Text analytics has become an indispensable tool in the era of big data, allowing businesses and researchers to sift through massive amounts of unstructured data and extract meaningful insights. This process goes far beyond the traditional use of keywords for information retrieval. Here’s how text analytics is being used to delve deeper into big data.

Understanding Text Analytics

Text analytics, also known as text mining, involves a set of techniques and processes that enable the conversion of unstructured text data into structured data. This transformation allows for the application of statistical, linguistic, and machine learning methods to analyze, understand, and interpret the data. The ultimate goal is to uncover patterns, trends, sentiments, and relationships that might not be immediately apparent.

Beyond Keywords: The Depth of Analysis

Keyword search is a primary method for information retrieval that has been used extensively over the years. However, it has its limitations. Keywords can be ambiguous, context-dependent, and can miss nuanced or conceptually related information that does not contain the specific terms used in a search query.

Text analytics transcends these limitations by employing:

Natural Language Processing (NLP): It allows machines to understand and interpret human language as it is spoken or written. NLP breaks down language into shorter, elemental pieces, helps to understand the relationships between these pieces, and explores how these pieces work together to create meaning.

Sentiment Analysis: This helps in understanding the sentiment behind the text, whether it’s positive, negative, or neutral. This is especially useful for brands monitoring social media, customer reviews, and feedback.

Topic Modeling: Instead of just picking up keywords, text analytics can identify themes or topics within a large text corpus. Techniques like Latent Dirichlet Allocation (LDA) can be used to discover abstract topics throughout a document collection.

Entity Recognition: This involves identifying and classifying key elements in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc.

Relationship Extraction: This process determines the semantic relationships between entities within the text, which can reveal complex structures of data.

Applications of Text Analytics

The potential applications of text analytics are vast and varied:

Market Intelligence: By analyzing news articles, blog posts, and social media, companies can get a better sense of market trends and the competitive landscape.

Customer Experience: Analyzing customer feedback, support tickets, and surveys with text analytics can reveal insights about customer satisfaction and areas for improvement.

Risk Management: Financial institutions can use text analytics to monitor communication and documents to detect fraud and manage risk.

Healthcare: Text analytics can help in parsing through medical records to identify patterns and insights for research and treatment.

Challenges in Text Analytics

Despite the potential, there are challenges that need to be addressed:

Data Quality and Preprocessing: The quality of insights depends heavily on the quality of data and preprocessing steps to clean and prepare data can be complex and time-consuming.

Sarcasm and Irony: These subtle aspects of human communication can be difficult for text analytics algorithms to interpret correctly.

Contextual Understanding: Words can have different meanings in different contexts, and capturing the exact meaning can be challenging.

Language and Cultural Variations: Nuances in language and cultural contexts can affect the interpretation of text data.

The Future of Text Analytics

The future of text analytics is promising with advancements in AI and machine learning:

Deep Learning: The use of deep learning models can improve the accuracy of text analytics by capturing more complex patterns in the data.

Real-Time Analysis: As processing power increases, the ability to perform text analytics in real-time will become more common, allowing for instant insights.

Integration with Other Data Types: Combining text data with other data types like images and videos for multimodal analysis could provide even deeper insights.


In a digital era where data is king, Skellam stands as a beacon of innovation, redefining the way consumer-focused brands leverage their customer data for strategic advantage. At the heart of this revolution lies Skellam’s advanced Customer Data Platform (CDP), a paradigm shift in understanding and engaging with the customer base.

The Skellam Advantage

Skellam’s commitment to revolutionizing customer data management is evidenced by its state-of-the-art CDP. This platform is not just a tool; it’s a comprehensive ecosystem that encapsulates the essence of customer interactions across myriad touchpoints. Whether it’s deciphering the nuances of purchasing behaviors, analyzing product usage, or aligning with buying goals, Skellam’s CDP is adept at capturing and integrating data across devices and channels, online and offline.

What sets Skellam apart is its meticulous approach to data accuracy, alignment, and privacy. This trinity of values ensures that customer profiles are not just repositories of information, but dynamic and insightful narratives of each customer’s journey. Skellam’s tailored solutions offer a lens into the customer’s world, enabling brands to make informed decisions that resonate on a personal level with their audience.

Custom CDP Solutions by Skellam

Recognizing that no two businesses are alike, Skellam prides itself on delivering custom CDP solutions. Their bespoke platforms are a testament to the company’s philosophy that customer data platforms should be as unique as the customers they study. This customization not only eliminates the constraints of off-the-shelf solutions but also enriches existing marketing and sales ecosystems, leading to heightened customer engagement and streamlined processes.

Skellam’s collaborative approach with internal teams is pivotal. It ensures that every nuance of the business’s operations is considered, allowing the CDP to integrate and elevate the entire value chain of the business. This alliance is strategic and transformative, empowering businesses to augment market share, boost profitability, and make data-centric strategic decisions.

About Skellam

At the core of Skellam is an assembly of visionaries, each an expert in AI, data science, and product development. Their mission is simple yet profound: to craft complex, custom solutions that address the unique challenges faced by businesses. The suite of services Skellam offers is comprehensive, ranging from custom-built Data & AI Products to Martech & Customer Analytics, and from Data Science to Data Engineering.

Resources and Further Reading

Skellam is not only a pioneer in the practical application of data solutions but also an intellectual thought leader. The company frequently publishes insights on the evolving landscape of technology and its impact across sectors. Their in-depth exploration into AI’s role in transforming the restaurant industry and the intricacies of NLP underscores their expertise and commitment to advancing human-machine communication.

Final Thoughts

As the digital landscape continues to expand, the need for nuanced, powerful, and ethical use of customer data grows. Skellam is at the vanguard of this movement, providing not just services but visionary partnership to businesses seeking to harness the full potential of their customer data. Through its bespoke CDP solutions, Skellam is enabling enterprises to achieve unparalleled customer understanding and engagement.

For companies looking to navigate the complex currents of big data and emerge as leaders in customer-centric innovation, Skellam offers the map and the compass. It is more than a service provider; it is a pathfinder for those who dare to embrace the transformative power of data. With Skellam, the journey towards data-driven brilliance is not just a possibility—it is a promise.