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CloudThat, in collaboration with AWS, hosted the webinar on ‘The Role of Data Analytics in Intelligent Decision Making’. We had the pleasure of hosting Rajdip Chaudhuri, Senior Partner Solutions Architect for Data & Analytics at AWS India, and Arihant Bengani, Cloud Solution Architect for Data & IoT at CloudThat, as our speakers. Each of our speakers shared exciting and informative unique skills and experiences with the audience.
In today’s fast-paced world, businesses are constantly inundated with data from various sources. From customer behaviour to market trends, the amount of data available can be overwhelming. However, with the help of data analytics, businesses can turn this flood of information into valuable insights that drive intelligent decision-making. In this blog, we will explore the role of data analytics in intelligent decision-making and how it transforms how businesses operate. We will explore the challenges with Data, the Business Value of Data, Modern Data Strategy, Security of Data Lake-AWS Lake Formation, and Customer Case Studies.
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Challenges with Data Management
Legacy data solutions are outdated methods and technologies, such as on-premises databases, file systems, or spreadsheets, that may lack scalability, security, or advanced features. Legacy data solutions can pose challenges in maintenance and upgrades, lack of compatibility with newer technologies, and limitations in handling the growing volumes of data. Let us discuss in detail:
- Data Acceptance and Understanding – Legacy data solutions often lack a clear understanding of the data being processed, leading to inconsistent and inaccurate results. To overcome this challenge, organizations should focus on data acceptance and understanding, which involves analyzing the data for business needs and technical and operational aspects and ensuring an understanding of how it is collected, stored, and used.
- Understanding Technological Needs – Legacy data solutions may not align with an organization’s technological needs, leading to slow, inefficient, or non-scalable data solutions. To address this challenge, organizations should understand their technological capabilities and requirements, such as hardware and software infrastructure, data storage and processing capabilities, and specialized tool needs with respect to expense, time, and effort.
- Data Security and Compliance – Legacy data solutions may lack modern security and compliance requirements, leaving data vulnerable to breaches and unauthorized access. Organizations should prioritize data security and compliance by implementing appropriate measures, such as encryption and access controls, to comply with relevant regulations and standards.
The Business Value of a Modern Data Strategy and Data-Driven Organization
Data is vital for organizations seeking to reinvent themselves, enabling better decision-making and innovation. However, many struggle to leverage it effectively, despite its potential to drive growth and increase net income. Being data-driven is essential for future competitiveness, with businesses experiencing annual growth rates over 30%. A data-driven culture is crucial for unlocking value, but only 28% have a comprehensive strategy for utilizing analytics tools and infrastructure, while 55% rely on manual approaches. Despite challenges, some organizations are thriving even amidst the COVID-19 pandemic.
Modern Data Strategy
Modernizing the data infrastructure from a legacy solution to a scalable and secure cloud provider is important. This helps reduce operational overheads with the use of purpose-built, cloud-based databases. Additionally, modern analytics tools that can handle structured, unstructured, and streaming data at scale should be adopted. To harness the full benefits of machine learning, it’s recommended to standardize on a modern ML infrastructure.
AWS modernizes data and workloads at all stages of the data journey, offering secure and scalable solutions. They handle management tasks for legacy on-premises or cloud data stores, provide purpose-built data stores for modernization, enable the rapid building of scalable data lakes, offer compliance solutions, and accelerate Machine Learning innovation with standardized processes and faster model development times.
To transform a business with data, companies must break down data silos and make them accessible and shared securely to unlock their value. AWS provides a solution to connect data stores, allowing for fast decision-making and unified security and governance across the enterprise. Purpose-built analytics and visualization services help organizations move from insights to actions faster, extracting the most value from their data.
Companies use microservices architecture for modular independent components, and AWS provides purpose-built databases for specific use cases and integrates ML into their data-related services. Amazon Redshift ML and Amazon Athena ML enable data analysts to run ML on their data without building or training an ML model. At the same time, Amazon QuickSight Q generates a data model using ML to help business analysts ask questions in plain language and receive near-real-time answers.
Modern Data Strategy in Action
Implementing a modern data strategy on AWS offers several key benefits for businesses. These include scalability, which allows companies to adjust their data infrastructure as needed without investing in costly on-premises hardware, cost savings through only paying for necessary data storage and processing resources, improved performance via AWS’ high-performance data services, enhanced security through data encryption and monitoring tools, increased agility through faster project and experiment launch times, and advanced analytics capabilities through machine learning tools like Amazon SageMaker and QuickSight. This strategy can help businesses stay competitive in a data-driven world by offering crucial advantages.
AWS's Comprehensive Suite of Data Analytics Services
AWS offers a comprehensive suite of data analytic services to help businesses gain insights from their data, make data-driven decisions, and stay competitive in a rapidly changing business environment. Here are some of the key services:
- Amazon Redshift – A fully managed data warehouse that allows businesses to store and analyze petabytes of data. Amazon Redshift can handle large-scale data analytics workloads and offers fast query performance.
