4.388 bn internet users, 5.112 bn mobile users and 3.484bn active social media users worldwide (Global Digital Reports 2020)
The fact is, data is taking center stage as the lead role in a plethora of industries, activities and innovations, globally.
Artificial Intelligence provides us with the means to help extract valuable insights from structured and unstructured data for faster better decision-making to raise productivity and lift performance throughout a company's entire value chain.
Although AI has been around since the 1950s, it is only recently that the technology has begun to be clearly visible in real-world applications. In the last 4 years, investment in AI has dramatically risen.
The advancement of this and the seed that lies at the core of the rise of AI can be identified by the following three factors:
Ready access to big data that is being generated from e-commerce, businesses, governments, science, wearables, and social media
The improvement of machine learning (ML)algorithms, which serves as a consequence of large amounts of available data
Greater computing power and the rise of cloud-based services - which helps run sophisticated machine learning algorithms
Part of the challenge can be attributed to the evolution of technology…
Companies attempt to empower their workforce, to make them citizen data scientists only to fail partly because the approach has been too technology focused.
From data warehousing to big data and most of today's AI solutions; all of these are mainly built for Data Analysts, Data Scientists, IT Wizards.
What is clear, data is taking center stage as the lead role in a plethora of industries and innovations globally. Companies constantly evaluate technology, select and deploy solutions to help with all manner of business activities and challenges. As the importance of data in business escalates it's ever more evident companies require a data strategy to frame technology choices and enable insight driven business models. Data science is not a choice anymore it's necessary for long term success.
Data scientists, are in demand and short supply, expensive, difficult to find and tougher to retain. Insufficient expertise creates bottlenecks that slows down progress or completely disadvantages companies who are either unable to afford or attract and retain the necessary people and skillsets.
Data Scientists need a vibrant urban culture and a challenging intellectual environment; however, most SMEs are not in such places and therefore have difficulties to recruit data scientists--or can only attract mediocre talent.
Good data scientists are scarce and thus very expensive. For a functioning data science team, a company typically needs about five data scientists to cover a broad area of expertise and to produce usable results. If the employees are not 100% utilized with Data Science projects, expensive unnecessary costs arise.
Most SMEs do not have interesting enough projects leading to an under-utilization of the work-force and a high fluctuation of employees resulting in a costly knowledge loss.
For the companies that can afford a comprehensive data science team, it’s a challenge to retain people who are probably spending several hours each week looking for new jobs and constantly interacting with potential employers.
So how does an enterprise who can afford data science talent ensure that they have loyalty and don’t add to the turnover rates, spiraling recruitment costs, suffer lost productivity, and hemorrhage knowledge loss…It’s not easy