A Cognitive Analytics Management Framework (CAM-Part 2): Societal Needs, Shared-Value Models, Performance Indicators, Big Data, Business Analytics Models and Tools

Introduction

The cognitive analytics management (CAM) framework was based on the combination of the relevant management and professional practices, the rigorous analytical and scientific models for improving the efficiency productivity of organizations. It is inspired by the natural marriage of living pairs for sustainable growth and development. Nature has distinct but complementary pairs: male and female, day and night, machine and people. Cognitive people use insights to set directions, similar to using light to illuminate darkness; a pianist uses piano, lyrics, and instruments to generate shared value music. The quality of shared value music can be positioned anywhere between two extremes -too poor and outstanding- based on the maestro’s skills, and coordination of these components. Similarly, the framework would not realize its vision with the highest quality of shared values unless it has well understood components , synchronised information and coordinated support from a good management of leadership and teams at an organization. The framework implementation success depends heavily on the availability of data and powerful computing to analyze models that underpin our businesses, economy, and life.

Since CAM framework is a mission driven approach, it requires the identification of societal need. The first section will investigate the cause and effect of societal challenges, and discuss the potential of performance growth in reducing poverty in the presence of shared value models. In the second section, the essential components for innovating shared values are presented starting from innovation concept, culture, process, measurement to management. In the third section, shared value concept, impact, principle, shared value framework for strategic positioning of an organization, differences between shared value models and classical business and corporate social responsibility models are discussed. In the fourth section, successful implementation of shared value models in all sectors which affect society are reviewed and categorized by sectors to provide a better understanding of shared value positive impacts on societal challenges.

In the fifth section, guidelines to develop key performance indicators for mission driven smart objectives are provided, a list of associated traps to avoid when implementing performance management and measurement systems is presented and finally the basic phases to develop a budget performance systems are highlighted. In the sixth section, the new concept of big data and business analytics, their current challenges, potentials, best practices, models and tools are discussed. The essential components for the development of cognitive-analytics capability strategy are presented. These components include: i) Commitment of senior executive leaders to develop a cognitive smart organization, for which an evidence-based cognitive decision making process is adopted; ii) Deployment of data strategy and associated technology infrastructure to capture data and link various datasets sources, and an enterprise integrated system composed of data network, storage management, business modeling analytics and software tools in order to build collaborative partnerships among the eco-system of an organization, the network infrastructure and enterprise systems require investment from committed leaders; iii); Securing skilled workforce and data confident users across disciplines and operating functions; iv) Enabling data appropriately and securely to link, and share across sectors and disciplines. These enablers require governance policies for an open data sharing strategy to realize the intended impacts on stakeholders. This strategy is necessary to enable the transformation of cognitive analytics and technology from a supporting tool into a strategic weapon with positive impacts on stakeholders, with increased accountability, satisfaction, transparency and trust through open data. This cognitive management approach would further lead to faster new scientific discoveries and new business innovations, to meet market-changing products and services; v) developing an internal communication to promote the cognitive innovation and entrepreneurship culture, to attract the right talent that is short everywhere. The scarce critical talent needs the right strategy to attract and the proper incentive and motivation rewards to retain.