Problems with current BI ecosystem

What is it that first comes to your mind when you hear decision support system and business intelligence. To an enterprise knowledge worker, these are synonymous with dashboards and reports. With increasing hype and commoditization of data processing, pivoting and visualization tools;  BI and insights applications have penetrated well into many enterprises. Among these applications, there are three primary market segments - 1) generic BI platforms such as Microsoft BI platform, Microstrategy etc., 2) Industry specific BI platforms such as Salesforce Wave Analytics etc. and 3) Visual Insights Platforms such as Tableau, QlikView etc…

A BI ecosystem in an enterprise typically works in a bi-modal approach; where an enterprise BI team hosts BI platforms while businesses use these platforms to build reports and dashboards for their analysis. For most parts this is an excellent modus operandi except for the fact that it overlooks an important BI need. In any organization where business units standardize their structure, processes and activities; this bi-modal approach to BI becomes inefficient, because same business unit services end up building silo'ed solutions for common BI needs owing to similar processes and activities. A good example of this case scenario is IT service management. In IT Service Management, IT performance is tracked via process indicators for processes such as incident management, change management etc which are delivered through enterprise ITSM tools. This common business structure leads to common metrics and underlying data. To meet these common BI needs, organizations build management dashboards that are used by executives for performance tracking while service operations teams build their own reports for ad-hoc analysis.

This bi-modal BI approach for common business structure leads to following issues:

1. Same metrics when delivered by different business services end up delivering different results due to difference of understanding, biases, lack of codification and overlooking of data quality issues.

2. Duplicate development cost and delay

3. Unequal performance comparison

In fact, with every organizational restructuring, these BI needs re-emerge leading to perpetual BI development of similar order. Additionally standard BI ecosystem still suffers with three major problems:

1. High development cost and turnaround time on canned BI reporting

2. Data understanding and reporting skills required for self-service reporting

3. BI Reporting & Advanced Analytics are more or less still two separate silos

Let’s understand these issues in more detail.

1. High development cost and turnaround time on canned BI reporting

Let’s first understand how a BI report [dashboard] is developed. 

• Identify business process metrics to be monitored
• Identify data sources
• Identify data elements
• Build datasets
• Build data models (optional)
• Develop report template
• Bind report to data elements from datasets/models  

This is a standard approach to build a report or dashboard with few possible variations. This process may take from weeks to a few quarters based on data quality and report complexity. Now if a business needs to have multiple reports or dashboards – this whole process is repeated times the number of reports required, notwithstanding same metrics appearing in multiple reports. Not only is there a repetition of the process but also the datasets get cast in a particular fashion owing to the reporting style. The next time there is change in data schema, the risk looms large on potential report breakage. 

 

2. Data understanding and reporting skills required for self-service reporting

While Self-Service reporting addresses the cost and TTM issues, it brings in fresh set of requirements to be fulfilled. Self-service reporting requires business people to be in constant know of data models, data elements and reporting tools. Against canned reporting - Self-service reporting demands a discipline by users to share the results with all concerned stakeholders, so that everybody is aware of results. However in practice it’s hardly achievable; not to mention different business units might want to see results in their own way. When this discipline is not maintained and every business unit builds their own self-service report – a problem of “multiple versions of truth” arrives due to the fact that different users have their own interpretations of how a metric is calculated and boundary conditions are to be observed.

 

3. BI Reporting & Advanced Analytics are more or less still two separate silos

While Advanced Analytics has the ability to deal with abstract information, BI reporting requires a structured data approach. Usually Advanced analytics results are shown using BI reporting but if a demand arises to return to analytics to calculate the results on the fly, it becomes a challenge next to impossible. Let’s understand it by an example. 

Suppose we want to understand how a service is performing on “Time to Resolve (TTR)” with respect to the incidents that it is responsible for. The standard way would be to monitor average which is simple to calculate and report. However average is not an accurate indicator for a large range of data values. The right way is to understand the distribution of TTR and determine what percentage of incidents is causing how much TTR. The simplest way to do this is through percentiles/quartiles. However most standard BI reporting solutions are not capable enough to provide percentiles. They might deliver for the overall volume but they can’t really perform, once slicing and dicing comes into the picture. BI reporting’s inability to work in sync with advanced analytics solutions puts them in silos.

 

These problems are more of a legacy design flaw rather than technical roadblocks. BI vendors mostly focus on reporting and dashboards than on advanced analytics because advanced analytics is very domain specific and use case centric. The existing BI solutions try to expose data to end users because all domain specific metrics are not pertinent and tend to change for different industries.

At first these issues may seem like universal problems with no known solution, but the fact is; these are the problems associated with the current approach to BI. These issues are resolved when an alternative approach to BI is considered. In the next blog I'll explain how I fixed these issues and delivered better results that scores well on every drawback that current BI ecosystem has.