As I discussed in my previous blog, making sense of transaction data can be time consuming and costly. Through many interviews with leading banks and credit unions we determined the importance of having ready-to-analyze data. In this post I’ll walk through the benefits and potential cost-savings of no longer having to dig through various data sources and wait weeks for data compilation.
Most organizations have a combination of some or all of the following reporting cadence:
- Daily Reporting
- Monthly Reporting
- Quarterly Reporting
- Ad hoc Reporting
There are gains to be had in each of these four reporting scenarios if the transaction data is available in a ready-to-analyze state. Daily, monthly, and quarterly reports once defined are typically automated but still require having to wait for the reporting to be available as the data is being collected. Also, since these reports are constantly being altered, they frequently need to be run multiple times. For the purpose of this analysis, we’ll look at the routine costs of regular data collection and pulling reports together.
For urgent and always required ad hoc reporting over a year it is not unusual for the equivalent of a full-time employee to be consumed with this activity (or even two or three employees for larger organizations). As the scenario outlined within the table below illustrates, these numbers add up!
|Medium sized financial institution|
|Number of ATMs||1,000 ATMs|
|Number of business analysts||1 to 3 full-time equivalents (FTE)|
|Fully loaded cost of an analyst||$50/hr|
|Reporting Time Savings||Annual Savings|
|Daily reporting – Having ready access to data saves an average of two hours per day (time spent tweaking and updating regular reporting)||$25,000 (2 hrs x 250 annual working days x $50)|
|Monthly + Quarterly reporting – Having data in a “ready-to-analyze” state reduces material gathering, cleansing and report generation efforts by 25% per month (approx. 2 days /month)Quarterly reporting is typically a summary of monthly reporting, so no additional time is included in the analysis||$9,600 (2 days x 12 times x $50)|
|Ad hoc reporting – 1 FTE constantly responding to a backlog of reports that are required from different lines of business and management relating to the ATM channel||$100,000 (1FTE @ $50/hr)|
|Estimated benefits of “ready-to-analyze” data for a medium sized financial institution||$134,600 per year|
There are numerous other benefits to having an “always on” data source in a ready-to-analyze state. It is much more than the time saved in producing the analysis, but extends to having ‘fresh’ data available in a timely manner. In a future blog post, I’ll talk about:
- Cost savings from
- Replacement of existing outsourced reporting and analytics services
- “In-sourcing” research that is currently out-sourced
- Fewer data cleanup, lineage and extraction costs
- How operating in a just-in-time analytics mode can help decision makers:
- Identify strategies for increasing profitability ahead of the competition
- Improve customer satisfaction and loyalty with richer data and quicker response times
- Identify insights that were not previously obvious
- Enable a paradigm shift from traditional reporting to on-demand self-service Analytics-based data exploration and analysis
It’s a difficult task to change the status quo, especially when these changes go against organizational inertia. Deviation from an established path is often met with skepticism because of the apparent short-term downside of change. However, stagnation means that you are not only standing still but falling behind your competitors who respond more quickly to changes in the technological landscape.
To learn more about how your organization can benefit from easy to ready-to-analyze data and Big Data banking analytics, I invite you to watch an on-demand webinar presented by me and Celent Senior Analyst, Bob Meara: Driving Banking Engagement with Customer Analytics. To arrange for a personal demonstration of INETCO Analytics, email .