What is Data Warehouse Automation (and why should your CXO care)?

In the last decade, software developers addressed the barriers to comprehensive data programs by developing robust data warehouse automation tools that automated code generation for recurring tasks in data projects. Although the solution had obvious benefits to the developers hacking out code in the basement, the value proposition was unclear for business executives. "You want me to spend 100k on what?"
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Assumption: To remain competitive organizations must leverage data to augment their product or services offering and/or use analytics to reduce costs or innovate value-added business processes.

Why do I need a governed data warehouse? Can't I stick with my self-service solution?
No, self-service business intelligence (BI) will not replace a data warehouse. Your organization needs:

• A single version of the truth -- Self-service solutions frequently require users to perform their own 'data-munging' (extraction and cleansing) which translates into inconsistencies between reports and departments. In comparison, pre-defined metrics in a data warehouse enforce consistency across the enterprise.
• The ability to keep history -- Operational and transactional systems overwrite and lose critical data history.
• Improved end-user productivity -- In creating structured datastores, organizations democratize data access and facilitate data-driven innovation (with cleansed & consistent data).

We still need self-service tools. Data-driven innovation frequently stems from exploration by individuals or small teams before transitioning into enterprise-wide solutions. The cutting edge of data innovation lies at the intersection of self-service flexibility and agile datastore implementations serviced by an automation tool.

--Update 4/10/2016 --
Onyx Reporting uses and recommends data warehouse automation tools developed by TimeXtender; because while the application does auto-generate code, all parts of the business intelligence development process (extract, transform and load) are accessible and customizable using traditional tools in the Microsoft BI stack.

Challenges with Current Data Project Lifecycles

The reality of implementing traditional BI is that it can be daunting, expensive, and risky.

• High employee costs -- maintaining an in-house business intelligence team is expensive. Before outsourcing, many organizations push the task over to the already overworked database administrators or IT department who's core competency is not modeling data solutions.
• Low Consistency & continuity risks -- outsourcing project development is a common solution, but every developer or developer team has their own strengths, formatting, and styling preferences making risky reliance on key individuals or teams commonplace.
• Long development phases = low return on investment + lack of agility -- Hand-coding data extraction and transformation can be very tedious, time-consuming and inflexible making agile responses to changing business requirements difficult. 'Minor' changes can require sweeping updates to hundreds or thousands of lines of code.

​What does Data Warehouse Automation include?

• Automated code generation.
• Automated deployment, maintenance and update of code.
• Automated execution of data extraction.
• Automated monitoring and reporting of execution.

Who benefits from Data Warehouse Automation (DWA)?

​Business Owners: Faster turnaround on data projects = Faster return on investment and increased flexibility to respond to changing business requirements and reduced costs.

DWA consistently cuts development time and costs in excess of 50%. The 3-month prototype moves into circulation after a few weeks allowing lead developers and modelers to transition to other high-value initiatives while the remaining project team finalizes the implementation.

When business users inevitably uncover an unexpected data reality or roadblock, developers -- empowered by auto-generated and parameterized code -- can flexibly address the challenge while avoiding tedious rewrites.

Developers: Automated code generation = Standardized & consistent code, less tedium + increased flexibility.

Despite multiple developers touching the project, the auto-generated code looks consistent and encapsulates both the collective expertise of the implementation team as well as decades of 'best practices' integrated into the software by the developers of the DWA tool.

Database Administrators: Generated build + automated deployment and execution = Fewer opportunities for catastrophic human error.

How much does it cost?
Rephrase the question. How many innovative data projects could you move into production in a year if projects completed 50% faster? Alternatively, how much money are you leaving on the table, or letting walk away from you by NOT having improved analytics?

*DWA has software, training, and hardware requirements which vary from organization to organization. We're here to scope your next project.

Do I have to retool my organization and my development team?
No. DWA tools like those developed by TimeXtender use technologies your organization probably already owns (Microsoft SQL Server stack), so all the development is done on a familiar platform in a common language.  Developers and data analysts can complete product training in a week and move forward with leveraging their understanding of dimensional modeling techniques to create datastores.  The only remaining question is how users will interface with the datastore. PowerBI, Qlik, Targit and Jet Reports are all supported options!

Real world example
In the days following BREXIT, a produce importer and distributor approached Onyx Reporting with an urgent analytics requirement. To support price negotiations with resellers, the business needed an automated way of tracking and reporting moving exchange rates and their impact on supply costs. Additionally, the business wanted to mitigate supply chain risk by monitoring and distributing the countries of origin for their produce.
A proof of concept was available in days, the project was production-ready within weeks. The project team included one developer and one business user.

Are you ready to find out what your organization can do with data warehouse automation?

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