Dashboards - as part of a reporting package, case management, or as a stand-alone environment - are a staple of communications and visualization within most of today’s enterprises looking to present results. Whether they are updated as part of the reporting process or deployed as dynamic dashboards within case management, dashboards have been vital in providing the enterprise with the insights they need to drive business improvement.
Business demand, especially demand centered on the enterprises’ strategic goal to compete on operational excellence, is building upon and growing beyond traditional Business Intelligence (BI) and associated reporting/dashboarding capabilities. Specifically, businesses are accelerating their investments in analytics to drive revenue and earnings growth, customer experience gains, internal efficiencies – with the distinct goal of competing on the basis of operational excellence. To achieve competitive advantage in revenue management, customer care, or product management processes (or others), businesses need to control and harness these processes in order to minimize risk and create differentiation in the market.
This is easier said than done. Competing on the basis of operational excellence is in great part premised on confronting three dominant business process realities:
- Operations are composed of integrated processes that span numerous business functions and systems
- Business rules that govern process performance are highly conditional, opaque, and often hidden inside IT systems
- The data that fuels processes are rapidly growing, dynamic, diverse in format, and managed (sometimes as silos) across a set of operational systems, warehouses, and user desktops.
In other words, today’s processes are big, dense, and often opaque to the business stakeholders attempting to de-risk, improve, or capitalize on them.
There are many factors involved in overcoming business process challenges; arguably, one of the most important factors is gaining visibility to the processes themselves. Using the logic that “one cannot solve what one cannot see,” process visualization becomes a gating factor to controlling and harnessing business processes.
However, for process analytics where the business needs to model and analyze the process on a continuous basis to detect risk and identify evolving opportunities, the analytic must be treated differently which makes the need for visualization distinctly greater. Limiting visualization to outputs and building analytics in code significantly limits the effectiveness or even the utility of process analytics. As a result, three key obstacles exist in the quest for visualization in process analytics:
- The Discovery-Requirements Paradox: Today’s waterfall development approach depends on a set of requirements that enable developers to build the queries. This creates a paradox in that waterfall analytic processes depend on requirements to build the queries, but the process is not sufficiently understood to develop the requirements themselves. This can make the analytic undoable or make the process so long and costly that it destroys the business case.
- The Hypothetical Problem: In some cases, business experts execute a set of hypotheses based on incomplete or overly high-level understanding of both the risks and opportunities necessary to produce the intended result. But what if the underlying bottom-up reality is different in the business assumption or hypothesis? The analytic will produce little value and little visibility as to where the risk or opportunity lay. More to the point, without bottom-up process discovery, the business stakeholders must be expert (at a detailed level) and current on the reality of the dynamic and complex processes needed to produce the intended result.
- The De-coupling Challenge: One of the natural outcomes of code-based analytics is that it de-couples the business expert and the developer. This creates obvious difficulties in having them understand and communicate with each other as they go back and forth in developing and optimizing the analytics. This approach artificially separates the combined expertise of the business expert who understands the operations and the analysts that understands how to apply the analytics.
So what are the visualization capabilities that are needed to overcome process complexities and achieve the business’ strategic goals? First and foremost, “who” sees the process and “how” they see it matters to the quality and impact of visualization. Collaboration among the business experts that understand the process and the analysts that can use the technology is intrinsic to creating the needed value from discovering the process to driving improvements. To do this, the analytics, directly enabled by visualization, need to provide an end-to-end capability from discovery to reporting including:
- Reporting within the Analytic: Permits operational leaders and executives to graphically understand results, directly engage in the analytic to understand how results were developed, and trace back through the analytic and operational source data to ensure full confidence in the results.
- Visual Development Environment: Uses a unified visual environment, from reading source data through to executing controls and driving and modeling improvement. A critical component is a collaborative discovery and analytic capability that allows analysts and business experts to explore different analytic paths, to profile and identify patterns in the data and logic model, and to analyze both risk and opportunities.
- Atomic-Level Data and Logic: Allows drill down to the atomic-level data and logic in order to understand the process, risk, and opportunity at the most detailed level. This provides insight which may be obscured by aggregate-level data analysis, and delivers transparency which ensures the validity of results.
- Traditional Reporting and Dashboarding: Delivers results into a reporting and dashboarding environment as part of a well-established reporting process, and enables user-defined queries into the analytic.
These capabilities change the game, giving business experts and analysts fundamentally new visibility and analytic power. New visualization capabilities also overcome the three most common challenges mentioned previously. For instance, by acquiring all of the data and logic and providing an easy-to-understand visual environment, businesses can attack risk and opportunity with precision and without requirements. This addresses the “discovery requirements paradox” by simply letting the data and the process reveal risk and opportunity through the collaborative discovery and analytic process. The bottom-up approach addresses the “hypothetical problem” by enabling businesses to discover, learn, and analyze based on the actual reality. In this case, hypotheses are simply different paths of exploration that can be easily tested, pursued, or adapted as the process is discovered and analyzed. Lastly, to address the “de-coupling challenge“ a common visual and development environment enables analysts and business experts – i.e. those who know different parts of the integrated process, from order management, call center operations, billing, provisioning, operations, among others – to easily and commonly view, discover, explore, and analyze the process, thereby creating the combination of real knowledge and analytic insights.
Building the Case
Over the next few years, big, dense, complex, and opaque processes will grow to be bigger, denser, and even more complex processes due to the sheer pace of businesses today. The enabling - and critical - role of visualization is not about prettier charts or better dashboard organization. Rather, it is about creating bottom-up, rigorous analytics, that allows the organization to fully capitalize on the collaboration of the business expert and analysts throughout the discovery, analytic, and control process. As such, these capabilities are transitioning from “nice-to-haves” that enhance the analytic process to critical capabilities that are essential to producing the results needed to effectively compete on operational excellence.
About the Author
Victor Milligan is Chief Strategy and Marketing Officer at Martin Dawes Analytics, a leading global, process analytics software provider. For more information, contact Victor at email@example.com, or visit www.mda-data.com