Know the Text Analytics Use Cases in Businesses

Bineesh Mathew | March 31, 2022 | 190 views

“Text analytics can help organizations discover patterns in large unstructured data sets. Unstructured data, such as videos, photos, and audio accounts for at least 80% of your company’s data, a true blind spot for most businesses.”

- IBM

Data scientists use advanced data science approaches to examine text. This textual data provides a better understanding of client attitudes toward certain topics or uncovers additional information.

Using this text analytics, you can turn free-form text into structured data for use in prediction models or uncover hidden patterns in your data. If you want, you can use text analysis to identify prospective customers who might be interested in cross-selling, forecast customer attitudes, and understand fraud-prevention behaviors.

Businesses understand the value of their raw text across all industries. As a result, with the help of data, they can reduce operational expenses, find previously unknown linkages, and get a better insight into the future trends.

Is it hard to comprehend that text accounts for 80% of all corporate data?

Online reviews, call center transcripts, consumer surveys, and other written documents are examples. This raw text data is a gold mine for understanding customer attitudes. Text mining and analytics transform these underutilized data sources into actionable information. However, each organization must have the expertise, infrastructure, and analytical perspective to implement this cutting-edge technology in their own way.


How is Text Analytics Used in Companies?

Companies can use NLP and untapped data sources in a number of ground-breaking ways.

Many businesses are already successfully employing text to drive their operations. In addition, text analytics can help you improve your procedures if you're transitioning from business intelligence reporting to data science.


Best Five Text Analytics Use Cases for Businesses

Companies and people, regardless of industry, desire to make better-informed business decisions based on trackable and measurable data. Thanks to improvements in text analysis, companies can now mine the text for insights and improve their service or offering to thrive in their industry.

Read on to understand some of the text analytics use cases that could be applied in your company.


Voice of the Customer (VOC) to Extract Customer Opinion on a product


Companies employ VOC applications to determine what customers say about a product or service.

Emails, call center logs, surveys, and social media streams such as blogs, tweets, forum postings, newsfeeds, and so on are examples of data sources. A telecommunications company, for example, might use voice of customer text analysis to look for complaints about their online services on Twitter.

It will give them an early warning when customers aren’t happy with the service's performance, so that they can act before the client calls to complain or publicly ask for the contract to be terminated.


Lead Generation through Social Media

A piece of social media information can be used to retain and get new clients. It is like the use of the VOC application.

For example, if a person tweets that they are interested in a particular product or service, text analytics can detect this and pass the information to a sales representative, who can then pursue the prospect and turn them into a customer.


Finding Out What Customers Value through Market Research

According to numerous statistics, consumers are interested in other people's thoughts and experiences. According to a study, at least 90% of humans are influenced by what they read.

Also, if the review is terrible, the sentiment is shared. In the last few years, several websites have been collecting reviews of local eateries, vacation spots, and, of course, commercial products.

If your company is thinking about going into a new market or looking into new product ideas, why not start by looking into online market research reviews from real people?

Market research helps you know what features are important to customers when you start your marketing effort. It's critical to know which characteristics influence purchasing decisions and contribute to customer unhappiness.


Use Customer Complaints to Identify New Product Ideals

Understanding the consumer experience is critical, and internet reviews offer a dependable means to do it. Of course, when a consumer encounters problems, no one expects them to be happy, but it can be positive if the support is speedy and helpful.

Social media handles could be effectively used to understand the feedback and complaints of customers. Responding to them promptly makes the customers feel good. It also, gives an idea of the expectations of the customers and the new product ideals.


Analyzing the Customer Sentiments

Whether you're selling a handbag or consumer software on the App Store, text analytics may help you categorize reviews quickly. Unfortunately, a spreadsheet and hours of reading and categorizing reviews are generally required for the manual option. Aside from the discomfort of working long hours, we frequently find irregularities due to the physical nature of this labor. So why not create a data categorization and scoring model that you can use to rerun the data daily, weekly, or monthly?


