quality assessment, and data governance.
• Automation with AI & NLP: The future of data warehousing lies in leveraging the power of AI and NLP to automate data processing tasks. AI-powered algorithms can analyze data at scale, identify patterns, and provide real-time insights, reducing manual efforts and improving efficiency.
• Enhanced Data Insights: With AI and NLP, organizations can gain deeper insights from their data. These technologies can analyze unstructured data, such as social media posts or customer reviews, to uncover valuable insights and hidden patterns that can inform decision-making.
• Advanced Language Processing: NLP can overcome language barriers in data warehousing. It can process and analyze data in multiple languages, allowing organizations to tap into global markets and gain insights from multilingual data.
• Predictive Analytics: AI and NLP can enable predictive analytics in data warehousing, helping organizations forecast future trends, identify potential risks, and make data-driven decisions proactively. Example: By using predictive analytics through AI and NLP, a retail organization can forecast the demand for a particular product during a particular time and adjust their inventory levels accordingly, reducing the risk of stock outs and improving customer satisfaction.

Conclusion

In conclusion, AI and NLP are reshaping the landscape of data warehousing, enabling automation, enhancing data insights, overcoming language barriers, and facilitating predictive analytics. Organizations that embrace these technologies will be better positioned to leverage their data for competitive advantage in the digital era. At Indium Software, we are committed to harnessing the power of AI and NLP to unlock new possibilities in data warehousing and help businesses thrive in the data-driven world.

The post Revolutionizing Data Warehousing: The Role of AI & NLP appeared first on Indium.

It can help e-marketplaces with product classification. It can help to improve product design, devise focused marketing strategies, increase operational efficiency, and much more.

Recommended: What Text Analytics Tells Us about a Customer’s e-commerce Shopping Experience

While the list of possibilities is long, the success rate is not as high. According to a Gartner study, 80% of AI projects will be unable to scale in 2020 and by 2022, only 20% will deliver business outcomes.

One of the key reasons for this failure could be not selecting the right text analytics tool that can meet business goals and scale up.

A Buyer’s Guide for choosing a Text Analytics Solution

When scouting the market for the right text analytics solution, the 5 points to keep in mind include:

1. Customization: Each organization has a different business goal and a different set of data mix to work with. Most advanced platforms use a variety of methods such as machine learning, natural language processing, business rules and topic identification to analyze data. However, these tools tend to have a fixed, black-box model approach which may generate results fast but may be ineffective in working with small data sets. Being able to see the combination of algorithms and modify them to suit your specific and unique needs is necessary for you to benefit from the tool. 
2. Accuracy of Sentiment Analysis: Human beings communicate in complex ways. Words in themselves may sometimes mean the exact opposite in a particular context. Sarcasm is a tool that conveys much but requires reading between the lines to understand it. Emojis and exclamations contribute to the meaning. So tools that merely group words to identify the sentiment as positive or negative can be widely off the mark and be misleading. Training the tool with enough data sets to be able to accurately assess the tone becomes very important for the tool to be successful. A solution such as teX.ai from Indium Software is built on a strong foundation of semantics where the tone and the other components of communication are also factored in to arrive at the meaning accurately.
3. Use of Metadata: Aiding the semantics capabilities of any good tool like teX.ai is metadata that is often ignored by many tools. This can enhance the understanding of the sentiment better and get clarity in the face of ambiguity.
4. Multi-Lingual Support: This is the age of globalization where businesses can reach out to international markets. The Internet supports people to express themselves in the language they are most comfortable with and this makes it essential for businesses to be able to tap into chatter in those languages. Text analytics focused on English alone is no longer enough and the tool should be just as proficient in the semantics of that language to be able to unearth hidden meanings.
5. Dashboards and Visualization: Are you getting only basic charts or does the tool empower you to customize reports for a better understanding of the results is a clincher. Tools with better analytics models and enhanced dashboards along with multiple visualization options can help you get a better view from your slicking and dicing of data.

Indium Software’s teX.ai is a comprehensive tool with several visualization options, an intuitive user interface, customizable algorithms providing semantics-based sentiment analysis with multi-linguistic support that can fit the text analytics needs of organizations of any size.

Relevant read: Text Analytics of Social Media Comments Using Sentiment Analysis

Are You Ready for Text Analytics?

While the tool capabilities are very important for the success of the text analytics project, it is also essential to assess your internal preparedness to derive greater success from your text analytics initiative.

• Having the Right Data sets: Your organization must have enough documentation with textual data and of the right kind to get meaningful insights.
• The Right Team: While the text analytics software can help with analytics, your team should have the capability to benefit from the data to gather actionable insights.
• Company-wide Buy-In: Any analytics initiative can provide holistic insights only with an enterprise-wide commitment to implement the changes needed to enhance customer delight.
• Speed of Implementation: The speed of transformation as well as implementing the changes based on insights will have an impact on the success of the project.

Indium – End-to-End Solution Provider

teX.ai is a SaaS product solution from Indium Software, a technology solutions company. Incepted in 1999, Indium is a ISO 27001 certified company with 1000+ team members, servicing 350+ clients across several domains. It provides customer-centric, high quality technology solutions that deliver business value for Fortune 500 and Global Enterprises.

The teX.ai solution and the experienced team with cross-domain expertise can empower you to leverage your unstructured data for insights that can accelerate growth.  teX.ai helps produce structured data, metadata & insights by extracting data from text, summarizing information and classifying content.

If you wish to implement a scalable text analytics project to transform your business, contact us now.

The post Picking the Right Text Analytics Product: A 5-Step Guide appeared first on Indium.

