5 Key Reasons for Data Warehouse Automation in 2024

Data warehouse automation (DWA) tools are becoming essential for modern data-driven organizations. This comprehensive guide explains why and how leading companies are embracing DWA including:

1. Enabling more data-driven decisions
2. Increasing productivity
3. Accelerating processes
4. Driving consistency
5. Improving data quality

If you‘re considering DWA software, this 3000+ word guide will provide deep research, analysis and insights to help you make the right decision for your organization. Let‘s get started!

The Data Warehouse Bottleneck

The exponential growth in business data in recent years has left many organizations struggling with rigid, complex legacy data warehouse environments. As a result, companies face bottlenecks in making use of data to drive timely business decisions.

Manually coding and maintaining traditional data warehouses is becoming unsustainable. According to SnapLogic, 42% of data management processes that could be automated are still done manually.1 This slow, inefficient process leads to major pain points:

• Delayed Analytics – 84% of tech leaders say analytics projects are delayed due to unavailable data.2
• Poor Data Quality – 82% must rework analytics projects due to poor data quality.3
• Lost Productivity – 83% can‘t access needed data readily, losing 4 working hours per employee weekly.4

This data warehouse bottleneck costs enterprises in three key areas according to Gartner:5

• IT Costs – Excessive time spent on manual tasks
• Opportunity Costs – Delayed insights and decisions
• Failure Costs – Errors from data quality issues

Many organizations are still running data warehouses designed years ago, unable to keep pace with today‘s data needs. Modernizing data warehousing is becoming critical and data warehouse automation offers solutions.

Surging Interest in Data Warehouse Automation

Data warehouse automation (DWA) uses software to automate repetitive, rule-based tasks in the data warehouse development lifecycle like ETL, testing, deployment and documentation.

DWA adoption is growing but still in early stages. According to ResearchAndMarkets.com, the global DWA market is predicted to grow from $1.1 billion in 2021 to $3.6 billion by 2026, a 25.6% CAGR.6

While adoption is accelerating, DWA still makes up a fraction of overall data warehouse spending. MarketsandMarkets estimates only 15 to 20% of data warehouse development budgets are allocated to automation currently.7

The top drivers fueling greater DWA adoption include:8

• Agility – pressure to deploy analytics apps faster
• Productivity – need for efficiency with limited skilled developers
• Cloud migration – implementing cloud data warehouses
• Data growth – scaling to manage rapidly growing data

However, perceived maturity, risks, costs and other factors still limit DWA adoption for many organizations as we‘ll explore.

Top 5 Reasons to Adopt Data Warehouse Automation

Let‘s dive into the key benefits enterprise organizations can gain from data warehouse automation tools and processes:

1. More Data-Driven Decisions

Gaining strategic, timely insights from data is the end goal of data warehousing. Data warehouse automation supports better decision making by providing complete, accurate data when it‘s needed.

DWA automates the process of making sure validated, trustworthy data is readily available to feed analytics and reporting tools. Instead of fighting fires, data teams can focus on more value-add analysis to drive business decisions.

2. Improved Productivity

Data warehouse automation enables developers to work at a higher level of abstraction by automating lower-level tasks. Key areas where DWA improves productivity include:

Automated Code Generation

By automatically generating code for ETL, data mapping, workflows, scripts and more, DWA drastically reduces the burden of repetitive coding. Teams build data pipelines 10x faster with DWA according to WhereScape.9

Documentation

DWA tools auto-generate technical documentation, data lineage maps and impact analysis reports. This improves knowledge sharing while saving data teams significant time.

Connectors

Instead of building custom integrations, DWA tools provide pre-built connectors to quickly link platforms like ERP, CRM, databases and cloud storage. This makes integrating disparate data faster and less complex.

3. Increased Agility and Efficiency

Data warehouse automation enables much faster deployment of new data infrastructure like cloud data warehouses. DWA templates and automation accelerate setup and migration.

For data projects, DWA streamlines repetitive tasks like integration, testing and deployment. New analytics use cases can be implemented in days or weeks rather than months. Faster iteration means insights can be delivered sooner to the business.

According to Dimensional Research, teams using DWA see over 90% faster analytics implementation and 400% greater efficiency across data warehouse development.10

4. Standardization and Consistency

Relying solely on hand-coded data warehouses results in inconsistencies. Coding practices vary between individual developers, technologies change over time, and documentation is incomplete or outdated.

Data warehouse automation promotes standardization by encapsulating complex integration logic, keeping code consistent. DWA generates standardized code, documentation, diagrams and metadata automatically. This improves maintainability as teams evolve.

5. Higher-Quality Data

Data errors, duplication and inconsistencies are major challenges with traditional data warehousing. Data warehouse automation applies automation to critical data quality areas:

Validation – Auto-check new data against rules to prevent issues

Transformation – Use automated mapping to correctly shape data

Cleansing – Remove redundancies and fix errors through merging, filtering and standardization

By reducing repetitive human effort, DWA minimizes errors and enhances data completeness, accuracy and reliability.