Summing Up

Companies now have many options to perform text analysis thanks to the rise and availability of unstructured text data. However, simply wanting to use text analytics and predictive analytics isn't enough. You need to first understand where you are as a company from an analytical point of view, and then you need to create a plan on how to embrace these new opportunities. Understanding where you are now can help you determine your next steps and protect you from taking on more than you can handle.


Frequently Asked Questions


How do companies use text analytics?

Text analytics is being used by businesses to analyze consumer comments, evaluate client interactions, assess claims, and uncover compliance concerns. Text analytics software based on natural language processing (NLP) can be used to quickly scan internal legal documents for words and phrases related to finance or fraud.


What can text analytics be used for?

Text analytics is used to gain deeper insights from unstructured text. For example, it can help you see a pattern or trend.


Can business intelligence be improved through text analytics?

Text analytics can help you understand trends, patterns, and actionable insights that you can apply to make data-driven decisions. It can be done by combining the findings of text analysis with business intelligence tools to put the numbers into easy-to-understand reports and images.

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Hammerspace’s Parallel Global File System orchestrates data automatically and by policy in advance to make data present locally without wasting time waiting for data placement. And data placement occurs fast! Using dual, 100Gb/E networks, Hammerspace can intelligently orchestrate data at 22.5GB/second to where it is needed. This performance level enables workflow automation to orchestrate data in the background on a file-granular basis directly, by policy, making it possible to start working with the data as soon as the first file is transferred and without needing to wait for the entire data set to be moved locally. Unstructured data workloads in the cloud can take full advantage of as many compute cores as allocated and take advantage of as much bandwidth as is needed for the job, even saturating the network within the cloud when desired to connect the compute environment with applications. A recent analysis of EDA workloads in Microsoft Azure showed that Hammerspace scales performance linearly, taking full advantage of the network configuration available in Azure. This high-performance cloud file access is necessary for compute-intensive use cases, including processing genomics data, rendering visual effects, training machine learning models and implementing high-performance computing architectures in the cloud. High-performance across data centers and to the cloud in the Release 5 software include: Backblaze, Zadara, and Wasabi support Continual system-wide optimization to increase scalability, improve back-end performance, and improve resilience in very large, distributed environments New Hammerspace Management GUI, with user-customizable tiles, better administrator experience, and increased observability of activity within shares Increased scale, increasing the number of Hammerspace clusters supported in a single global data environment from 8 to 16 locations 2) High-Performance Across Interconnect within the Data Center: Saturate Ethernet or InfiniBand Networks within the Data Center Data centers need massive performance to ingest data from instruments and large compute clusters. Hammerspace makes it possible to reduce the friction between resources, to get the most out of both your compute and storage environment, reducing the idle time waiting on data to ingest into storage. Hammerspace supports a wide range of high-performance storage platforms that organizations have in place today. The power of the Hammerspace architecture is its ability to saturate even the fastest storage and network infrastructures, orchestrating direct I/O and scaling linearly across otherwise incompatible platforms to maximize aggregate throughput and IOPS. It does this while providing the performance of a parallel file system coupled with the ease of standards-based global NAS connectivity and out-of-band metadata updates. In one recent test with moderately sized server configurations deploying just 16 DSX nodes, the Hammerspace file system took advantage of the full storage performance to hit 1.17 Tbits/second, which was the max throughput the NVMe storage could handle, and with 32kb file sizes and low CPU utilization. The tests demonstrated that the performance would scale linearly to extreme levels if additional storage and networking were added. High-performance across interconnect within the data center enhancements in the Release 5 software include: 20 percent increase in metadata performance