See The Value Of Data Intelligence

See The Value Of Data Intelligence

“The true sign of intelligence is not knowledge but imagination” 

Albert Einstein 1879-1955

Digital has impacted human lives in so many ways that consumers themselves have become drivers of industry trends. Irrespective of the domain, it is revolutionizing how a product or service is conceptualized, created, owned, and expired. 

The value chain is continuously expanding, yet the speed of interaction is warped, producing a non-linear, networked interaction. This transformation has influenced every level of business conduct. Business models are dramatically shifting and a company’s value proposition, revenue streams, ownership, and experiences, among other elements, require a clear articulation and sound understanding, by being put in commercial terms to establish a viable relationship. 

In some businesses, the model may be too varied or complex, making it overly difficult to illustrate in a chart or table. Thus, it is a massive change from the previous era and a huge request to comply for many of us. This is where intelligence has a significant role to show us what is happening and what could happen in our business. In many cases, organizations are facing difficulty looking past the potential value of data intelligence, and thus, missing out on the advantages that taking on a data-centric approach would generate. This is causing a big gap between leading companies and average companies. 

The Challenge of Knowledge Gap 

If we look back to the past decade, the industry faced the pivotal challenge of filling the knowledge gap as baby boomers reach retirement and prepare to leave the profession.

The Manufacturing Institute and Deloitte estimate that “2.7 million workers will retire from manufacturing within the next 10 years. Industry leaders worry that manufacturing in the U.S. will be hobbled by an estimated gap of 2 million jobs left unfilled due to the skills gap.” 

This type of skill and knowledge referred to as tacit knowledge, which people possess in their minds, is difficult to quantify or express to others through speech or in writing. Industry leaders have made many attempts to embrace technology with the aim of preserving such knowledge in a structured manner. Managers and leaders with this viewpoint possess experience in using data, information, and knowledge, establishing a framework for knowledge management in their organization. Today we need to see the value of intelligence, create a framework through which machine and human intelligence will collaborate to create value for businesses and society.

Understanding of Intelligence

Einstein said, “The true sign of intelligence is not knowledge but imagination”. 

In our framework of data, information, knowledge, and intelligence, we define, intelligence as the ability to understand and predict internal and external situations. 

In our current circumstances, we need to imagine our business boundary expanded much beyond our normal value chain, imagine competing in a much broader and unknown landscape. It makes the business leaders’ work extremely challenging. The good news is with the increase in computing power and development of various algorithms, artificial intelligence can be of help here. 

Within the next 10 years, all tactical decisions will be made by machines, while strategic decision-making will remain the domain of human beings. 

ValueInfinity Inc.

Consequently, the role of humans may become more restricted in nature, as more and more jobs become automated and machines displace the routine, lower-level tasks that have long been performed by people. 

The Need for a Different Type of Leadership 

Leaders in the present day must recognize that the universal context is changing rapidly. With increasing globalization and advancements in technology, the playing field continues to widen. The occurrence of any event seemingly far away can still impact a company’s business operations and administration much faster and stronger than most managers can detect. 

This calls for a different style of leadership; one in which a significant amount of time is devoted to learning about the external world, connecting with the customers and internal sales team to swiftly figuring out how the organization’s goals and activities can be aligned within a much larger context. 

This would benefit the company and encourage greater internal collaboration to remain meaningful and competitive within the ever-changing environment. It would require leaders to fabricate a thriving environment by designing appropriate roles and providing employees with the right tools and framework to experiment and develop creative and meaningful solutions.

ValueInfinity (VI) predicts that within the next 10 years, all tactical decisions will be made by machines, while strategic decision-making will remain the domain of human beings.

The Changing Work Structure

The past two decades have witnessed the deployment of various blue-collar jobs to machines, from customer service to finance to security. Currently, these machines, appointed with artificial intelligence (AI) technology, have even assumed certain tasks that were formerly set aside for highly educated, trained, white-collar professionals.

 An example includes paralegal jobs in the sphere of law that involves documentation revision are now accomplished with AI algorithms. The daunting reality is that the escalating trend towards automation, coupled with the offshoring and outsourcing of business operations, and resultantly, domestic jobs, clashes with the college aspirations of hopeful students. After graduating, these individuals generally take up entry-level jobs in the knowledge and information sector, which are exceedingly vulnerable to automation, thereby rendering their sought-after white-collar jobs unskilled.

Today, in North America itself, there is a shortage of one million managers, who will need to comprehend the value of data-based intelligence, how it fits within a certain context, and therefore, appreciate its relevance in organizational decision-making.

Fortunately, on the flip side, the same machines that are being developed to outperform humans in various tasks will require human beings to develop and manage the operating systems on which they will be run; there are huge openings for humans. In other words, while old skills may be made obsolete, new and perhaps, more complex skills will be required to perform challenging jobs, possibly steepening workers’ learning curves. 

Data science, machine learning with artificial intelligence, big-data analytics are such high skills for which demand has been sharply mounting. The issue then, that necessitates further investigation is, how can such people be developed within the educational system? Large companies need to take up the responsibilities for building the people, not waiting for experienced professionals in data analytics, while universities work closely with the industry to enhance practical knowledge and insights, and emphasize strategic human skills. 

Today, in North America itself, there is a shortage of one million managers, who will need to comprehend the value of data-based intelligence, how it fits within a certain context, and therefore, appreciate its relevance in organizational decision-making. 

The concern that managers encounter is the uncertain implications of making such a choice when options are incredibly varied or unknown, and for so many years, they have been making prominent decisions with mainly their intuition. This fundamental change management problem can arise, often manifesting as a distrust of the data or analytics in some of the decision-makers.

New Way of Work and Thinking

Historically, human beings have been trained to divide a problem into multiple fragments and solve the larger problem by working out each of the smaller problems. Nonetheless, the unraveling process is not so simple anymore. Data, connectivity, interaction, and interpretation all bring in new opportunities to the larger picture in any given situation. However, this perspective needs to be held in a new way and broadened to incorporate what could not be seen previously.

Machines will encompass the power to handle a larger variety of data at a much greater scale and speed, so this ‘system thinking’ may be better practiced when machines are given data from the value chain.

When the context can be viewed in totality, valuable insights can be obtained and the potential value and return on investment (ROI) can be acquired from one single source of the solution. Creating this context, however, entails breaking down the barriers among functions, departments, organizations, and value chains; this is something that is yet to be done successfully. 

When analytical programs are run to identify problems, discover solutions, or uncover characteristics, a good understanding of the analytical results produced is required, by connecting to the physics of the system. How machines will learn and find a solution to a physical system will be different from how people have been used to seeing this. Machines will encompass the power to handle a larger variety of data at a much larger scale and speed, so this ‘system thinking’ may be better practiced when machines are given data from the value chain. 

Engineers will need to develop the methods to correlate these findings with physical systems, which is expected to take some time. However, this is not a huge worry, we experienced encouraging results in the past. Engineers were eventually able to correlate computational analysis with empirical data in the past, and today, there is more intense analytics within the virtual environment, specifically in the designing of products and solutions, which has reduced the dependency on physical prototypes in the last decade or so. 

We think managers and leaders will also in due course be able to relate the analytical results with the physics of the problem efficiently. To do that, organizations need to put open-minded people together in a cross-functional team and work on building the capabilities.

Tips to the Leadership 

The majority of organizations are unable to recognize the value of data intelligence in their business operations. Discuss and create a cross-functional understanding to establish the fact and potential of a solution.

Following the table, content can serve the purpose of understanding the challenges in achieving the value of data intelligence, causes and set the goal, and creating a good forward-looking plan by the cross-functional leadership team.

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