Is your business information coherent enough for advanced analysis, or is it time to get serious about aggregation? Data warehouses have massive potential to imbue your reporting and scrutiny tasks with increased accuracy, but there’s more than one way to implement a repository. It’s up to you to create a system that satisfies the need for uniform data integration while remaining responsive to your analysis practices.
Data warehouse requirements gathering is the first step to implementing mission-appropriate warehousing practices. Defining your needs clearly from the start will ensure that the software tools and methods you eventually adopt are actually suited to the task, so bear the following considerations in mind:
Categorize Your Needs
Most data warehousing users have diverse goals. For instance, you may seek to reveal specific information about the average length of help desk calls, but you could also want to plot this data against the type of problems each call concerns. Both dimensions can be categorized as business requirements. Your graph formatting preference, on the other hand, is really a technical requirement.
Differentiate between the business data you want to track and the technical requirements that impact how your tracking tools operate, such as publishing directives and reporting schedules. While both kinds of requirements are likely to change, making the distinction now will enable you to implement a cleaner system that lets you modify low-level database processes and high-level analysis workflows independently.
Assess Where Your Data Comes From
What kind of processes create the data you want to track, and how is the information they generate formatted? The answer to this question could determine which methodologies satisfy your needs.
For instance, databases that employ online analytical processing, or OLAP, are great at making sense of multidimensional datasets, such as sales, marketing and business process information. On the downside, certain OLAP implementations may have a good deal of latency. If your results trickle in directly from point-of-sale terminals all throughout the day, on-line transaction processing, or OLTP, may be a superior choice. Alternatively, you might implement a hybrid solution that leverages both techniques and aggregates data from multiple independent data marts.
Don’t worry if you don’t know enough about your data in advance to decide what strategies to use. At this early stage of data warehouse requirements gathering, it’s sufficient to get a good feel for the capabilities you might need and leave yourself with options.
Assess What You’ll Do with Your Data
Data warehouses revolve around databases, and databases depend on queries to function. This holds true whether you’re comparing data streams from individual sources or grouping large volumes of information generated by data marts. The operations, or transactions, that you perform involve low-level queries that seek, retrieve and modify target values.
As with learning where your data comes from, defining your process goals impacts which data oversight and maintenance techniques are the most viable. The frequency and nature of the transactions you undertake may also affect the performance of other data warehousing functions, such as automatically recording information. Similarly, some data storage tools aren’t good at handling concurrent operations by multiple users, which could limit analytics capabilities for large organizations. Although hybrid techniques and customized implementations can usually solve most problems, it all begins with you defining your operational goals.
Implementing an Effective Data Warehousing Methodology
Could a data warehouse change the way your organization operates? If you make time for planning now, it’s highly likely that you’ll reap the benefits later. You may even learn about solutions and techniques that hadn’t occurred to you before.
Discover more about what your organization needs to make the most of warehousing; build a data warehouse requirements gathering template to start defining your goals and capitalizing on smarter data analytics.