to accelerate file creation in primary storage use cases Accelerated collaboration on shared files in high client count environments RDMA support for global data over NFS v4.2, providing high-performance, coupled with the simplicity and open standards of NAS protocols to all data in the global data environment, no matter where it is located 3) High-Performance Server-local IO: Deliver to Applications Near Theoretical I/O Subsystem Maximum Performance of Cloud Instances, VMs, and Bare Metal Servers High-performance use cases, edge environments and DevOps workloads all benefit from leveraging the full performance of the local server. Hammerspace takes full advantage of the underlying infrastructure, delivering 73.12 Gbits/sec performance from a single NVMe-based server, providing nearly the same performance through the file system that would be achieved on the same server hardware with direct-to-kernel access. The Hammerspace Parallel Global File System architecture separates the metadata control plane from the data path and can use embedded parallel file system clients with NFS v4.2 in Linux, resulting in minimal overhead in the data path. For servers running at the Edge, Hammerspace elegantly handles situations where edge or remote sites become disconnected. Since file metadata is global across all sites, local read/write continues until the site reconnects, at which time the metadata synchronizes with the rest of the global data environment. Quotes: David Flynn, founder and CEO of Hammerspace and previous co-founder and CEO of Fusion-IO “Technology typically follows a continuum of incremental advancements over previous generations. But every once in a while, a quantum leap forward is taken with innovation that changes paradigms. This was the case at Fusion-IO when we invented the concept of highly-reliable high-performance SSDs that ultimately became the NVMe technology. Another paradigm shift is upon us to create high-performance global data architectures incorporating instruments and sensors, edge sites, data centers, and diverse cloud regions.” Eyal Waldman, co-founder and previous CEO of Mellanox Technologies, Hammerspace Advisory Board Member “The innovation at Mellanox was focused on increasing data center efficiency by providing the highest throughput and lowest latency possible in the data center and in the cloud to deliver data faster to applications and unlock system performance capability. I see high-performance access to global data as the next step in innovation for high-performance environments. The challenge of fast networks and fast computers has been well solved for years but making remote data available to these environments was a poorly solved problem until Hammerspace came into the market. Hammerspace makes it possible to take cloud and data utilization to the next level of decentralization, where data resides.” Trond Myklebust, Maintainer for the Linux Kernel NFS Client and Chief Technology Officer of Hammerspace “Hammerspace helped drive the IETF process and wrote enterprise quality code based on the standard, making NFS4.2 enterprise-grade parallel performance NAS a reality.” Jeremy Smith, CTO of Jellyfish Pictures "We wanted to see if the technology really stood up to all the hype about RDMA to NFS4.2 performance. The interconnectivity that RoCE/RDMA provides is really outstanding. When looking to get the maximum amount of performance for our clients, enabling this was an obvious choice.” Mark Nossokoff, Research Director at Hyperion Research “Data being consumed by both traditional HPC modeling and simulation workloads and modern AI and HPDA workloads is being generated, stored, and shared between a disparate range of resources, such as the edge, HPC data centers, and the cloud. Current HPC architectures are struggling to keep up with the challenges presented by such a distributed data environment. By addressing the key areas of collaboration at scale while supporting system performance capabilities and minimizing potential costly data movement in HPC cloud environments, Hammerspace aims to deliver a key missing ingredient that many HPC users and system architects are looking for.” About Hammerspace Hammerspace delivers a Global Data Environment that spans across on-prem data centers and public cloud infrastructure enabling the decentralized cloud. With origins in Linux, NFS, open standards, flash and deep file system and data management technology leadership, Hammerspace delivers the world’s first and only solution to connect global users with their data and applications, on any existing data center infrastructure or public cloud services including AWS, Google Cloud, Microsoft Azure and Seagate Lyve Cloud.

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DATA VISUALIZATION

Opaque Systems, Pioneer in Confidential Computing, Unveils the First Multi-Party Confidential AI and Analytics Platform

