The market is fully engaged with and has a well-established focus on transformational technologies and approaches. That, along with the ever-presence of forms of legacy technologies, systems and data perpetually results in unforeseen gaps in execution and challenges to realizing expected value from those investments. And the variety of new solution options continues to expand exponentially with emergent technologies, compounding the value contribution dynamic.
Organizations are being drawn to many new solutions and services that promise an ability to address a myriad of stated challenges across highly matrixed environments, which is attractive because there are just too many things to consider simultaneously for reasoning alone to be enough. However, there is one key challenge that is inadequately addressed in solution vendor’s value propositions and Point-of-View presentations, namely their reliance on legacy and/or ungoverned data to execute that promise of value.
Today, a common focus and goal is a self-sustainable system, run by algorithms and driven by an ever-increasing volume of data. For example, consider two areas of focus, along with their top challenges:
Companies cannot always accurately predict where they will realize success from their B.I. initiatives. A major contribution of that relates to the data B.I. relies on, which is often outdated, irrelevant, inaccurate, incomplete and from disparate and ungoverned sources. To illustrate, here are the top 3 industry-acknowledged B.I. execution challenges:
1. Inadequate self-service analysis
2. Inability to accurately analyze data across multiple and/or decentralized systems
3. Perpetual challenges unlocking data buried in systems
Up to 70% of BI implementations do not meet business goals, according to Gartner
The Artificial Intelligence (A.I.) fervor is being driven by the need for faster and smarter decisions from the ever-increasing volumes of data. However, the ability to mimic cognitive functions we associate with the human brain are in an early stage of development and evolution. Here are some of the critical challenge areas of focus:
1. Insufficient access to massive and clean datasets, with minimal biases
2. The decreasing half-life of data value and currency
3. A lack of maturity in the functions and application of A.I.
4. The limited ability of the A.I. solution to adapt due to linear specialization in use of A.I.
5. Inability to collaborate due to complex dynamics and lack of depth in areas needed to realize value and an ROI from the technology
According to Experian, 77% of companies believe their bottom line is affected by inaccurate and incomplete data and that on average, those respondents believe 12% of revenue is wasted due to poor data quality
The Human Factor
The science of human behavior traces back to the beginning of Psychology-as-a-Science, in 1876. Instead of interpreting performance differences as human flaws, or errors, there was an analysis of the differences among individuals which led to a better understanding of work and organizational behavior. There was a recognition that team performance was directly affected by group dynamics, including infrastructure, available tools, processes and leadership. And through this discipline it was first discovered that the highest performing teams included members with a diversity of skills.
The Interdependent Relationship Between Technology Processes and Human Behavior
Technology uses systematic methodologies to improving productivity and competence, including sets of processes, procedures and strategies for solving problems related to challenges. More specifically, it is a disciplined execution of focus, analysis, design, development, implementation, and evaluation of solutions to achieve success in improving performance with a reasonable total cost of ownership (TCO). It typically combines three fundamental processes: performance analysis, cause analysis, and an intervention /solution that can effectively solve problems.
However, even with a well-designed solution, the adoption, execution and realization of success may continue to be a mirage unless there is an understanding how the environment (including both visible and invisible factors) is affecting performance.
Inherent in any technology-driven solution model is the agreement that organization work is performed at three or four levels, namely the individual/function/team level, the operations/process level, the organization level and the marketplace level.
Each of the four levels contribute to the origination of performance issues and how the issues permeate the different levels. The value of understanding the inter-dependency between data, technology and human processes is to broaden a perspective that allows a narrower scope of intervention in an otherwise over-complex ecosystem.
Ensure The Emperor is Wearing Clothing
The impact of data-dependent automation technology on human performance has often been isolated and fragmented, with disconnected solutions in different domains. This marginalized coverage has implications for the potential of impact and value realization. There must be a clear understanding about the dynamic of human interaction with automation technology, and an inability to effectively value, govern and trust enterprise data. These are critical requirements for success across emerging technologies and solutions.