Data Analytics

With a new regulatory alert being issued every 7 minutes, growing compliance regulations are challenging banking institutions in a variety of ways. Changing customer behavior, and the constant evolution of technology is compelling them to change how compliance is approached. Ensuring compliance with a rising number of government and industry regulations can be hard-hitting and put a strain on the already drained resources. While traditional compliance models were effective for an era where simple enforcement was sufficient, today, they offer a limited understanding of business operations and underlying risk exposures. With the risk of regulatory sanction, reputation and financial loss, due to a failure to observe compliance obligations becoming extremely far-reaching, those who adapt best are the ones to enjoy a distinct competitive advantage. As each new industry regulation and its associated deadline causes a massive influx of new data that has to be stored and analyzed, garnering insights rapidly becomes vital for optimizing processes and pinpointing any potential problems areas. With compliance costing businesses $5.47 million annually and non-compliance $14 million, analytics is enabling organizations to keep pace and avoid the risk of costly non-compliance. It is helping banking organizations to stay ahead of compliance requirements, and better anticipate and respond to change. Here’s how analytics can help with banking compliance: Unearth reporting insights: Institutional banking clients, as well as regulatory auditors, constantly demand banks to reveal risk and possible exposure scenarios. Real-time analytics is a critical aspect here that allows banks to handle high volumes of data and unearth insights that meet the growing compliance needs. Using analytics, organizations can collect and distribute necessary compliance data to deliver reporting insights that are required throughout the enterprise, and meet regulatory requirements with ease. Improve risk control: Since non-compliance can result in substantial losses, analytics can help scale up the computational power of risk management. Decision-makers can ask more complex questions and get more accurate answers faster while developing new business strategies. Analytics-aided techniques can produce more accurate regulatory reports and deliver them more quickly. Since the need to pre-aggregate data is eliminated, risk managers are in a better position to understand the nuances in data, reduce fraud losses, and improve risk control across the enterprise. Enhance productivity: As banks need to be always ready to provide regulators with a quick response to regulatory stress tests, analytics plays a big role in making processes faster and more effective. Using advanced analytics, organizations can achieve faster and more accurate responses to regulatory requests and give teams analytics-driven decision support. Banks can use analytics to understand compliance levels across the enterprise, identify avenues that fare poorly, and take measures to enhance productivity and save money. Drive agility: With thousands of new regulatory requirements being ushered in every year, manually managing compliance activities is a fruitless undertaking. Manual compliance efforts are not only cumbersome and tedious, but they are also extremely prone to error. This increases the degree of risk and limits a company’s ability to meet growing regulatory requirements. Analytics allows organizations to better manage risk and compliance obligations; by aggregating data that’s needed from across the business, analytics paves the way for greater reporting accuracy and efficiency. Using analytics, organizations can respond quickly to the evolving regulatory landscape, and drive agility. Lower costs: With massive legacy and personnel costs going towards regulatory and financial reconciliation, firms have a pressing need to comply at a lower total cost of ownership. Since regulations and the market environment greatly hamper banks’ abilities to just throw money at the problem, analytics helps drive improved metrics and reporting through automation. Banks can transform raw data for cognitive and analytic processing, meet regulatory needs at a fraction of the costs, and drive higher efficiency. Effectively manage compliance Banking and other financial services companies have to contend with a variety of industry regulations and compliance requirements. As the time and cost of regulatory compliance and reporting vastly increases with every new regulation, keeping up is a great cause for additional stress – especially at a time when new competition and increasing customer demands is creeping from the sides. Advanced analytics is enabling the banking industry to become smarter in managing the myriad challenges it faces – by offering compliance officers enterprise-wide intelligence, analytics can help avoid financial non-compliance and stay a step ahead. Analytics-backed solutions are enabling banks to not only manage the increasing cost of compliance, but also the risk of non-compliance – both monetary and reputational.

