Pivot Tables

A simplistic version of OLAP that many users can quickly relate to includes the use of pivot tables in a spreadsheet environment. Pivot tables leverage data in a flat, spreadsheet file to present alternative scenarios that describe a business activity. Through basic spreadsheet functionality, users can quickly generate a table view of relevant variables at a particular level of aggregation. For example, a spreadsheet of data that describes a software company’s sales activities can include numerous rows according to corresponding variables. Hypothetical data recording national sales activities of branches across the country are illustrated in Table 12.1.

With a simple pivot function, Table 12.2 could be calculated with ease.

Table 12.1 Hypothetical Data Recording National Sales Activities

Continued

Table 12.1 Continued

Table 12.2 Sales by Product Category by City

Dynamic Reporting through OLAP

Pivot tables are similar to OLAP in that they provide a multidimensional view of an activity. Enterprise OLAP provides a greater scale to the analytic process, as it provides the platform to address multiple levels of aggregation of data resources, can depict updated views as source data are updated, and can process extremely large volumes of data. With this flexibility, OLAP can help decision makers investigate information addressing multiple descriptive scenarios regarding an operation’s activity, therefore enhancing the knowledge-generation process and overall ability to generate effective strategic conclusions. The diversity of information views involves various dimensions of time, performance metrics, and descriptive variables.

These inputs must be organized to provide information (variables at levels of detail) that describe a business scenario to facilitate decision support for the end user. Consider the graphical view of a cube in Figure 12.2.

Figure 12.2 depicts an illustration of an OLAP cube that facilitates analytics of patient-satisfaction rates at a cardio area in a hospital. The cube presents a multidimensional view of a few variables that could potentially affect patient satisfaction. The platform gives the analyst the ability to query data variables from different levels of detail and in different combinations, through both numeric data and visualization. The tabs at the top of the graphic depict the variables that are available to be analyzed. The scenario depicted illustrates the number of daily visits to a patient by hospital staff (nurses, nurse assistants, technicians) and the corresponding patient-satisfaction rates.

Users also have the ability to change variable views regarding patient satisfaction from different perspectives, including:

■ Time (weekly, monthly, quarterly)

■ Illness severity (critical, observation: according to cardio Diagnosis-Related Group [DRG])

■ Length of stay (LOS; duration of excess of LOS beyond expected)

■ Physician (primary physician, specialty)

Figure 12.2 Multidimensional cube.

By navigating the different dimensions of the cube, the analyst can quickly identify strengths and weaknesses of different operational and patient descriptive variables on the performance metric. OLAP enhances the decision makers’ ability to understand more fully some of the attributes that drive patient satisfaction. Other variables can be introduced with the deliberation of stakeholders (e.g., staff training in patient communication tactics, etc.).

Remember, the digital era including big data entails not only volume of data but also new variables (sources of data). Both of these factors are considered when conducting analytics. In other words, a conceptual model must be generated that best describes the attributes of a desired process (entity to be better understood), and then data corresponding to those variables must be applied to that analytic framework. Big data adds complexity to the generation of the conceptual model as it introduces new descriptive variables that may not have been available or incorporated in the traditional structure of the particular process (e.g., categorizing physician comments on electronic health records [EHRs] during patient visits). The value of big data follows the basic concepts just mentioned; however, it can provide even greater value to the user by providing more robust models that provide greater descriptions and understanding of what affects process performance. In the patient-satisfaction scenario depicted in the preceding text, perhaps a new variable that leverages social media (e.g., hospital Facebook likes) can be incorporated for another frame of reference. When considering big volumes and velocities of data in an OLAP environment, methods such as parallel processing and map reduction of data resources must be considered.

OLAP provides a robust source of business intelligence to decision makers, as it can leverage data resources including big data volumes and also provides a platform that offers a flexible, accurate, and user-friendly mechanism to understand quickly what has happened and what is happening to a business process. The multidimensional framework will give users the power to view multiple scenarios of a given process, such as the following:

■ Are there associations between DRGs and excesses in patient LOS?

■ What association do nurse-to-patient ratios have with readmit rates?

