How grouping your existing BI use cases by data latency requirements can help clarify your BI Strategy
A lot of companies are burdened by legacy BI platforms and are not able to deliver the right data to their users at the right time to run their business today. They know that they need to modernize their BI platforms but do not know how to go about it. First let’s look at what should be the goals of a BI Modernization strategy.
Here are the goals from my view for any BI Modernization Strategy –
1. Speed of change — Probably the most important goal. The new platforms should be able to add new fields or add new data sources much faster than legacy platforms.
2. Combine data from multiple sources — This one was always difficult to achieve in legacy tools but is possible with modern tools.
3. Superior performance — Report performance and data load timings should be significantly improved.
4. Self Service — Enable business users and power users to build their own reports and dashboards without relying on IT and super users
5. Support different data latency requirements — Real Time, Operational and Analytical Reporting are the three ways to look at data latency requirements
6. Reduce overall cost — The BI software licensing models and the costs of compute and storage have come down enormously leading to a significant reduction in costs if implemented correctly compared to legacy BI
Grouping BI Use Models by Data Latency
In most companies BI would have evolved in a somewhat organic manner over many years or decades. Many different BI tools would have been used at different times either brought in by acquisitions or by the best tool available at that time. When companies try to replace their legacy tools, they often try to replace one tool with another tool. For example, their project would like “we should move from OBIEE or Cognos to Tableau or Power BI” and/or “let’s move our data warehouse to Snowflake”. They then end up customizing or building solutions into Tableau/Power BI which it was not meant to do, and the project runs into issues.
A better strategy would be to consider the use models of your current BI solution. One way to look at your current use model is by data latency requirements.
There are three ways to classify use models by data latency –
1. Real-Time Data
When the users want to see the data as it is “right now” in the operational system then that is a real-time requirement. This kind of data needs are common in the supply chain, finance, sales or call center area. The supply chain users would want to see what are the orders they need to ship right now, or in finance they would want to see what is the GL Balance at 12 AM at the end of the quarter, or in sales they want to see quota to orders at frequent intervals at the end of a quarter or in call center they would like to see call volume related data in real-time. None of the use cases can wait for the data refresh to the warehouse to happen. The best place to do this reporting is embedded analytics inside the ERP or CRM software but its often the case that the reporting is often slow or does not offer the capabilities of a BI tool. Hence these requirements fall into the BI arena. The primary users of real-time reporting are operational line level employees in the manufacturing floor, shipping or in finance operations who use the data to perform day to day tasks. The BI user interface can be simple but effective for these needs as user is just trying to get the data they need at any given time.
2. Operational Reporting
Operational reporting is needed when the data being analyzed can be a few hours or few days old. This kind of reporting is useful to analyze order or call volumes count over the last few days, how many of the marketing leads have been generated by various campaigns this month. The data needed for operational reporting are usually in multiple systems and the reporting solution should be able to join these data together. For example, the campaign data could be in Eloqua or Marketo, leads data might be in Salesforce and the order data might be in Oracle or SAP. The operational reporting solution should be able to bring the data from these three sources and tie the data together to get a full picture of the campaign effectiveness. In most companies, the bulk of BI requirements fall into this category which is essential for running the business. The primary users of operational reporting are managers who are analyzing data to make decisions on their operations. They usually make decisions like how many trucks do I need for my shipments today, or which campaigns should I run today based on the data and analysis they were able to obtain from their operational reporting.
3. Analytical Reporting
This kind of reporting is useful to analyze data trends over months, quarters or years. The data is usually stored for the long term and frequently data is also stored as snapshots to get the view of the data at a specific day of a month. Then the data is usually summarized to give a long-term view of the various metrics. Some common examples are revenue by industry or region over many years or how gross margin for a product has changed from last year vs. this year. In this type of reporting the frequency of data refreshes can be once a day, week or month and data is usually summarized and stored for trend reporting. The user base here are analysts who are answering questions based on trends in the data or executives and senior managers of various departments to do long range planning of their business based on the historical trends. The BI tool used here should be very user friendly and will need to enable self-service for the analysts.
Now if you look at your traditional BI environments, all these three use models will be included in all the BI tools. For example, OBIEE will be performing real-time, operational or analytical reporting but so is Business Objects or Cognos or other tools, just that they will be doing the same for different departments or different source applications in your company. When you are trying to modernize the BI architecture, you can look across all your current BI applications and group the reports or dashboards serving these three use cases. Once they are grouped into different use cases, you have a good understanding how much your company uses each of these use models. Then once you have this analysis in hand you can proceed on working out the architecture for these three use models.
I will cover possible architecture for each of these use models in the following articles. Please stay tuned and let me know your comments on whether classifying your BI landscape into these use models makes sense for your company.