The global natural language processing (NLP) market is experiencing a remarkable surge. It’s projected to reach an estimated value of $41 billion by 2025, 14 times more than what it was in 2017. NLP plays a pivotal role in bridging the communication gap between humans and machines. By combining computational linguistics with statistical, machine learning, and deep learning models, NLP enables computers to process human language in text and voice formats — comprehending not only the words but also the true meaning, intent, and sentiment behind the communication. In this article, we delve into how NLP goes beyond text and delves into the captivating realms of image and speech processing. NLP Beyond Text NLP, traditionally associated with text processing, has now ventured into the realms of image and speech, revolutionizing data analysis and communication. Processing Images with NLP Advancements such as multi-atlas segmentation, fuzzy clustering, graph cuts, genetic algorithms, support vector machines, and deep learning have greatly improved image analysis. NLP techniques now enable computers to interpret images, recognize objects, and generate descriptive captions. This way, these techniques contribute to content accessibility and enrich image search engines. Processing Speech with NLP Speech recognition, or speech-to-text, poses unique challenges due to the complexities of human speech. However, despite the intricacies in accent, intonation, and grammar, NLP algorithms efficiently convert voice data into text. Additionally, part-of-speech tagging allows NLP models to identify the grammatical role of words based on context. All in all, NLP’s application of deep learning and neural networks has led to the creation of spoken dialogue systems, speech-to-speech translation engines, sentiment analysis, and emotion identification. These advances empower innovative solutions like mining social media for health and finance information and revolutionize how we interact with technology and analyze data. Applications of NLP in Image and Speech Processing The fact that NLP can now help with image and speech processing is groundbreaking for so many reasons. Here are some of the most prominent applications: 1. Image Captioning Image captioning combines computer vision with NLP to generate descriptive and contextual captions for images. Leveraging deep learning techniques, NLP models can analyze the visual content of an image and generate natural language descriptions. This application finds extensive use in: Content accessibility Enriching image search engines Aiding visually impaired users in comprehending image content The underlying NLP models process the image data to recognize objects, actions, and scenes, thus producing coherent and informative captions for better human understanding. Also Read: A CXO’s Guide to Collaboration Between Citizen Data Scientists and Data Science Teams 2. Visual Question Answering (VQA) VQA is an intriguing application where NLP models enable machines to comprehend and respond to questions about images. Through NLP-powered algorithms, the model processes the image and the accompanying question to generate an accurate textual answer. This multidisciplinary approach involves image feature extraction, question parsing, and reasoning capabilities, making it a challenging yet valuable task. VQA finds applications in interactive visual systems, educational tools, and AI-driven assistive technologies. 3. Speech Recognition NLP-driven speech recognition is at the core of voice-enabled systems and speech-to-text applications. Applying deep learning architectures, NLP models can transcribe spoken language into written text with impressive accuracy. The underlying techniques involve: Acoustic modeling to capture speech patterns Language modeling to understand the context and grammar of the spoken content. This technology is extensively employed in virtual assistants, transcription services, and voice-activated devices. 4. Natural Language Generation (NLG) NLG is a powerful application that allows machines to generate human-like natural language text. In image and speech processing, NLG can be utilized to create textual descriptions for images or convert textual data into spoken language. The combination of NLP techniques with machine learning models empowers systems to generate coherent and contextually relevant narratives. NLG has various applications, such as generating detailed reports from data visualizations, creating personalized product recommendations, and enhancing the user experience in conversational interfaces. 5. Machine Translation Machine translation is a classic NLP application that has been extended to handle multimodal data. In image and speech processing, NLP models can translate image captions or spoken content from one language to another. This entails encoding the visual or auditory input, followed by language translation using sophisticated machine translation models. Multimodal machine translation is valuable in scenarios involving multilingual image retrieval, cross-lingual speech transcription, and enhancing global communication. But There Are Challenges as Well All the above applications exemplify the synergistic potential of NLP in image and speech processing. They, well and truly, bridge the gap between unstructured multimedia data and human-readable text. However, NLP initiatives may face three primary hurdles: language, context, and reasoning. Language poses a challenge as current applications treat text as data rather than understanding it as humans do. Another challenge pertains to context comprehension, as it requires algorithms to focus on language structure, not just individual words — a deficiency in many existing applications. Then there’s the need for verifying the history and reasoning employed by NLP algorithms to arrive at conclusions, which can be daunting. Of course, overcoming these obstacles is crucial to enhance the performance and capabilities of NLP systems. How Can Rubiscape Help? Rubiscape is a modular and comprehensive platform that offers a wide range of tools and features for managing the data science lifecycle. It equips businesses with the resources to expedite data preparation, feature engineering, and model training, thus saving time and effort in developing NLP systems. Further, Rubiscape supports scalability — an immensely viable facet for NLP applications that require real-time processing of image and speech data. So, if you are looking for a powerful and flexible platform to help you develop NLP systems for image and speech processing, look no more. Connect with us today to get started!

