Despite the past hype on management cockpits and the valiant attempts by BI, Process Dashboards, and Decision Management Tools, there hasn’t been an authentic management cockpit for organizations to see the current conditions to take decisive action in an appropriate time frame. A real management cockpit doesn’t just apply high-quality graphics to selected data so that all can see. A management cockpit allows management to grasp complex situations quickly by integrating all the pertinent data, promoting the collaboration of many individual views necessary to make the required decisions as soon as possible, thus taking timely and proper actions. The answer is "Yes," but let’s dig into some of the details to understand more.
What Does a Real Management Cockpit Do?
Besides giving a highly integrated visualization of complex interactions, it enables insight analysis either on-demand or in an automated fashion. Please refer to the decision journey in Figure 1 below. The decision journey can include descriptive analytics to further understand the situation or drill-downs into specific aspects of data for the decision(s). Managers can understand the implications of decisions by leveraging predictive analytics and collaboration with other managers or workers. Some collaborations might involve knowledge bots/agents to understand the impact of potential decisions further. Insights can be further refined with more insight analytics and collaboration.
Further contextual analysis could show the interaction and effects to other contexts to avoid suboptimal decisions or interference/influence on intertwined contexts. For known decisions around normal conditions, the amount of analysis can be lower than emergent decisions around new experience situations. This analysis and collaboration will be completed in a more real-time fashion, shifting the organization's focus to responding quickly to meet threats or opportunities while maintaining business outcomes.
Figure 1 The Decision Journey
What Are the Challenges to Deliver a Management Cockpit?
There is any number of roadblocks to attaining a management cockpit that works. First and foremost is the data. Often the data sources are not readily consumable as they are in Excel and PowerPoint, and the critical information usually comes from multiple technology and data sources. Much labor is used to condense data into actionable insights. This process is cumbersome, time-consuming, and error-prone. Also, it is challenging to collate feedback, comments, and actions necessary from multiple management stakeholders. Managers also need to collaborate while finding solutions and making recommendations, especially in remote worker scenarios.
Also, applying the proper insight analytics in the right sequence can affect the quality and timing. Without good insights, decisions are not optimal. Getting into analysis paralysis and not getting the proper overview for the management to base their decisions on can cause a significant delay in making and executing decisions. In the worse situation making the wrong decision entirely. The critical problem is that Business Managers need a way to gain insight into issues and challenges they face quickly. Those challenges may lie in the external landscape such as products, competition, market changes, sources of raw materials, storms, logistical problems, etc., or an organization's strategy, business processes, risks, etc.
What Does a Real Management Cockpit Consist of?
A management Cockpit is a platform that consists of several moving and connected parts. See figure 2. for a visual description of the details. I will summarize the significant components in the text below.
Figure 2 Management Cockpit Platform Logical Architecture
In order to deliver the results on the right side of the chart above, the following components need to be smoothly operating:
· View Management:
View Management is where organizations can set up roles and associate a pre-architected view for each position. Of course, custom roles can be delivered as needed. Typically, these views indicate the level of detail and types in visualizations proven helpful. Of course, customizations can be made for individuals.
· Visualization Management:
Visualization management contains the basic visualization that can be leveraged with types of data sources and analytical outputs. These components can be organized into a role view, dashboard view, or any other output form.
· Goal Management:
Goal management contains the declaration of the desired business outcomes. These represent the stakeholder's take on performance success in terms of trends as well as detailed outcomes. Often tolerances and trigger points can be declared and set in goal management. It is also an umbrella outcome that aggregates critical performance indicators (KPIs) managed in Performance Management.
· Collaboration Management:
Collaboration management manages the individuals and or groups allowed to collaborate securely. CM will enable notifications, locations, time zones, contact numbers, and notes with permissions. Collaborations can be aggregated and published by the author, issue, stakeholder, etc. In some cases, knowledge bots/agents can be legitimate collaborators.
· Action Management:
Action management will contain the typical responses to a decision, like changing an explicit rule, adding a new bot, change a process/system, keep a decision audit trail, keep a collaboration pattern for future efforts, kick off a new project, contact partners for changes, etc.
· Process Management:
Process management plays two roles here. One is the operational process that delivers the goals and outcomes daily. The other is to capture specific decision processes for future leverage and reusability.
· Performance Management:
Performance management keeps track of and highlights out-of-tolerance situations for known processes, systems, or integrations. It contains guardrails and rules for success. PM is where detailed KPIs are managed.
· Insight Management:
Insight management is where all the insight analytic components are registered and described for future use. IM is also where successful combinations of analytics and AI are saved and documented for future use capitalization.
· Automation Management:
Automation management is where the standard reusable low code implementations, microservices, audit procedures, etc., can be cataloged and described.
· Secure Data Mesh
The data mesh is where logical data views are dynamically linked to an event, patterns, operational, and archived data as the data/information engine that supports the management cockpit. The DM does care if the data source is on the cloud or not and links across cloud resources.
Yes, the management cockpit now exists, but it is still growing and evolving. It will be a key specialized digital business platform now and in the future. Few vendors come close to the above architecture, but some of my favorite candidates pursuing this architecture are Wizly & Tibco. Feel free to click the links provided above.
In our Infrastructure Protection" area of work, we carry out assessments (risk, vulnerabilities, remediation) on country critical infrastructure or company infrastructure. The usual scenario is multiple assessors at multiple sites.
We use what we call a "command and control center" to allocate resources, track progress, consolidate data.
It's not uncommon to have to 30-50 entities, each with 50-100 data points per entity and 10-50 data items per data point.
Things change by the minute, assessors have questions re clauses in compliance standards.
I really cannot see who the work could orchestrated without a free-form search Kbase.
We subscribe to Edith Penrose's RBV (Resource Based View) method but you have to read between the lines in her "The theory of the growth of the firm (1959)" - there is no explicit reference to "RBV" I have ever been able to find in her book. I figure she invented RBV but it only became known as RBV some 35 years later.
This comment has been removed by a blog administrator.ReplyDelete