Data Warehouse Automation: Five Steps to Success

Many companies have recognized that data is the beating heart of their business.

It’s the fuel that can drive competitive advantage and help inform decision makers of the best direction forward.

However, in a world where data is constantly changing and expanding, the question becomes, “How can businesses fully harness their data?” The data warehouse has become the core repository for enterprise data, but as they continue to expand with more data, users and complexity, performance, costs and ROI can take a hit.

Chief Data Officers (CDO) are faced with the continual challenge of optimizing impact, with many turning to automation tools to revitalize their data warehouse infrastructure.

Specifically, Data Warehouse Automation (DWA) helps IT teams eliminate repetitive design, development, deployment and operational tasks within the data warehouse lifecycle.

With automated data warehousing, IT teams can fast-track new data integration, more effectively work with big data, and devote more time to the business intelligence initiatives that will yield the greatest impact for their organizations.

DWA software works by employing metadata, data warehousing methodologies, pattern detection to help developers autogenerate data warehouse designs and coding through the use of design tools and time-saving development wizards and templates.



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display(div-gpt-ad-1439400881943-0); }); It’s a process that can overcome much of the time-intensive, repetitive work often associated with hand-coding of DW software projects.

Instead, developers can focus on more strategic elements of data warehousing, and ensure they deliver a data warehouse capability that will meet the needs of the business as it encounters new opportunities and challenges.

But embracing automation is not just a matter of implementing new tools or technologies.

Success can depend on building a number of key steps, ideas and processes into the strategy: Step 1: Think Ahead Before touching the technology, start with a thorough evaluation of where improvement in data warehouse performance will deliver the most business benefit.

A survey conducted by TDWI underlines the point, having revealed that ‘realigning to business objectives’ was the main reason organizations cite when looking to modernize DW infrastructure.

With that in mind, collaboration between all the stakeholders must form part of the planning process.

Business and IT teams should look at the wider transformation objectives before any decisions are made about where automation should focus.

Step 2: Review what’s already in place A large number of organizations already have data warehousing in place, and it plays a key role in their business intelligence strategy.

What’s often lacking, however, is an up-to-date understanding of capacity and performance requirements that will be needed by the business in the future.

Given the exponential growth in data volumes, having this as a foundation point is key.

Without clarity, any automation strategy risks focusing too much on current needs and won’t effectively integrate with a growing or changing business now or in the future.

Step 3: Focus on efficiency Implementing DWA technologies and tools usually has to compete with other important technology priorities.

Choosing WHAT to automate, therefore, is key to a strategy that delivers efficiently against all the relevant objectives; be that budget, performance, timing, etc.

Many CDOs find that automation is particularly cost-effective for systematic processes, because it can eliminate the need for human involvement or accelerate existing methods.

Tasks such as hand-coding SQL, writing scripts or manually managing metadata are good examples of where automation can really improve efficiency.

Step 4: Aim for continual improvement In an ideal world CDOs should view their automation strategy as a long-term process of continual improvement.

Businesses evolve and recent events have taught everyone that unprecedented and unpredictable challenges can be just around the corner.

This means that a phased approach can be very beneficial, because it allows changes to be made along the way, mistakes to be rectified and ensures the IT team has the opportunity to always align its DWA work with the wider needs of the organization.

Step 5: Adopt the right mindset Automation experts will quite rightly argue that DWA is as much about mentality as technology.

Success depends on blending effective leadership, transparent processes and communication with the right tools and technologies to meet those key business goals.

Done well, DWA can deliver a transformational impact on business intelligence and decision-making.

It can become a constant asset to businesses that are always aiming to improve, helping them not just to understand what’s happening today, but what’s possible in the future.

About the Author Stan Geiger is the director of multi-platform tools, which includes WhereScape data automation, at Idera, Inc.

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