Why every company should understand and employ data warehouse automation

There are two kinds of companies, those that get analytics right, and those that fail. Failure may not come overnight, but in a competitive marketplace companies that lack the ability to make fact-based decisions are destined to under perform and eventually they fail.


What does it mean to get analytics right? It means everyone in the organization has the information they need, when they need it, to make good, timely decisions. While it sounds simple, this objective has proven difficult for many companies to achieve.


Part of the difficulty stems from the variation in data and reporting requirements from one company to the next. What works well for one company may not be ideal for another. Add to this the vast number of approaches to analytics, and the plethora of enabling technologies, and it’s no wonder why so many companies struggle to enable adequate analytics.


For most companies, the answer to this problem can actually be quite simple. It’s a data warehouse. Basically, a database where the important data from each software system in use by the company is integrated, cleansed and organized in way that yields a complete view of operations across the company and makes reporting a snap. The heart of the solution is a dimensionally modeled star schema database that presents data to users in a way that is intuitive, making it easy for anyone to create reports using tools like Power BI or Tableau.


And while the data warehouse is easy to conceptualize and understand, it can be hard to execute. Remember, everyone’s data and requirements are different, so every data warehouse must be custom designed. There is no shortcut.


Whether you’ve already started down this path or are just hearing about it for the first time, there are a few things you can do to increase your odds of success. The most important thing to know is that a data warehouse is a living breathing thing that requires constant development and maintenance. If you treat analytics like a critical business function rather than a project you will dramatically increase your chances of success.


Another important consideration is finding the right combination of skills. Many companies fail at data warehousing because it requires a diverse set of specialized skills including business analysis, data modeling, code development and report authoring. One proven approach is to leverage outside experts at first and attempt to build competency in house over time if desired.


Then there’s data warehouse automation, a little-known trick of the trade that can revolutionize the entire process. Writing ETL, the code that transforms the data, is the largest component of any data warehouse build, typically accounting for 70% of total effort. Data warehouse automation software eliminates the vast majority of that work, making it possible to discard waterfall projects and adopt agile methodology. It also applies best practices and enforces consistency which improves data quality and reduces maintenance. Companies that employ data warehouse automation routinely achieve a higher level of success, and do so in a fraction of the time it takes to develop by hand.


While data warehousing and data warehouse automation won’t solve every reporting need, they are a sure way to solve the majority of the daily reporting needs most companies face, and a proven way to get analytics right.


Have questions about enabling enterprise-class analytics in your organization? Give us a call at 1-833-BI-READY for a free consultation.

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