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EDW Modernization
Enterprise data warehouses continue to evolve as they promote modernization, owing to ever-increasing data relevancy within the business realm. Traditional data warehousing can help to attain complete and clean data at a small scale. However, it suffers in terms of the ability to scale to big data, conduct real time data operations, explore data, handle new data types and conduct efficient data flows for advanced analytics. The format variety and heterogeneity of data have contributed significantly to the need for modernization. A modern data warehouse is designed to address all formats (structured, semi/unstructured) of data and take advantage of cloud technologies to deliver scalable solutions and apply machine learning to enable a deeper understanding of data.
Features of Enterprise Data Warehouse Modernization by Stacktics
An EDW enterprise data warehouse is intended to be a single repository for all of an organization’s data, in contrast to a smaller data warehouse which may be specific to a business department or line of business (like a data mart).
- Multiple Data Platform Types: Users can opt to choose a platform type depending on their use case, such as a relational database to access and process data using Structured Query Language (SQL). Modern data platforms like Hadoop or cloud-native storage can be used to provide support for unstructured and multi-structured data. There are other data platforms that provide data processing in the platform itself for exploration and analysis.
- Cloud and Hybrid Platform Strategies: Data warehousing on cloud platforms involves the migration of data warehouses to the cloud to leverage benefits like scalability, elasticity, lower cost, minimal system integration, stability, reliability and high availability. There are use cases where the data warehouse infrastructure would involve a hybrid combination of on-premise and cloud-based systems.
- Computing and processing: The support of separating computer functions from storage allows optimized investment in modern data warehouse infrastructure. The total separation and the ability to access data in any tier of storage can produce significant advantages in the total cost of ownership.Typically cloud providers charge a higher rate for computing services compared to storage so an organization can spin up additional computing resources only when needed for computing intensive workloads, and accordingly spin down when the workloads are completed. This saves on cost while maximizing the computing performance.
- Business, Analytics and Report: Business modernization involves making business decisions based on advanced analytics, using techniques like data mining, statistics and natural language processing. Usage of machine learning and artificial intelligence has been advocated for predictive analysis to get optimum business value. Reporting has also been revolutionized by personalizing it for the users, offering visual representations and tailoring them around Key Point Indicators (KPIs).
Vendors Offering Data Warehouse Services
The main vendors offering data warehousing services are Google, Amazon, Microsoft and Snowflake Cloud.
- Google provides BigQuery for serverless data warehousing, enabling scalable analysis over petabytes of data.
- Amazon provides Amazon Redshift which helps to achieve efficient storage and optimum query performance.
- Microsoft provides Azure SQL data warehouse which is a managed service with a relational data store and real-time reporting capabilities.
- Snowflake is built on top of the Amazon Web Services, Microsoft Azure, and Google cloud infrastructure. The Snowflake architecture allows storage and computation to scale independently, so customers can use and pay for storage and computation separately. The sharing compatibility makes it easy for organizations to quickly share secure data in real-time.
Pros:
- Data visualization and exploration help in developing analytics-based applications.
- Analytics has helped in expediting decision-making and operational efficiency.
- Real-time analytics has aided the frequent generation of reports and analysis cycles, operating in near real-time.
Cons:
- Data warehouse modernization can be discouraged by the additional cost involved in implementing and maintaining new technologies with hardware and software upgrades.
- Internal technical teams might suffer from incomplete technical skills for data warehousing and other new technologies.
- Steep learning curve, drawing out implementation time-to-value.
Intelligent Search
Intelligent Search leveraging artificial intelligence technology eliminates data silos by finding the correct information conveniently. Intelligent search can extract information from anywhere regardless of the format: big data in databases, document management systems, digital content, webpages, on paper etc. Intelligent search, unlike traditional search engines, can extract information and provide answers specific to the business. It carries the ability to understand human language with its natural language processing capabilities, learn and understand document structure, leverage machine learning to continuously improve search query result relevancy over time, predict what information will provide the most value to users, and more.
Intelligent Search offers the ability to discover hidden insights from unstructured text data to drive decision, create a simple team-wide enterprise search platform to put relevant information at fingertips and provides customers services at scale with a customer self-serve capability to reduce customer support costs. These aspects work exceptionally well to ultimately improve customer satisfaction.
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