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. With that in mind, we created this data warehouse requirements gathering template to help you make sense of the process and choose the right business intelligence software for your needs.
What Is a Data Warehouse?
A central tenet of business intelligence, the definition of a data warehouse is a technology that centralizes structured data from other sources so it can be put through other BI processes like analytics, data mining, online analytical processing (OLAP), etc. It can draw data from relational databases, transactional systems and other software like CRM.
Data Warehouse vs. Database
One area of confusion for many users is the difference between a data warehouse and a database. Databases and data warehouses are both systems for storing relational data, but they serve different functions.
Data warehouses store large sets of historical data to assist users in completing complex queries via OLAP. Databases store current transactions and let users access specific data points for business process transactions called online transaction processing (OLTP).
Basically, databases are up-to-the-minute repositories for data typically from a single source. Data warehouses also store a range of data aggregated from databases. They are heavily intertwined but perform different tasks for business intelligence processes.
Data Warehouse Business Requirements
So what should you expect from a data warehouse? It’s up to you to create a system that satisfies the need for uniform data integration while remaining responsive to your analysis practices, but there are some general requirements that can serve as a great jumping-off point. 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.
All BI tools offer data warehousing features along with other capabilities like data visualization. So we’ve compiled this BI data warehouse requirements questionnaire and template to help you on your way!
1. Platform Functions
These features establish a baseline for the system to operate around. Interactivity refers to the communication process between human users and the software and how easy the system is to use. Customizations and white labeling allow users to remake the software to their preferences and needs. This has the double benefit of a seamless experience with other software systems you might use and the assurance that your employees will actually use it.
2. Data Visualization
Once data is organized in a data warehouse, it is ready to be visualized. This involves the system discovering trends and patterns in data sets and generating graphs, charts, scattergrams and other visual depictions.
Visualization makes complex statistical relations easy to interpret for users. Did you know that when we sit down to read a website, we only read an average of 28 percent of the words on the page? We skim, make assumptions and extrapolate based on the words we do read to glean information. That’s one reason visual depictions are so much more effective at delivering information to our brains. Data visualization helps bridge that gap and offer information that sticks.
Storyboarding functions like a flowchart — it maps out the flow of data and insights in a linear narrative to make it easily digestible. The drag and drop feature lets users customize their dashboard at the click of a button and create personalized templates to meet their specific needs.
Charts and Graphs
Drag and Drop Creation
Freehand SQL Command
The analytics portion of BI offers insights into your business processes by evaluating trends in data and applying predictions to them. Benchmarking compares business practices and performance to industry metrics in order to create action plans to improve your business.
Predictive analytics offers suggestions based on forecasts for future performance or data events. Social media analytics is pretty simply just what it sounds like — it tracks engagement, followers, traffic and other social media metrics to generate reports on your organization’s social presence. Web analytics is similar but tracks metrics for your website.
Geolocation analysis measures the location of customers, traffic or other location-based metrics. These can be used to glean an understanding of customer demographics, improve services, optimize sales territories and more. Ad-hoc analysis is a report generated on a specific query for a single question or KPI; it can be custom-made or generated from a template.
Data mining is a subcategory of BI like data warehousing. It is the process of collecting the data from the database or warehouse in order to analyze it. In-memory analytics performs complex queries that would otherwise be done on physical disks within the RAM of the machine, increasing the speed of analysis.
Machine learning automates the model building process. It is a form of AI that allows systems to learn from previous data in order to identify patterns and reach conclusions without human interference.
Social Media Analytics
Online analytical processing (or OLAP) is a process that performs multi-dimensional analysis on large, layered datasets. It drills down and explores data to offer users both detailed information on their daily operations and overviews of business trends. With this data, users can extrapolate predictions by changing variables and uncovering relations between them within the data.
Time-Series Auto Generation
5. Document Management
Reporting is another key tenet of BI, and what happens to those reports after they’re generated all takes place in document management. Users can export reports and visualizations in a range of document formats to send to team members, investors and more with ease. Versioning and version control ensure that individual instances of a software solution (for example, the iOS on your iPhone when you bought it versus the most recent update) employ different versions of the product. This lets software programmers track changes and revert back to previous versions if a serious bug occurs.
Export to Microsoft Excel
Export to Microsoft Workbook
Export to PDF
Export to HTML
6. Decision Services
This module focuses on how users take the insights they derive from data and turn it into action. Financial management features offer forecasting and budgeting to help you achieve financial success. Regulatory compliance and threat/fraud detection capabilities ensure data security, alert you to suspicious activity and protect you during audits.
While some BI tools restrict their users to proprietary architecture, more and more are offering a range of integrations with other kinds of software systems and datasources. For example, service-centered organizations need to be able to draw data directly from their CRM to generate reports and visualizations on that information.
Extract, transform, load (ETL) is also a crucial integration. ETL combines three database functions into a single tool in order to transfer data from one database to another.
Big data integration is also important — it enables large data set incorporation from sources like Hadoop, Hive, etc.
MS Office Applications
Big Data Connectors
How to Choose the Right Data Warehousing System
Now you know the general business requirements for data warehouses, but how does one go about choosing a system that meets their needs?
First, it’s important to 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.
Next, you should 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.
Now think about what your goals are for this 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.
This data warehouse business requirements document should prepare you to choose the best solution for your unique needs. By filling out this data warehouse requirements document, you can identify your key requirements. Next, compare BI vendors based on their delivery of the features you identified as crucial in order to create a shortlist of your top platforms. Finally, compare prices with this pricing guide and request demos of your shortlist products to take them for a test drive and get a feel for their usability. This will prepare you to submit an RFP and select your product!
Do you still have questions? Which data warehouse requirements and features are key for your organization? Let us know in the comments!