Data analytics is becoming increasingly integral to business success, with many companies deploying analytics in new and innovative ways. After all, data insights provide organizations with the ability to make better strategic decisions, transform products and services, and create a differentiated customer experience. But how do you measure how much value data analytics initiatives are really bringing to the table? Although it may not be an easy answer, this article provides a framework for assessing the ROI of data analytics initiatives. Key Focus Areas for Measuring the Value of Data Analytics Direct data monetization, quicker time to market, etc., are a few important factors to take into account when evaluating ROI for data science projects, but they are not the only ones. Organizations can obtain a comprehensive image of the ROI of data and analytics initiatives by taking into account the following pertinent aspects: Financial Metrics The economic impact of data analytics initiatives can be evaluated using financial indicators that reflect the overall value of insight gained and the impact on the bottom line. For example, you can measure success by calculating the total revenue generated as a result of a data analytics initiative. You can also determine whether cost savings or additional profit was created through increased revenue generation. Some key metrics to consider here are Intrinsic Value of Information (IVI), Business Value of Information (BVI), and Performance Value of Information (PVI). These metrics measure the information value in terms of the impact on the business performance and success. A key focus should also be on cost reduction. Cost reductions can be a result of process simplification, reduced risk, or improved performance. It can also be a result of increased revenue or reduced operating costs. At the end of the day, the ROI of the data analytics projects should reflect the change in cost structure. In other words, it should show if the initiatives have generated savings on the cost of training, services, technology, etc. Impact on Decision-Making To only consider the numerical impact of data analytics initiatives on the bottom line can be misleading. In order to fully understand the value of data analytics, it is important to measure the impact that data-driven insights have on an enterprise’s decision-making process. But how is this “impact” measured and tracked? —For one, the impact can be evaluated based on the accuracy of decisions and forecasts. For example, if a data analytics initiative enables you to make better decisions in real-time about your product pricing, that can translate into improved margins and revenue. —Impact can also be assessed based on how well the customer needs and preferences are looped into the decision process. After all, the overarching goal of data analytics initiatives is to enable organizations to better align their offerings with user requirements. —Finally, enterprises can measure the impact based on how quickly and easily insights from internal and external sources are leveraged to make a better decision. Is it an easy process to identify insights and access data from all sources? Can you easily transform the data into information and then convert the information into intelligence or insight for a particular use case? Overall, by evaluating how well the insights are translated into strategic decisions that improve customer experiences, optimize processes, and stimulate revenue growth, organizations can determine the overall value of the data analytics initiatives. Also Read: A CXO’s Guide to Collaboration Between Citizen Data Scientists and Data Science Teams Usage of Resources This indicator assesses how effectively infrastructure, staff, and computational capacity are allocated for data science projects. For assessing the usage of resources and how that plays into the success of data analytics initiatives, organizations must: Analyze the speed at which data initiatives pay off. They can take into account how long it takes to design, deploy, and begin experiencing advantages. Evaluate the efficiency with which resources are allocated among the many stages of a data project, including data collection, processing, analysis, and implementation. Examine the data projects’ overall adaptability and scalability. It’s essential to check if the resources involved are flexible enough to accommodate changing project requirements. Customer Experience Data-driven improvements that result in positive consumer experiences can promote brand loyalty and business expansion. So, customer experience metrics that assess the influence of data analytics initiatives on customer engagement and satisfaction are immensely useful. Lower churn rates or higher client lifetime value are some examples of metrics that can be worthwhile considering in this regard. For a granular comprehension of the value created, businesses can gauge how data-driven innovations affect customers’ opinions of the brand, products, and services by monitoring measures like Net Promoter Score (NPS) or Customer Satisfaction Score (CSAT). Apart from all these focus areas, businesses can also concentrate on: Assessing the time to market of product or service launch and improvements Evaluating the ROI of specific use cases and user groups Analyzing the value of data-driven initiatives from a regulatory standpoint Tracking changes in customer behavior patterns due to data-driven improvements Monitoring changes in customer responses to data-driven campaigns such as personalized email campaigns or targeted ads In a Nutshell The ultimate objective of assessing the ROI of data science projects is to bring about a significant and measurable impact on the organization’s performance. By evaluating the effect on business operations and calculating the cost savings and revenue generated by data analytics initiatives, businesses can gauge whether their investments increase profitability and efficiency. They can then proceed towards becoming data smart. But what if they can realize success with analytics initiatives right off the bat? That’s where leveraging a unified data platform like Rubiscape becomes critical. Not only can it democratize data access, but it also drives data-backed innovation, increases data literacy across the board, and weaves agility into data science executions. Get in touch with us to learn more. Linkedin X-twitter Facebook