Businesses have long sought to optimize core business processes to ensure maximum business performance. IT departments have supported the cause by building systems to automate many of the mechanical aspects of these processes ensuring repeatable, efficient execution of operations. These same IT departments, however, have continued to use traditional labor-intensive methods of building many of the key components needed to support the rapidly growing and changing face of the business.
A prime example comes in creating and managing data warehouses. It’s no secret that organizations that leverage information to make better business decisions faster than their competition have a significant advantage in the market. But, in order to make those decisions the right information needs to be available at the right time. That means that a data warehouse or other decision-enabling supply of data must be available and managed to keep pace with the speed of business.
Traditional approaches to building and managing analytical foundations like data warehouses fall short. The activities for gathering and analyzing requirements, creating and managing the representative models, integrating data, enabling access through a BI layer, testing and releasing to production can take upwards of 12 – 18 months. When a new and compelling business event occurs, a year and half can seem like an eternity. As that timeline ticks by, the rules have changed and the game is over. Traditional approaches just don’t cut it.
What ends up happening, in many cases, is that the business users who need to take action will find alternative ways to get the information through other means. Often, this evolves into “shadow IT” and introduces new sources of ungoverned data of suspect quality being loosely managed in unsecure environments. It’s enough to give you nightmares. The alternative is just as frightening. Business-users end up making decisions based data on hand rather than on what’s actually needed. In either case, there is significant risk (operational, reputational, and ultimately financial) introduced into the decision-making process which can prove very costly.
Why is it that we continue to treat data differently than we do other high-value enterprise assets? We have automated, well-managed processes to handle accounts receivables. We have ERP systems to handle and automate supply chain processes. We have HR systems to effectively manage our human assets. Yet, we leave our high-value information assets to be managed by traditional and inefficient means – that’s just messed up!
There was a time when customer billing was done by hand, but you wouldn’t dream of performing that process manually today. Billing systems were designed to increase the accuracy, efficiency and throughput of the billing process. Perhaps if we look at building and managing a data warehouse as we do any other core business process, then breaking out of the mold of tradition wouldn’t be such a difficult concept to get our heads around.
This topic is worth exploring in much greater detail. Over the next few blog entries we’ll look at the process of building a data warehouse, analyze the mechanical aspects of the process, identify areas for optimization, and discuss how to automate many of the steps that can be treated as repeatable operations. In doing so, we’ll identify methods to optimize the process and shorten the cycle time from information request to business decision without sacrificing quality or increasing risk. Stay tuned!