Project Management week 2 DQ
Enterprise Performance Management
Module 2: Turning Data into Information
3. Enabling Technologies
Figure 3.7 below depicts an example of a high-level reporting architecture.
Not every enterprise follows this structure, but the diagram provides a general sense of how enabling technologies are organized.
There are more front-end tools in the marketplace than what is depicted above, including spreadsheets, data visualization, scorecards, flash reports, etc.
Conclusion:
The gather process helps determine where we are right now. Getting the gather process down to a science benefits the organization in multiple ways (one of which is improving decision reaction time) and lets people spend more time with the analysis of numbers rather than collecting, massaging, and disseminating. The gather science also provides a level of transparency that allows stakeholders to obtain confidence in the numbers they are receiving. If data users know how data were collected, where they came from, and the process applied to put the numbers together BEFORE receiving them, decisions based on facts are well supported.
After successfully completing this module, students will have the tools necessary to begin building gather processes within their enterprises.
Click to Enlarge
(Source: Dimon, 2013, p. 56)
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Check Your Understanding
References
Dimon, R. (2013). Enterprise performance management done right: An operating system for your organization. Hoboken, NJ:
Wiley.
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2. Turning Data into Useful Information
We are all probably familiar with what Dimon (2013) calls soup-du-jour reports (p. 33), reports that change depending upon what the current “fire” is. More often than not, soup-du-jour reports are ad-hoc and are built on the fly to satisfy a very specific need. There are also reports Dimon (2013) refers to as meat and potatoes type reports (p. 33). These reports are canned and typically “pushed” to managers on a regular basis. Examples of meat and potatoes type reports include monthly sales variances, profit center profit and loss reports, accounts receivable aging reports, etc. Pushed reports are ok (although generally no better than that). However, many of them are never used, nor do the stakeholders receiving them know what they mean or what to do with the information.
A good solution to soup-du-jour and meat and potatoes reporting strategies is to provide a “pull” opportunity. Pull is similar to self- serve reporting. This type of reporting is interactive, adaptable, tailored, and reusable. For example, a product manager for an educational organization wants to know how well students are faring in recently redeveloped courses. The organization is interested in pass rates, grade point average, and student success on the final exam. Pull reporting allows the product manager to customize a report that pulls the exact data the organization is looking for at the intervals of time that make the most sense—possibly at the end of a term or comparing term-by-term success.
Turning data into useful information requires several key components, which ensure that the appropriate point of view is considered along with the quality of the information and how the information is delivered.
Point of view
Different people (roles) will require different types of information presented in different ways. Marketing, finance, and information technology managers, for example, will want different types of information. Figure 3.2 provides a great perspective on the types of information different stakeholders might need.
Quality of Information
There are five dimensions to the quality of information:
. Business Qualities: These include growth, speed, risk, etc.
. Scenario Qualities: These include actual, budget, variance, trends, etc.
. Contextual Qualities: These include periodicity, versions, currency, security, performance, etc.
. Process Qualities: These include goal setting, modeling, planning, analyzing, etc.
. People Qualities: These include ownership, role, consumers, etc.
This week’s readings explore each of these dimensions in detail.
Information Delivery
Dimon (2013) describes a generic reporting architecture as having three layers. These layers are depicted in Figure 3.3 below.
Click to Enlarge
(Source: Source: Dimon, 2013, p. 46)
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Extract, transform, load (ETL) software is used to transport the data between layers.
At the bottom of the figure is the transactional layer. This is the transactional system that enters data into the reporting system. Examples include customer relationship management (CRM) software and human resource information systems. Depending on the enterprise, there could be others. Examples include point of sale transactions, inventory, etc.
The data warehouses and operational data stores are where data is combined and stored. Details about the data warehouse and operational data store are included in this week’s readings.
Data Marts use online analytic processing (OLAP) mechanisms and are usually subject specific.
Click to Enlarge
(Source: Dimon, 2013, p. 48)
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1. The Gather Process
The gather process takes quite a bit of time to transform data into numbers that make sense and are usable. This module will help you figure out where you are in terms of gathering data, knowing where you need to go, and knowing whether you need to make adjustments to your current enterprise’s gather process.
The way we consume data has changed dramatically over the years. Dimon (2013) presents an excellent outline of “what was” and “where we are headed” in terms of data consumption.
Taking a closer look at the gather process, you’ll see how all the pieces fit together. Figure 3.1 below depicts the gather process within the management operating system.
If you look at the right side of the figure, you can see that actuals are the input—actuals are the data that come from the various transactional systems after the data have been cleansed, correlated, converted, etc. The actuals are put into the gather process in a consumable way. Once the actuals go into the process, gaps are exposed. For example, a stakeholder may be reviewing financial data against business metrics and realize there isn’t enough data or that the data isn’t paralleling the metrics.
On the top of the figure, you’ll notice plan is an input and variance is an output. Data is not normally housed in transactional systems such as budgets, plans, and forecasts. These data are married with actuals at this point in the gather process. Any variances to the plan, forecasts, gaps, etc., are clearly visible at this point in the gather process.
Inside the figure are financial and operational reporting. These two types of reporting are often combined in the same report. In some enterprises, they are distinct reports. It is becoming more common to have these combined into a single report.
To the left of the figure are analysis and self-serve. Analysis of the data and metadata provides a means for undertaking root cause analysis. This phase of the gather process also helps bring patterns and trends to the forefront. The self-serve aspect of the gather process happens during the analysis phase. It is possible that a stakeholder will want to go back to the gather process to obtain more information that will assist with the analysis phase.
Many enterprises do not have this process automated. Much of the gather process is manual, especially when you get to the plan, variance, and analysis phases. Comparing data from one system to another is often done by taking multiple data sets, “gluing” them together in a spreadsheet, and doing analysis by hand.
Click to Enlarge
(Source: Dimon, 2013, p. 43)
Click to Enlarge
(Source: Dimon, 2013, p. 32)
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