In my previous blog I highlighted problems that exist because of current approach to Business Intelligence (BI). Read Problems with current BI ecosystem.
In this article I will talk about the approach I took on working around those problem and deliver a better user experience. I'll start with the design thought that was adopted in search of a better BI solution. Like any business scenario, solution design always starts with defining a problem or opportunity statement. Because BI is a universal need, this search of problem statement didn't need any soul searching. This omnipresent problem statement from BI professional perspective is:
How to support better business decision making using technology, applications and best practices with collection, integration, analysis, and presentation of business information?
From here on, it takes shape of an interview or interrogation.
1. How are business decisions taken?
2. What information do business users need to make decision?
3. How frequently do they refer to facts?
4. How should information be delivered for most effective consumption?
5. What methods or analysis should be applied to data to simplify the judgement process?
Some of the answers were evident while others needed counselling from expert users. Because my focus is on IT Service Management within my organization, these answers were to be applied to a large organization divided into various IT Services managing different IT businesses. One thing that clearly stood out in the search of answers was need of standard set of metrics and KPIs. The purpose of BI is to measure performance of metrics/KPIs against goals, describe their attributes and diagnose their behaviour. Well groomed metrics are a bedrock of an effective BI process.
Design Paradigm
I treat BI like any other physical product or service. It requires input, processing and is delivered as product with an aim of reaching maximum consumer base. The best model to sell any product to maximum number of consumers is e-Commerce model. Make products available through a catalog and let people chose what they want. For maximum satisfaction, make products configurable so that consumers can chose the customizations as per their need. This customization ensures that while users don't have to bother about how products were built, they retain control on how it is consumed. We live in an era of heightened consciousness hence providing transparency about the processing mechanism adds to the value.
By now you would have already made the connection that I decided to offer BI in an e-Commerce model. I call this combination of e-Commerce and BI as "Collated Insights". In Collated Insights world; Metrics/KPIs are offered as products from a catalog of metrics where they are neatly categorized by business processes for easy search. All a consumer has to do is select required metrics. Once selected, these metrics start showing in a pre-configured dashboard template such as a card based view that shows absolute numerical values and a graph over a time scale. Before I start explaining about how my team achieved this, lets spend some time understanding philosophy of Collated Insights.
Collated Insights is a way of analysis that can be distributed and reproduced on demand. As the name signifies - it follows a methodology of collecting and combining insights. This methodology has two primary components - collate and deliver.
Collate
This is the most important of the two processes. In this process, focus is on collecting, compiling and codifying the organizational metrics. It should ideally be done by someone who has good understanding of the business and inclination for continual improvement. Metrics must be properly designed, understood and collected; otherwise they can lead to a trust deficit in them which can be very dangerous. Metrics should be comprehended in their business context with a very clear understanding of what metrics are used for improvement vs performance. Wherever possible, metrics should be defined tops down i.e. start with business measures such as profit, turnover etc. and follow the trail to operational metrics. It is paramount to establish a metrics registry that is enduring and available for reference. This process is akin to establishing a business vision and architecture where rationale and implications should be clearly marked out. A good metric is one that can be tied to an action. Clearly detail metric attributes such as data source, unit, access requirements, thresholds, targets, boundary conditions etc. Each metric must have associated measures which are used to value these metrics. As an example a Time To Resolve (TTR) for support tickets is a metric which can have possible measures such as Total TTR, Mean TTR, TTR percentile etc.
Here's a template for building metrics:
Metric Name: Time to Resolve (TTR) Incidents
Description: This KPI indicates the total amount of time taken (in hours) to resolve incidents.
Source DB Server :
Source Database Name :
Source Database Table :
Column : DurationMinutes
YTD Field (time attribute by which duration is calculated): Incident Resolved Date
Unit: Hours
Measures:
a) Total TTR
Measure Description: This measure is calculated as the sum of total time (in hours) spent in resolving incidents.
Formula: SUM(DurationMinutes)/24
Conditions: <boundary conditions e.g. SQL where clause statements>
b) Mean TTR
Measure Description: This measure is calculated as the average of total time (in hours) spent in resolving incidents.