It is referred to as text mining. One of the prominent reasons owing to which people use it is for the extraction of additional data from the unstructured data sources with an eye to enriching the master data of the customers with an eye to production the new customer insight.

It is also useful for the determination of sentiments and different types of products and services.

Results of the survey, tweets, online reviews, emails and different kinds of written feedback consist of insight into the customers.

The recorded interactions have a bunch of information that can be transformed into the text without any hassles.

According to Markets and Markets, the global text analytics market value is forecast to reach USD 8.8 billion by 2022 with a compound annual growth rate (CAGR) of 17.2 percent. Social media analytics and the growing need for predictive analytics for businesses are among the major factors driving the growth of the text analytics market.

With the aid of text analytics, you will be capable of uncovering a wide array of themes and patterns.

Thus, you will have an information about the thoughts of your customers. With it, you will be able to gain an understanding of their requirements and needs.

Top Tools for Text analytics

Text analytics with Hadoop

Analyzing text with the aid of Hadoop happens to be an amazing option when the full volume of the source files is huge and the Hadoop Cluster has prerequisite sources.

Thus, the analysis of the text happens more quickly in Hadoop. The text analysis is known for the extraction of entities from the unstructured text.

It is helpful for the transformation of the unstructured data into the structured data. This is crucial for running any sort of analysis with the aid of the data in text resources.

You just require the text sources as the input for the analysis of the text.

Later on, you can remove the text sources theoretically, as they will not be required for the process of analysis.

Text analytics with HANA

With the aid of SAP HANA, it is possible to extract real insight from the unstructured data.

This platform stands out of the ordinary in offering text analysis, search and text mining functionality from the unstructured text sources.

Statistical algorithms can be applied by which you can detect the patterns in the large document collections, which is inclusive of key term identification as well as document.

Almost 80 percent of the relevant information of the enterprise is derived from the unstructured data.

With the aid of SAP HANA, you can get access to the greater volume of data that is inclusive of unstructured text data from a wide array of sources. SAP HANA allows people to do the full-text analysis.

Text analytics with R

Here is the list of the leading four options that are used in the Big Data Services industry with an eye to accomplishing text analysis in R:

Keyword Match Algorithm

It is considered to be the most powerful tool for performing text analysis. It stands second to none in the extraction of keywords from the not so well separated keywords.

It comes with the option to assign priority to the algorithm. You, however, require a pre-defined list of keywords from where you require searching.

At times, it has been seen to capture a few types of misclassified cases.

Word match algorithm

This is known to be the fix for the min-classified cases that are found in the last algorithm.

Here, words are matched in lieu of the keywords. It functions in a perfect manner to find the well-separated words.

For example, with the aid of this algorithm, it is possible to extract the word Ramesh from Ramesh Shastri.

It enables the priority order as well. For example, in case you intend to give higher priority to Ramesh than Shastri in the above-mentioned tag, it can be executed at ease.

General Expressions

This process requires extensive research from the sentence structures. In order to begin with it, you do not require any sort of list.

The percentage of accuracy is really high if you gain success in finding the stronger and regular expression.

An in-depth research is required for the creation of regular expression. In case the data is not structured well, this process lets you tag a smaller number of the observations.

This algorithm can be used if you are not aware of the language of the text. It functions as the feedback to the other algorithm.

If parameters are optimized in a perfect manner, it can be predicted with ease. You do not need any dictionary.

It is also used for providing feedback to the other algorithms. At times, it is not that precise in the name of the subject.

It has a tendency for capturing the trends that do not indicate anything significant.

Text analytics with Excel

Excel is recognized to be an effective and convenient solution for accomplishing your requirements for text analysis.

You can go for an analysis of several customer reviews for gaining an insight into the product.

The Excel add-in functions on ParallelDots AI APIs that are used by the enterprises and developers for empowering the analytics for the past two years.

You can conduct keyword analysis on a bunch of negative and positive sentences with an eye to understanding why people are disliking or liking the product.

This analysis let you get an insight into the key phrases that contribute to the sentiment about the product.

For instance, a phone manufacturer can conduct the analysis of reviews from the social media, eCommerce sites, and tech review blogs.

After that, keywords can be extracted for the negative and positive sentiment sentences for finding the features, disliked or liked by the users regarding about a specific phone model.

In addition to this, you can go to a higher level by the analysis of the product reviews and then categorize the same with an eye to identifying if the review is a query, feedback, spam or opinion.

This is useful to filter the essential reviews and then act on them quickly.

You can correlate the analyzed data such as intent, keywords, and sentiment with the internal business metrics like sales data, marketing spends for getting actionable insights.

Text analytics with Python

Here are some of the applications in which python stays at the forefront that enable the use of a wide assortment of advanced libraries, specifically the natural language processing toolkit.

It comprises of a series of libraries and advanced functions for the performance of specific operation present in the text for pre-processing it for using the same for the derivation of information from the same.

Chatbots

Though customer service is present for most of the products, it is not available always effective as most of the people want their complaints should be solved during the usual working hours, thereby resulting in a rush. Chatbots are considered the perfect solution to the same.

Sentimental analysis

During online shopping, most of the customers provide feedback. This feedback is classified into the categories like negative, positive and neutral, thereby letting the customers make a better-purchased decision about the product.

It also helps the company in filtering out the flaws from the negative reviews for the improvement of the product.

Conclusion

The text analytics is known for conferring the early warning of the trouble as it showcases the points, your clients are not satisfied with.

With the aid of the text analytics tool, you will gain success in extracting valuable details from the data that cannot be quantified in the other ways at ease.

It is useful in turning the unstructured thoughts of customers into the unstructured data at ease that you can use for your business.

The post 5 Widely used Tools and Techniques for Text Analytics appeared first on Indium.