■ How does patient demand for healthcare change during different months of the year?

The key to a valuable OLAP cube involves the combination of a few factors. One of these relates to the concept mentioned earlier, namely, that a cube must effectively describe a business scenario. The conceptual model that is used to build the cube must include noteworthy variables (relevant) with an appropriate detailed format that give users true business intelligence. The next major factor is filling the cube with accurate, current, and consistent data. Deficiencies in either of these areas can quickly render the analytic method useless for decision-making. It should be noted that using OLAP for certain healthcare applications (e.g., outcomes associated with processes) introduces greater complexity than standard industry environments (e.g., sales by product by region) and requires more complex dimension modeling.¹

Analytics at a Glance through Dashboards

In today’s ultrafast, ultracompetitive digital economy, it seems that the more senior a manager you may be, the less time that is available for investigation and drilling around multidimensional cubes. Often, the level of analytics is filtered down to a few insightful reports, ongoing insights absorbed in the marketplace, and the access to real-time dashboards that display key performance indicators relevant to a particular process. These dashboards are designed to provide decision makers with a feedback mechanism as to how an organization is performing closer to real time. In the healthcare industry, streaming data from a medical sensor depicting real-time vital information of a patient takes the importance of a dashboard to a new level (just consider the traditional EKG or heart monitor). The key elements of dashboards are the delineation of relevant key performance indicators (KPIs) to a particular process, timeliness of their readings (currency of information), and finally, a user-friendly visual that provides the decision maker with a clear way of determining whether a process is operating successfully or not. The more traditional visual platform resembles that of an odometer in an automobile, where color schemes of performance reflect that of traffic lights (e.g., green, all is well; yellow, caution; and red, something is wrong and needs to be investigated). However, dashboard technology is quickly evolving where styles can include combinations of a variety of visuals (bar, line, pie charts) according to designated scales and are being utilized by decision makers at all levels in an organization.

The key to the effectiveness of a dashboard design involves its connection to the process at hand and use for decision-making. Displays must be simple to understand and interpret. Just as a simple graphic display must adhere to design conventions (e.g., coherent color scheme, axis labeling, scale), so too must dashboard design, which adds complexity to the process as it combines various visual elements. The true key to a successful dashboard is evident by its effectiveness in providing timely, easy-to-understand decision support of a corresponding process. Dashboards that are too busy (include too many visuals), that are difficult to interpret, can quickly become omitted from an analyst’s arsenal of decision support information.

Consider the dashboard example in Figure 12.3. The various graphic displays are clearly delineated from one another (separate sections) and are clearly labeled. Also, the design includes different visual displays, so the information presentation does not appear to overlap or include a blended view. Finally, complementary but distinctly different KPIs give the decision maker a well-rounded view of a human capital management application in this case.

Figure 12.3 Clearly designed employee analytic dashboard. (From http://www. dashboards-for-business.com/dashboards-templates/business-intelligence/ business-intelligence-executive-dashboard; Domo, Inc., http://www.domo. com.)

Robust Business Intelligence and Drill-Down behind Dashboard Views

Dashboards provide an instantaneous mechanism to analyze the performance status of a process. Organizations with extensive analytic capabilities through business intelligence applications can have OLAP cubes that can be quickly drilled into from a dashboard KPI that provides descriptive analytics of underlying variables that underpin the KPI. A prime example of a digital KPI is the bounce rate on a landing page for an organization, especially when a new marketing initiative has been launched. Perhaps an organization has initiated a digital marketing campaign with banners listed on various complementary referral sites. A red signal indicating a higher than acceptable bounce rate would provide decision makers with a timely analytic alert mechanism to investigate the source of the problem. A real-time cube or report could quickly depict which referral site may be the greatest source of misdirected traffic.

Not all dashboard displays need to be real time, where a simple refresh of data on an interim basis provides decision makers with an accurate indication of whether a process’s performance is adequate. However, the big data era involving high velocity of streaming data resources often requires a real-time dashboard visual of a given process to provide users with a quick view of variable impacts on KPIs.