Formula: AVERAGE(DurationMinutes)/24
Conditions: <boundary conditions e.g. SQL where clause statements>
Deliver
This is where technology plays the major role. Emphasis needs to be given on the way these collated metrics and their associated measures are delivered. BI and analytics is used for business transformation hence ensure it covers three important aspects of people, process and technology. The underlying delivery mechanism should have following attributes:
a) People (Adaptive)
Easy to Use/Intuitive
No specialized skill required
b) Process (Excellence)
Codified
Single Version of truth
Fast Response
c) Technology (Leadership)
Cutting Edge
Scalable
While Collated Insights shares many aspects with traditional BI approach, the differentiating factor is how metrics are delivered. A well designed Collated Insights solution reduces the time to market, cost and trust deficit in the organizational BI ecosystem. In the next and final blog I'll explain the architecture that was used to deliver it.
By now you would have already made the connection that I decided to offer BI in an e-Commerce model. I call this combination of e-Commerce and BI as "Collated Insights". In Collated Insights world; Metrics/KPIs are offered as products from a catalog of metrics where they are neatly categorized by business processes for easy search. All a consumer has to do is select required metrics. Once selected, these metrics start showing in a pre-configured dashboard template such as a card based view that shows absolute numerical values and a graph over a time scale. Before I start explaining about how my team achieved this, lets spend some time understanding philosophy of Collated Insights.
Collated Insights is a way of analysis that can be distributed and reproduced on demand. As the name signifies - it follows a methodology of collecting and combining insights. This methodology has two primary components - collate and deliver.
Collate
This is the most important of the two processes. In this process, focus is on collecting, compiling and codifying the organizational metrics. It should ideally be done by someone who has good understanding of the business and inclination for continual improvement. Metrics must be properly designed, understood and collected; otherwise they can lead to a trust deficit in them which can be very dangerous. Metrics should be comprehended in their business context with a very clear understanding of what metrics are used for improvement vs performance. Wherever possible, metrics should be defined tops down i.e. start with business measures such as profit, turnover etc. and follow the trail to operational metrics. It is paramount to establish a metrics registry that is enduring and available for reference. This process is akin to establishing a business vision and architecture where rationale and implications should be clearly marked out. A good metric is one that can be tied to an action. Clearly detail metric attributes such as data source, unit, access requirements, thresholds, targets, boundary conditions etc. Each metric must have associated measures which are used to value these metrics. As an example a Time To Resolve (TTR) for support tickets is a metric which can have possible measures such as Total TTR, Mean TTR, TTR percentile etc.
Here's a template for building metrics:
Metric Name: Time to Resolve (TTR) Incidents
Description: This KPI indicates the total amount of time taken (in hours) to resolve incidents.
Source DB Server :
Source Database Name :
Source Database Table :
Column : DurationMinutes
YTD Field (time attribute by which duration is calculated): Incident Resolved Date
Unit: Hours
Measures:
a) Total TTR
Measure Description: This measure is calculated as the sum of total time (in hours) spent in resolving incidents.
Formula: SUM(DurationMinutes)/24
Conditions: <boundary conditions e.g. SQL where clause statements>
b) Mean TTR
Measure Description: This measure is calculated as the average of total time (in hours) spent in resolving incidents.
Formula: AVERAGE(DurationMinutes)/24
Conditions: <boundary conditions e.g. SQL where clause statements>
Deliver
This is where technology plays the major role. Emphasis needs to be given on the way these collated metrics and their associated measures are delivered. BI and analytics is used for business transformation hence ensure it covers three important aspects of people, process and technology. The underlying delivery mechanism should have following attributes:
a) People (Adaptive)
Easy to Use/Intuitive
No specialized skill required
b) Process (Excellence)
Codified
Single Version of truth
Fast Response
c) Technology (Leadership)
Cutting Edge
Scalable
While Collated Insights shares many aspects with traditional BI approach, the differentiating factor is how metrics are delivered. A well designed Collated Insights solution reduces the time to market, cost and trust deficit in the organizational BI ecosystem. In the next and final blog I'll explain the architecture that was used to deliver it.
