Data warehousing has long standardized and organized data for business intelligence (BI) reporting. Today, companies see growing value in more predictive techniques like machine learning (ML) and data science (DS).
Can data warehouses meet the demands of these innovative analytical approaches as well? Let’s delve into the transformative potential of data warehousing now.
Data Warehousing – Traditional Role and Methods
Data warehouses and data marts assemble data from operational systems spread across an organization into one central database. It applies structure for querying and reporting by cleaning and mapping inconsistent formats. Data warehouses have been an essential part of basic business intelligence to enable factual company data analysis.
The warehouse stores current as well as historical data over the years in a way easy to slice by date, product, region, etc. Special database platforms like Teradata added performance for concurrent complex analytical workloads not suitable in say a transactional CRM system. Reports, visualizations, and summaries help managers understand financials, track KPIs, and make other strategic decisions. Thus, for many years, data warehousing supported descriptive and diagnostic analytics using BI tools.
Emergence of Machine Learning and Data Science
Artificial Intelligence and Machine Learning drive increasing interest today, unlocking unprecedented business forecasting through predictive analytics and even prescriptive recommendations. Deep machine learning utilizes regressions and clustering for behavioral analysis based on large volumes of data.
While deep learning with neural networks can recognize complex speeches as well as images and videos. Data science techniques bring big data principles like distributed computing along with statistical modeling. Together, these expand a company’s capability to uncover insights and opportunities hidden deep within mountains of information.
Data Warehousing Supports Advanced Analytics
Modern data warehousing services spans both cloud and on-premise implementations using lots of open-source big data tools. This data foundation offers capabilities important for machine learning and data science:
- Scalably aggregates massive, growing data volumes which advanced analytics relies upon from across customer accounts, IoT sensors, clickstreams, and a mix of structured and unstructured content types.
- Applies extract, transform, and load processes into clean, consistent data formats like Parquet for reliability during exploratory work.
- Augments raw data with business metadata (data about the data) as well as technical context that clarifies meaning and relationships.
- Allows a single platform accommodating traditional BI concurrent with machine learning model training and data science experimentation.
- By robustly taming data proliferation and complexity, data warehousing and big data pipelines fulfill essential prerequisites to adopting ML and DS techniques.
Why a Modern Data Warehouse is Necessary
Legacy Warehouse Limitations
While traditional on-premise data warehouses support large enterprise reporting requirements reliably, machine learning and data science breakthroughs compel upgrades. Today’s volumes, variety, and demands stretch old systems.
Scalable Capacity
Machine learning algorithms require immense datasets of quality sample data for accurate training and predictive outputs. Petabyte scale big data capacity expands beyond former terabyte constraints through modern parallel processing. Azure Synapse, for example, enables unified querying data lakes with limitless storage directly.
Agile Data Pipelines
Getting fresh, consistent data feeds to data scientists across siloed operational systems is key too. Slow, complex ETL should become automated “ELT” pipelines leveraging cloud data services. Change Data Capture in Snowflake quickly reflects upstream app data changes for example. This satisfies the always-needing more data reality of ML and DS.
Integrated Analytics
Leveraging cloud analytics partners like AWS SageMaker for complementary machine learning building blocks avoids added integration work. One cohesive environment from data consolidation to training models to applying predictions streamlines the end-to-end advanced analytics workflow.
Thus, while traditional warehouse strengths persist, optimistic machine learning and AI initiatives compel modernizing data foundations. Cloud’s expandable storage and services directly address earlier limitations around scaling up to big data. This expands options beyond descriptive BI to predictive and prescriptive analytics.
Collaboration across Teams
Shared data catalogs and metadata management in modernized platforms break down communication gaps between groups owning sources and analytic modelers. Streamlining access and context sharing exposes more data for experimentation and crosses technical barriers between teams.
Automated Monitoring
Scalable machine learning training workloads require rigorous performance monitoring of data and model pipelines to maintain. Cloud analytics instrument such observability automatically across complex architectures rather than manual custom coding needed on-premise.
Compliance Features
As data analysis expands to guide significant business decisions in machine learning, accountability becomes crucial. Capabilities like data lineage tracking to document processing from raw sources into final reporting now become standard in cloud data platforms to fulfill compliance needs.
Hybrid and Multi-Cloud
Blending both on-premise and cloud resources can be prudent for balancing control, costs, and capability. Multi-cloud adoption similarly avoids over-dependence on any one provider. Modern data warehouses readily enable hybrid infrastructure through cloud-optimized connectors and architectures.
Considerations for Optimizing for Machine Learning and Data Science
Flexible Data Pipelines
Designing reusable data pipelines adds value. Where ETL output feeds BI reporting initially, soon ML pulls from those same pipeline datasets after proving value. Avoid rebuilt pipelines each time new consumers emerge. Maintain consistency from the start by centrally governing sources, business logic, and security.
Prevent Analytics Silos
Sourcing all models and experiments from centralized data platforms versus scattered smaller data marts accessible only locally is best too. It simplifies integration and oversight throughout the analytics lifecycle to have standardized data everyone draws from. Dedicate data platform teams to curate this resource across groups.
Adaptable Architectures
Pick adaptable solutions able to accommodate new data types, structures, and access
patterns when additional analytical techniques emerge. This keeps overall modernization costs down long term. The cloud intrinsically brings easier extensibility to mix and match tools. Containers for compatible components also prevent lock-in.
Democratized Self-Service
Expanding access to cloud data platforms beyond data engineers serving only data scientists allows more teams to leverage unified data. Enable self-service SQL querying, simple report building, and dashboarding via cloud services so business units can supplement centralized analytics staff.
Observable AI Operations
Cloud machine learning platforms provide visibility into the performance of models and data pipelines, monitoring for bias and drift. This allows optimization and showcases accountability which builds confidence expanding AI usage. Maintain the ability to explain system behaviors as black box AI faces adoption barriers.
Tiered Analytics Storage
Not all data needs real-time performance. Analyze usage patterns and intelligently structure storage/compute tiers optimized for where access speed matters most. Many analytical queries run fine on cheaper object stores versus pricey transactional systems.
Metadata-Driven Architectures
Design not just for current ad hoc analysis but expected uses based on a clear enterprise analytics strategy. Then instrument data and its structural business meaning intentionally through catalogs and embedded metadata so discoveries transfer easily between tools.