Data is more than merely a byproduct for businesses these days; it’s a currency with real value to companies, provided they can capitalize on the insights contained within. One of the biggest foundational challenges is simply collecting and storing droves of data in a way that makes it quickly and easily usable for users. In other words, effective data warehousing is a cornerstone for any sustainable business intelligence (BI) strategy.
Here are three enterprise data warehousing pitfalls to avoid when you’re designing your BI system from the ground up.
Operating Without an Overall Data Strategy
You’re not just building a data warehouse for the present. Therefore, it’s a pitfall to design your data warehouse and subsequent business intelligence strategy based on your company’s current needs alone. One contributor for Computer Weekly calls a “three- to five-year information management roadmap of the organization” ideal. We’re living in an era in which rapid business growth is not just possible but probable thanks to innovations in technology. It’s safe to assume you’ll need to scale up your data practices over time—so it’s a mistake to the data warehouse for now rather than considering your expanding needs in the future.
In general, data warehousing without an overarching data strategy tends to yield worse results because efforts are often disjointed. A piecemeal strategy only serves to hinder communication between departments and waste resources. Taking a long-term look at your data strategy now and implementing a solid foundation will help your warehousing efforts stay relevant over time so you can continue to extract value from your data.
Failing to Collect Sufficient Data
Put it this way: Your organization can only work with the data it captures and saves. This all starts with your data warehousing practices. Although it can seem daunting to keep up with the sheer amount of information the modern business produces—especially if your company is a large one—it’s better to capture sufficient data and sift through it from there than it is to miss out on collecting valuable data in the first place. However, the fact that you’ve already defined your data strategy ahead of time will help you determine what’s worth collecting.
Make sure your data warehouse is equipped to collect all relevant data. For example, a retailer will need to collect data pertaining to customer behavior, revenue, sales channels, inventory, internal operations, returns, order fulfillment and much more.
Neglecting to Establish SMART Key Performance Indicators
The acronym SMART, while not exclusive to the data world, can serve as a handy roadmap for choosing the key performance indicators (KPIs) you ultimately measure to gauge company performance. According to Information Week, KPIs should be:
- Specific
- Measurable
- Attainable
- Relevant
- Timely
Although it’s important to establish a data warehouse to collect vast amounts of data, “it is very possible to experience data deluge and lose valuable time and money trying to measure everything.” Establishing SMART KPIs ensures companies use the information collected within their data warehouses to measure relevant aspects of performance.
Which KPIs are truly indicative of your company’s performance in various areas? For example, a telecom company will almost certainly want to track customer churn carefully over time, as well as the cost to acquire a new customer. Manufacturers will likely monitor downtime as it compares to uptime, as well as KPIs like manufacturing cycle time. While the specifics will vary based on your industry, solid data warehouse reporting complete with SMART KPIs is a must to measure the impact of your strategy.
These pitfalls will only put a damper on the effectiveness of your enterprise data warehousing, which in turn serves as the foundation for all subsequent data-related efforts. And without a strong base, your business intelligence strategy will only be as stable as a house of cards.