Opaque Systems | December 08, 2022

Opaque Systems, the pioneers of secure multi-party analytics and AI for Confidential Computing, today announced the latest advancements in Confidential AI and Analytics with the unveiling of its platform. The Opaque platform, built to unlock use cases in Confidential Computing, is created by the inventors of the popular MC2 open source project which was conceived in the RISELab at UC Berkeley. The Opaque Platform uniquely enables data scientists within and across organizations to securely share data and perform collaborative analytics directly on encrypted data protected by Trusted Execution Environments (TEEs). The platform further accelerates Confidential Computing use cases by enabling data scientists to leverage their existing SQL and Python skills to run analytics and machine learning while working with confidential data, overcoming the data analytics challenges inherent in TEEs due to their strict protection of how data is accessed and used. The Opaque platform advancements come on the heels of Opaque announcing its $22M Series A funding, Confidential Computing – projected to be a $54B market by 2026 by the Everest Group – provides a solution using TEEs or 'enclaves' that encrypt data during computation, isolating it from access, exposure and threats. However, TEEs have historically been challenging for data scientists due to the restricted access to data, lack of tools that enable data sharing and collaborative analytics, and the highly specialized skills needed to work with data encrypted in TEEs. The Opaque Platform overcomes these challenges by providing the first multi-party confidential analytics and AI solution that makes it possible to run frictionless analytics on encrypted data within TEEs, enable secure data sharing, and for the first time, enable multiple parties to perform collaborative analytics while ensuring each party only has access to the data they own. "Traditional approaches for protecting data and managing data privacy leave data exposed and at risk when being processed by applications, analytics, and machine learning (ML) models, The Opaque Confidential AI and Analytics Platform solves this challenge by enabling data scientists and analysts to perform scalable, secure analytics and machine learning directly on encrypted data within enclaves to unlock Confidential Computing use cases." -Rishabh Poddar, Co-founder & CEO, Opaque Systems. Strict privacy regulations result in sensitive data being difficult to access and analyze, said a Data Science Leader at a top US bank. New multi-party secure analytics and computational capabilities and Privacy Enhancing Technology from Opaque Systems will significantly improve the accuracy of AI/ML/NLP models and speed insights. The Opaque Confidential AI and Analytics Platform is designed to specifically ensure that both code and data within enclaves are inaccessible to other users or processes that are collocated on the system. Organizations can encrypt their confidential data on-premises, accelerate the transition of sensitive workloads to enclaves in Confidential Computing Clouds, and analyze encrypted data while ensuring it is never unencrypted during the lifecycle of the computation. Key capabilities and advancements include: Secure, Multi-Party Collaborative Analytics – Multiple data owners can pool their encrypted data together in the cloud, and jointly analyze the collective data without compromising confidentiality. Policy enforcement capabilities ensure the data owned by each party is never exposed to other data owners. Secure Data Sharing and Data Privacy – Teams across departments and across organizations can securely share data protected in TEEs while adhering to regulatory and compliance policies. Use cases requiring confidential data sharing include financial crime, drug research, ad targeting monetization and more. Data Protection Throughout the Lifecycle – Protects all sensitive data, including PII and SHI data, using advanced encryption and secure hardware enclave technology, throughout the lifecycle of computation—from data upload, to analytics and insights. Multi-tiered Security, Policy Enforcement, and Governance – Leverages multiple layers of security, including Intel® Software Guard Extensions, secure enclaves, advanced cryptography and policy enforcement to provide defense in depth, ensuring code integrity, data, and side-channel attack protection. Scalability and Orchestration of Enclave Clusters – Provides distributed confidential data processing across managed TEE clusters and automates orchestration of clusters overcoming performance and scaling challenges and supports secure inter-enclave communication. Confidential Computing is supported by all major cloud vendors including Microsoft Azure, Google Cloud and Amazon Web Services and major chip manufacturers including Intel and AMD. About Opaque Systems: Commercializing the open source MC2 technology invented at UC Berkeley by its founders, Opaque System provides the first collaborative analytics and AI platform for Confidential Computing. Opaque uniquely enables data to be securely shared and analyzed by multiple parties while maintaining complete confidentiality and protecting data end-to-end. The Opaque Platform leverages a novel combination of two key technologies layered on top of state-of-the-art cloud security—secure hardware enclaves and cryptographic fortification. This combination ensures that the overall computation is secure, fast, and scalable. The MC2 technology and Opaque innovation has already been adopted by several organizations, such as Ant Group, IBM, Scotiabank, and Ericsson.

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