Top 7 Analytics Use Cases for Aedeon Data Lake

Marketing analytics is analyzing marketing activity to drive business decisions related to customer interactions, understand the ROI of marketing efforts, and identify opportunities for improvement. One of the key challenges facing marketing analytics teams is the variety and volume of data collected across a distributed workforce or digital channels. The data is captured in various formats and has to be processed based on the unique requirements of business analytics and data science teams. Aedeon data lake provides these users with a real-time platform that ingests this data and allows users to build unique use cases, sharing data across teams and maintaining an up-to-date data repository for marketing analytics use cases in line with organizational governance protocols identity and access management and data quality.

It is challenging to find an accurate 360 view of your target audience considering the variables around customer personas and the limited availability of targeted, high-quality customer data. Additionally, organizations must establish a data analytics pipeline in compliance with stringent global regulations such as GDPR. The process of data collection involves data capture across multiple digital channels and can take weeks to set up, update and manage for a consistent and complete view. Aedeon Data Lake brings the scattered sources together and available for analytics processing from a centralized data repository. The data lake is built on the Amazon S3 platform, which offers multiple storage tiers for various price points, performance, and use case requirements.

An enterprise IT network comprises a complex infrastructure of hardware devices, application components, and IT service nodes. At every node, extensive metrics log data is generated in real-time and contains patterns and insights about the network traffic, application, and system performance. The challenge for business analytics teams evaluating network performance is twofold: the large volume of real-time data streams must be captured and integrated across disparate locations, and the insights must be drawn in real-time to take suitable control actions proactively. These actions can be automatically triggered or provide relevant alerts to the engineering teams. The Aedeon data lake provides a mechanism of ingestion using AWS Glue, Amazon Kinesis, and Amazon Managed Streaming for Apache Kafka (MSK) serverless services for batch and real-time data processing at scale. The data is cataloged and available immediately for discovery, cleansing, modeling, and other crucial analytics processes for real-time decision-making.

Cybersecurity is focused on a proactive approach to monitoring the performance of IT networks, applications, and systems and defending against cyber-attacks. Considering the sophistication of modern cyber-attacks and risk vectors, enterprises need rich sources of real-time metrics data and the ability to perform real-time analytics at scale. Data sources include user behavioral data, network logs, operating system and application event logs, hardware-related data from networking devices, and contextual information from past analytics. Aedeon Data Lake provides analytics and data science teams with a platform to run advanced machine learning algorithms and develop AI models using large volumes of data, which trains and adapts these models in real time. This helps users keep on top of the cyber risks proactively.

To maintain service dependability and high-value end-user experience, technology-driven organizations need real-time intervention capabilities based on monitoring events and proactively reacting to IT incidents that impact service dependability. They need to investigate application and service performance patterns and reach out with configuration changes before the issues affect end users. The changes may contain several correlations with past solutions on similar incidents and the behavior of traffic patterns and log metrics. Aedeon data lake allows organizations to maintain a data catalog and manage metadata to gain true context of the issues. Features like the data lake lineage provide context, including data sources, methods to build and aggregate data workloads, and how data evolved. This information allows support teams to identify and apply solutions to recurrent issues and root cause isolation and analysis.

Predictive maintenance is an important analytics use case to support the performance and efficiency of industrial systems that operate optimally within a limited life cycle. This life cycle can be extended or maintained effectively by conducting maintenance procedures proactively as soon as anomalous patterns of performance inefficiency are identified. For instance, utility industries need data analytics to monitor equipment for continuous electricity supply, pharmaceutical industries measure asset performance to prevent process malfunctions affecting critical drug production, and power generation companies use preventive maintenance plans to prevent blackouts and power outages. Predictive maintenance relies on data generated by IoT devices and network nodes that provide real-time information on various parameters. This information is generated in silos – the sources are abstracted from unauthorized users to ensure the security of critical industrial assets and infrastructure. Aedeon data lake allows these organizations to integrate multiple data sources while enforcing strict access controls and maintaining data quality. This ensures that only authorized users access accurate information for data processing and can execute preventive maintenance procedures based on the true performance of affected systems.

A recommendations engine or analytics pipeline that allows organizations to optimize business processes and capture end-user attention requires an advanced data platform. This platform should be capable of simplifying data onboarding, reducing data consumption overload, eliminating data silos, enhancing data usability, and enforcing key controls for data security, governance, and quality. These key capabilities power the Aedeon Data Lake platform and keep it from transforming into a data swamp as users import large volumes of real-time data streams for predictive and prescriptive analytics processing. The platform provides end-to-end data lifecycle management to ensure that analytics and data science teams can deploy complex models without investing efforts into mundane data management tasks. This results in faster time to insights using a production-ready Aedeon data lake platform that fully enables, engages, and governs sensitive data workloads for predictive analytics use cases such as recommendation engines and business process optimization.

Are you interested in learning more about a data lake solution for cost-effective, secure, and efficient analytics processing on real-time data streams? Let’s talk today to accelerate your digital transformation journey with a data lake solution that solves your organization's unique data analytics challenges.