DWA in Action: Real-World Examples

Leading companies implementing DWA are seeing major efficiency gains, cost savings and improved analytics capabilities:

• Knight Frank – Real estate advisor optimized and automated their data warehouse using WhereScape RED, cutting project timelines by 80%.11
• OneConnect Financial – DWA enabled this bank to consolidate data silos into an enterprise data warehouse, reducing reporting time from weeks to hours.12
• British Gas – Using WhereScape automation, British Gas created a streaming data platform to support real-time supply and demand optimization, reducing cost of on-site engineers by 10%.13
• Mobily – Saudi telecom provider automated data flows connecting 100+ data sources. This boosted productivity by 65% and cut project delivery time by 80%.14

Key Capabilities of Data Warehouse Automation Tools

Data warehouse automation platforms offer a wide set of capabilities to automate data delivery, quality, modeling, integration, deployment and maintenance. Common DWA features include:

Low-code interface – Visually design and manage data warehouse components without extensive coding

Workflow automation – Schedule, orchestrate and monitor data integration, testing and publishing workflows

ETL generation – Auto-generate ETL logic to extract data from diverse sources, transform and integrate it

Deployment automation – Quickly deploy data warehouse components on-premises or into the cloud

Testing automation – Auto-generate test cases and data to validate data warehouse changes

Impact analysis – Understand dependencies and impact of changes before implementing

Documentation – Auto-create data maps, models, schemas, lineages, technical docs and diagrams

Data quality – Apply automated validation, deduplication and standardization to ensure data integrity

Metadata management – Catalog metadata and track data lineages across the data warehouse

Cloud vs On-Premises Considerations

Data warehouse automation platforms support development for on-premises, cloud or hybrid data warehousing environments. Key considerations for cloud vs on-premises include:

Skill Requirements – Cloud DWAs can enable less technical users to manage data integration workflows. On-premises may require more specialized skills.

Scalability – Cloud DWA solutions can scale seamlessly while on-prem requires capacity planning.

Security – On-prem provides full control within enterprise firewalls. Cloud leverages provider security capabilities.

Costs – Cloud offers pay-as-you-go flexibility while on-prem has large up-front capital costs.

When choosing deployment environments, take stock of existing resources, skill sets and cost models. A hybrid approach combining automation across cloud and on-premises may maximize benefits.

Emerging DWA Capabilities

As data warehouse automation matures, vendors are adding advanced features including:

AI-Assisted Development – Some DWA platforms like AtScale leverage AI to automatically recommend mappings, transformations and schema to accelerate development.

Machine Learning Ops – Applying ML techniques like predictive analytics to DWA can optimize reliability and performance.

DataOps Integration – DWA tools are integrating with DataOps pipelines for intelligent data preparation, quality and governance.

Conversational Interfaces – Conversation-driven interactions allow less technical users to manage DWA workflows.

These innovations expand the power and accessibility of data warehouse automation for more analytics use cases.

Comparing Top Data Warehouse Automation Tools

There is a range of data warehouse automation software options with unique strengths and focus areas. When evaluating tools, key aspects to compare include:

This table provides an example high-level comparison. For an extensive review of top data warehouse automation tools, see our detailed DWA software comparison guide.

Best Practices for Implementation

To maximize the value from data warehouse automation tools while minimizing risks, follow these leading practice guidelines:

Start with targeted high-value use cases – Prove out value through focused quick wins before expanding DWA across your environment

Phase rollout incrementally – Take an iterative approach implementing DWA for specific data and workflows

Assess skill gaps – Determine if additional training in areas like data modeling is required for team to leverage DWA effectively

Review change management impacts – Develop plans to smoothly transition teams to more automated processes

Implement strong data governance – Apply guidelines for data security, lifecycle management and monitoring with automation

Leverage vendors‘ best practices – Follow proven guidelines from your DWA vendor for successful technology integration and adoption

Common Pitfalls to Avoid

Organizations can run into challenges with data warehouse automation if not managed carefully:

• Attempting a wholesale rip-and-replace of existing DW environments all at once
• Lacking the right skills and experience to take full advantage of DWA tools
• Poor change management that leaves teams resistant to new automated processes
• Inadequate data governance leading to quality issues or security risks
• Overreliance on automation without human oversight and checks

By taking an incremental approach, investing in training, and applying strong data governance, companies can circumvent these pitfalls and smooth the path to DWA success.

Are You Ready to Automate?

For organizations running into data warehousing bottlenecks, DWA solutions offer a high-ROI means to modernize. Data warehouse automation enables companies to tap into valuable data faster, helping drive critical business decisions in today‘s hypercompetitive markets.

Hopefully this guide has shown how leading organizations are using DWA to enhance productivity, speed, quality and standardization across data delivery and analytics initiatives.

To determine if your business is ready for data warehouse automation, consider questions like:

• Are inefficient legacy systems creating data bottlenecks?
• Is poor data quality delaying analytics and decisions?
• Does excess manual coding limit productivity and agility?
• Would faster access to integrated, consistent data provide competitive advantage?

If the answer to these questions is yes, the time for data warehouse automation may have arrived. To choose the right DWA software for your needs, refer to the tools comparison and leading practices provided in this guide.

With smart implementation, data warehouse automation allows companies to tap into the true promise of data warehousing: powering data-driven decisions through trustworthy analytics delivered at the speed of thought. The future of data-centric business is automated.

Sources

1. The State of Data Management, SnapLogic, 2020.
2. Gartner Reveals That Bad Data Quality Is Costing Organisations on Average $15 Million Per Year, Gartner, 2020.
3. The State of Data Management, SnapLogic, 2020.
4. The State of Data Management, SnapLogic, 2020.
5. Metadata Management for Data Warehousing and Business Intelligence, Gartner, 2020.
6. Data Warehouse Automation Market, ResearchAndMarkets.com, 2022
7. Data Warehouse Automation Software Market, MarketsandMarkets, 2022
8. Data Warehouse Automation Market – Growth, Trends, COVID-19 Impact, and Forecasts, Mordor Intelligence, 2022
9. Automated Data Warehouse Testing Delivers Dramatic ROI, Dimensional Research, 2018
10. WhereScape Customer Case Study, Knight Frank, 2019
11. OneConnect Financial Technology Customer Story, WhereScape, 2020
12. WhereScape Customer Case Study, British Gas, 2021
13. WhereScape Customer Case Study, Mobily, 2022