Our ‘Secrets’ blog posts look at best practice for BI and other data project development. In a world where so many projects fail, or take a long time to achieve their original objectives, Insource sets out to help yours avoid any pitfalls and ensure smooth, swift success.
In our last blog, we looked at how Data Preparation brings big benefits to data projects, by managing, cleaning and verifying data for multiple downstream uses. We’ve seen that through the use of automation technologies we can simplify and speed up the process of development of these complex processes.
However, in the wrong hands and with the wrong approach, what automation does bring with it is new ways to achieve the same issues faced by teams undertaking ‘ground up’ development, except that automation will get them there faster.
Use a structured Data Management development method that embraces automation technologies and incorporates a proven architecture
Automated Data Preparation has the capability to build a data refinery that’s consistent and reliable. Data Warehouse automation achieves the same for data warehousing. As a result, your BI and data project teams can feel confident that with the right approach their new development will work better, they will get there faster and the result will be more reliable. You just need to make sure the approach is clearly defined in the first place. Learn from experience, and from others. Draw on best practice to eliminate guesswork. What’s needed is an overriding method and architecture that development teams can follow to guide them to success. An approach and architecture that’s designed specifically to get the best out of automation techniques.
At Insource, we specialise in sophisticated development platforms, methods and architectures that bring together these technologies and approaches in one place, for your benefit. Our dedicated development platform, 'Data Academy', is a data management development product designed specifically to help you achieve your objectives faster and at lower cost. Our design architecture and development method Data Space Frame brings together hindsight, best practice, standards and approaches to ensure your development team stay on the straight and narrow and succeed against the statistical odds.