Many applications today rely on non-relational data stores, as there is no single data store that can handle all aspects of modern analytics. For example, a user might employ an RDBMS to store a dataset but then need to perform a cluster analysis. Because relational databases are not optimized for such tasks, the data will be moved to another system for processing.
Data from different sources needs to be analyzed together to get a holistic view of what is happening. As data resides in different places, it needs to be moved from one place to another so that it can be analyzed on the same platform. Nowadays, organisations face the tedious task of manual data movement between different storage platforms and integrating different processing platforms. Nowadays, it is not rare for organisations to write ad hoc programs or scripts to move the data out of the database and integrate different platforms to deal with this problem semi-automatically.
Complex data analytics in retail
Databloom Blossom is a data management platform that breaks up complex data analytics into multiple pieces and selects the right processing platform to execute each of them. It can also take care of any data movement and transformation required to perform these pieces of computation on the right processing platform. Importantly, Blossom can complement the capabilities of data processing platforms with each other to enable them to perform complex analytics. For example, it can complement a DBMS with the ML capabilities of Apache Spark to perform a clustering task over the DBMS data. All this in an invisible manner for the data and BI teams.
Enable data driven sales with federated learning
One of the world's largest retailers has increased sales by more than 22% per year using data-driven models. To build these models, the company's data team designs, builds, and constantly maintains large datasets that predict products customers might be interested in based on their search history and purchase history combined with data acquired in local stores. For example, the team can consider all of an order's history—from its beginning when it was placed until there was a return or exchange—as well as customer relations from the company's CRM system, returns from its sales system, quality measurements from stores' quality management system and supplier data from its financial obligations system.
Different data pools are needed to build a model that identifies patterns by considering the purchase of all customers in the past as well as the profile of the customers (e.g., country/city of residence), combined with forecasting and warehouse delivery predictions to enable a nearly perfect customer experience. The data team updates the model as new purchases, products, customers come every day, even every few hours depending on the customer’s base size. Data analysts can perform further analytics on the database itself as well as on a machine learning platform to understand the current trends in product purchase for marketing campaigns as well as for supply chain logistics. Achieving this leads the data analyst to run analytics over databases (e.g., data warehouses) and to move data out of those databases to a machine learning platform (e.g., PyTorch) to learn predictive models.
The picture below illustrates how Blossom enables retailers to acquire more customers, operate their stores efficiently, and increase the customer experience with the brand.
Federated AI in action: textural sentiment analysis
Sentiment analysis, also known as opinion mining, is a natural language processing task that aims to determine the sentiment or emotional tone of a given text. It is a popular application in today's digital world, where people express their opinions and emotions on a wide range of platforms, including social media, online reviews, and forums.
Textural sentiment analysis is a specific type of sentiment analysis that focuses on understanding the underlying emotions and opinions expressed in a given text. This can include identifying positive, negative, or neutral sentiment, as well as more specific emotions such as joy, anger, or sadness. Let's look at how this use case applies to the retail industry.
A company can use sentiment analysis to gauge how satisfied its users and customers are with the products and services it provides. To build such a model, the model first categorizes data points based on what insights the data team wants to gain. For example, time is relevant when discussing user activity patterns; frequency is relevant when talking about product views; rapidity of sale is relevant when discussing sales velocity. The model must then extract user and comment data from the databases and data stores, data warehouses or data lakes and create a sentiment analysis model for each user group in order to classify each comment as positive or negative. Finally, creating a sentiment analysis model for each user group in order to classify each comment as positive or negative. Such sentiment analysis enables organizations and agencies to assess what customers think about your products, services and sales processes.
Textural sentiment analysis is a powerful tool for understanding the emotions and opinions expressed in text. With the advancement of natural language processing, machine learning and deep learning techniques, it has become more accurate and efficient in understanding the context of the text and providing meaningful insights from it. However, there is still room for improvement in this field and researchers are working to make it more accurate and efficient.
Thanks to its design, optimizer, and executor, Blossom can provide a real federated data framework from the beginning and shorten the path to a working NLP AI:
- Heterogenous Data Sources. Blossom process data from (or over) multiple data sources in a seamlessly manner;
- Multi-Platform and Hybrid Cloud Execution. Blossom automatically deploys each sub-part of a pipeline to the most relevant cloud provider and processing platform in a seamless manner to reduce costs and improve performance;
- Federated Machine Learning and AI. Blossom comes with its own framework (including a parameter server) to run pipelines in a federated manner;
- Ease of use. Blossom allows users to focus on the logics of their applications by taking care of how to optimize, deploy, execute their pipelines.
Databloom is a federated data access and analytics company that develops the federated analytics platform "Blossom Sky" to enable decentralized AI. It provides fast and interactive enterprise-ready distribution, consisting of additional tooling and configurations, enabling data scientists and analysts to run AI models and training against various decentralized data sources ranging in size from gigabytes to petabytes. Databloom is a leading contributor to Apache Wayang, the federated data processing engine.
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