- Amazon EMR – The platform is managed and designed for big data processing, making it simple to handle large volumes of data with popular open-source frameworks like Apache Spark and Hadoop. Amazon EMR can help businesses run complex data analytics workloads at scale.
- Amazon Athena is an interactive query service without servers that enables businesses to analyze data stored in Amazon S3 using standard SQL queries. Amazon Athena eliminates the need for complex ETL processes and allows users to analyze data in real time.
- Amazon QuickSight – A cloud-based business intelligence service that allows businesses to create and share interactive dashboards and visualizations. Amazon QuickSight integrates with various data sources and can help enterprises make data-driven decisions.
- AWS Glue – A fully managed extract, transform, and load (ETL) service that allows businesses to move data between data stores. AWS Glue can automate many ETL processes and help businesses save time and money.
- Amazon Kinesis – A platform for real-time streaming data processing. Amazon Kinesis can help businesses ingest and process large amounts of streaming data, such as IoT sensor data or social media feeds.
- Amazon MSK – AWS MSK (Managed Streaming for Apache Kafka) is a fully managed service, making it simple to develop and deploy Apache Kafka applications on a large scale.
- AWS Lake Formation – Amazon Lake Formation is a managed service on AWS that simplifies the creation and management of secure data lakes. The service automates the setup process, facilitates data access controls, and ensures compliance with regulations.
Line Clear Express – Case Study
Line Clear Express is a comprehensive supply chain management expert that offers efficient and dependable delivery services throughout Malaysia. Their services include warehousing, pick and pack, last-mile delivery, and track and trace, ensuring seamless and reliable delivery nationwide.
After CloudThat assessed the client’s challenges, the client faced challenges accessing and managing dispersed raw user data, lacking a dedicated team for data engineering and BI. This limitation has affected their ability to make informed business decisions, hindering their growth.
CloudThat provided the client with the solution, i.e., data from multiple sources was integrated into a central repository, processed and refined using Databricks ETL and EDA jobs, and exported to S3 for near Real-Time reporting on QuickSight with 15-minute refresh rates. Interactive dashboards were integrated into the client’s applications, and multiple users were given access through AWS Cognito.
The webinar provided valuable insights into the latest trends and technological advancements, showcasing how they can be leveraged to address business challenges and achieve growth. It also highlighted that businesses could empower to optimize processes, improve efficiency, and enhance customer experiences, ultimately leading to increased revenue and profitability.
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Drop a query if you have any questions regarding Data Analytics with AWS, and I will get back to you quickly.
1. How can we build a Multitenant platform?
ANS: – A multitenant platform in AWS can be built using a combination of services like Amazon EC2, Amazon S3, Amazon RDS, AWS Lambda, Amazon Cognito, and Amazon API Gateway. By leveraging these services, developers can create a platform that can serve multiple tenants, each with its own isolated environment, data, and user management. This can be achieved through resource tagging, VPC isolation, and data partitioning. With these techniques, developers can ensure that each tenant’s data is kept separate and secure while allowing for efficient platform management and scaling. Overall, building a multitenant platform in AWS requires careful planning, architecture design, and security considerations to ensure the platform is scalable, secure, and easy to manage.
2. How retail industry can take advantage of Data Analytics for product placement in the market?
ANS: – The retail industry can use data analytics in AWS to improve product placement by analyzing customer data to optimize store layouts, product assortments, pricing, and promotions. With services like Amazon EMR, Amazon Redshift, Amazon Personalize, Amazon Kinesis, AWS Lambda, and Amazon QuickSight, retailers can analyze customer preferences, behaviours, and purchase history to deliver personalized recommendations, process real-time data, and visualize data for better insights. By leveraging data analytics, retailers can make data-driven decisions about product placement and promotions, improving the customer experience and increasing revenue.
3. How can data analytics be done for facial recognition technology?
ANS: – AWS offers tools and services like Amazon Rekognition, Amazon SageMaker, and Amazon Kinesis that can be used to analyze facial recognition technology. With these tools, businesses can detect and recognize faces, track and analyze facial expressions and emotions, compare two faces for similarity, analyze facial features and use facial recognition for security purposes. The insights gained from these analytics can be used to improve customer experience, understand customer preferences, and enhance security applications.
WRITTEN BY Arihant Bengani
Arihant Bengani is a Cloud Solution Architect leading the vertical of Data, AI and IoT for Tech Consulting at CloudThat. He is a Technology Enthusiast, AWS Data Analytics Speciality Certified and AWS Solutions Architect Associate Certified. He has published many tech blogs related to AI/ ML, IoT and Data Analytics.