Summary
ACI Learning, a leading provider of training solutions, faced a daunting challenge – a complex and scattered data landscape. Read how, through an in-depth assessment and strategic planning, ExistBI stepped in to provide a clear roadmap towards a unified data platform, developed a comprehensive architecture for an enterprise data warehouse, and set the stage for ACI Learning’s data transformation journey to unlock a holistic view of their operations and performance.
About ACI Learning
For over 40 years, ACI Learning has been training leaders in Cybersecurity, Audit, and Information Technology (IT). Operating under the banner of LeaderQuest and MIS Training Institute (MISTI), ACI Learning equips enterprises, professionals, and new employees in audit, IT audit, cybersecurity, protection, and readiness with the expertise to protect data and privacy, anticipate risks, and defend business-critical systems and assets.
Challenge
ACI Learning’s data landscape is composed of ten different applications and systems, all of which contribute to a siloed environment. The company recognized that newer technologies would enable ACI leadership and business team members to have a comprehensive view of business operations, a clear understanding of company performance, and enhanced data analytics capabilities.
ACI Learning sought a partner with a proven track record in data migration, data warehousing, analytics, and reporting to lay the groundwork for a modern data platform.
Solution
ExistBI was selected to tackle this challenge. Drawing on their extensive expertise, ExistBI undertook an in-depth assessment of ACI Learning’s data landscape and developed an architecture for an enterprise data warehouse (EDW). This EDW would be capable of integrating data from a wide array of sources, including Salesforce, NetSuite, XERO, and Acterys, and applying analytics to extract valuable insights.
After consultation with sales, customer, finance, and marketing teams, Microsoft Azure SQL Managed Instance, Microsoft Azure Data Factory, Microsoft Azure Storage Gen 2, and Microsoft Power BI were identified as the optimal tools for this task. Together, they provided a robust and scalable solution for data storage, ETL, data analytics, and reporting.
Data warehouse – Microsoft Azure SQL Managed Instance
Microsoft Azure SQL Managed Instance is designed to host SQL Server databases in the cloud without the need for customers to manage the underlying infrastructure. It combines the benefits of Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) to provide a convenient and flexible database hosting solution for organizations of all sizes.
Since it is a fully managed service, companies reduce administrative tasks and overhead by relying on Microsoft to take care of managing the infrastructure, including backups, patching, and high availability. Each managed instance has a dedicated set of resources (CPU, memory, storage) and is isolated within the Azure environment for better security and performance. Built-in security features like firewall rules, virtual network integration, and encryption protect an organization’s data.
Managed Instance allows organizations to scale compute and storage independently, providing flexibility to adjust costs and resources based on workload demands.
ETL and data integration service – Microsoft Azure Data Factory
Microsoft Azure Data Factory (ADF) is a cloud-based data integration service that enables organizations to create, schedule, and orchestrate data workflows that involve the extraction, transformation, and loading (ETL) of data from various sources into data stores and analytical systems. It supports a wide range of data sources and data sinks, and its data transformation tool allows organizations to perform data cleansing, filtering, aggregating, and other data manipulations.
Azure Data Factory supports various integration runtimes to execute activities in the pipelines. Azure Integration Runtime is the default runtime for most scenarios, while Self-hosted Integration Runtime allows data movement between on-premises data sources and cloud data stores. Schedule the execution of pipelines based on time or external events using triggers to automate data workflows and update data regularly.
Data lake – Microsoft Azure Storage Gen 2
Microsoft Azure Storage Gen 2, also known as Azure Blob storage with hierarchical namespace (HNS), offers enterprise-grade features for big data analytics and data warehousing scenarios with file system-like organization. It can handle massive amounts of data and high transaction rates, making it suitable for various data-intensive applications.
Azure Storage Gen 2 is commonly used for general-purpose object storage, including unstructured data, such as images, videos, documents, backups, logs, and any other large files. Data scientists and analysts use it as a data lake to store and analyze large volumes of structured and unstructured data. It can also be used to store rarely accessed or archived data at a lower cost.
Reporting – Microsoft Power BI
Microsoft Power BI is a powerful business intelligence (BI) and data visualization platform that allows users to connect to a wide variety of data sources, transform and clean the data, create interactive data visualizations, and share insights across an organization. It is designed to empower both business users and data professionals to make data-driven decisions by providing them with a user-friendly and feature-rich environment for data analysis and reporting.
One of the key strengths of Power BI is its interactive data visualization capabilities. Users can create a wide range of charts, graphs, maps, tables, and other visualizations to represent data and gain insights quickly. The visuals are highly customizable and can be easily created through a drag-and-drop interface. Users can also build interactive reports and dashboards that consolidate multiple visuals onto a single canvas. Shareable dashboards provide a high-level overview of key performance indicators (KPIs), while reports offer more detailed insights into the data.
Power BI supports natural language querying, which allows users to ask questions in everyday language and get responses represented in data visualizations. This makes data exploration more intuitive and accessible.
Results
Following the assessment, ExistBI delivered a comprehensive cloud-based EDW architecture to ACI Learning. This blueprint outlined a clear roadmap to ingest data from numerous sources, develop and test integrated data models, apply governance and security rules, and develop and test dashboards and reports. With this architecture, ACI Learning is now equipped with a strategic plan to unify its data landscape and extract actionable insights.
Having completed the assessment phase and armed with a clear data architecture, ACI Learning is ready for the next step – implementation. This planned phase promises to revolutionize their data landscape, providing a unified view of operations and performance that will enable them to make more informed, data-driven decisions.
Ready to take control of your data? Book a free consultation with our experts at ExistBI today and start your journey towards better data management.
