Experts in various fields mention that the best time to correct a negative behaviour is as close to the behaviour being exhibited as possible. This is true for children, pets and people. Why isn't this so for organizations? Why are they willing to wait days/weeks/months before knowing that a negative behaviour occurred?
What I'm referring to is latency and specifically latency between access to data and the ability to act on it. I've found many organizations are okay with having data that is a day or more latent. This latency has cost organizations significantly as they are effectively blind until the data is accessible.
An Example
To illustrate, I was working with an organization who gives commissions to its call center representatives based on their sales of various services/offerings. On one occasion, the company offered a special incentive for a particular service it was promoting. Each time a rep was able to provision said service, he/she would receive a larger commission than normally paid. The promotion was a great success. A few weeks later the campaign result report showed thousands of customers signed up for the service, higher than what was expected. After two months an executive noted that expenses had increased significantly but revenue had not and he wondered why.
After some investigation, it turned out that the reps were adding the service to each account that they spoke to and would remove it shortly thereafter. The commission report automatically increased a rep's count each time a service was added to an account but did not decrease a rep's count if they service was removed. Since the service was removed prior to being provisioned, the customer did not see a difference on their monthly statement and therefore there were not any customer complaints about their bills. Two months after the start of the incentive, once the abuse was discovered, the commission incentive was stopped and a few reps who were abusing the system quit. Since commissions were being paid weekly based on the report and this abuse was not uncovered for two months and consequently the organization lost close to $500,000. This may not have been completely prevented but could have been minimized if the company had shorter time between event and action (see diagram below).
The shorter the action time, the quicker an organization can act (or react) to changes in its environment. In the previous example, while campaign results were published within weeks, the financial report is published monthly and it took two months for it to drive action. With access to both views of the organization's data (campaign results vs. financial impact), the loss would have been significantly less if not mitigated.
This organization quickly saw the value in reducing latency. I worked with a team to create an operational data store (ODS) and created alerts for behaviours outside of a "normal" range.
Reducing latency
Since the data was directly loaded into the ODS from source systems, we significantly reduced data latency. We reduced analysis latency by creating alerts around key metrics that would allow the organization to manage by exception. Also since we were presenting data captured from all source systems, we could look at a variety of metrics and enable decisions to be made faster thus reducing decision latency.
All three actions combined reduced action time to the point where we could get the right data to the right people at the right time. The ODS could effectively provide a "real time" pulse of the organization's operations which allows managers to make better informed decisions.
"Real time" is a loaded term and means something different to all of us, especially in the context of BI. A topic that I will talk more about in my next post...
What I'm referring to is latency and specifically latency between access to data and the ability to act on it. I've found many organizations are okay with having data that is a day or more latent. This latency has cost organizations significantly as they are effectively blind until the data is accessible.
An Example
To illustrate, I was working with an organization who gives commissions to its call center representatives based on their sales of various services/offerings. On one occasion, the company offered a special incentive for a particular service it was promoting. Each time a rep was able to provision said service, he/she would receive a larger commission than normally paid. The promotion was a great success. A few weeks later the campaign result report showed thousands of customers signed up for the service, higher than what was expected. After two months an executive noted that expenses had increased significantly but revenue had not and he wondered why.
After some investigation, it turned out that the reps were adding the service to each account that they spoke to and would remove it shortly thereafter. The commission report automatically increased a rep's count each time a service was added to an account but did not decrease a rep's count if they service was removed. Since the service was removed prior to being provisioned, the customer did not see a difference on their monthly statement and therefore there were not any customer complaints about their bills. Two months after the start of the incentive, once the abuse was discovered, the commission incentive was stopped and a few reps who were abusing the system quit. Since commissions were being paid weekly based on the report and this abuse was not uncovered for two months and consequently the organization lost close to $500,000. This may not have been completely prevented but could have been minimized if the company had shorter time between event and action (see diagram below).
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| Framework for Right time Business Intelligence - Bolder Technology |
This organization quickly saw the value in reducing latency. I worked with a team to create an operational data store (ODS) and created alerts for behaviours outside of a "normal" range.
Reducing latency
Since the data was directly loaded into the ODS from source systems, we significantly reduced data latency. We reduced analysis latency by creating alerts around key metrics that would allow the organization to manage by exception. Also since we were presenting data captured from all source systems, we could look at a variety of metrics and enable decisions to be made faster thus reducing decision latency.
All three actions combined reduced action time to the point where we could get the right data to the right people at the right time. The ODS could effectively provide a "real time" pulse of the organization's operations which allows managers to make better informed decisions.
"Real time" is a loaded term and means something different to all of us, especially in the context of BI. A topic that I will talk more about in my next post...
