In today’s world, a successful organization has to achieve data and analytics competencies to stay competitive and relevant. By leveraging detailed relevant data at its disposal and applying variety of analytics on top of it, an organization can position itself ahead of disruptions in its industry. This is mandatory especially for many classic organizations that are under constant threat of disruptions in their own markets by newcomers or agile competitors. Although, nothing can save an obsolete product or business model, analytics is fundamental for new product innovations, and improving existing products and their reach to the right customers the right way.
The use of machine learning (ML) and advanced analytics to create value from corporate or public data is nothing new [1]. Leveraging these technologies for commercial use started in the late ‘80s and the early ‘90s when ML techniques—in particular neural networks and recommenders—and predictive analytics had become very popular. Today, we collectively cover these under “data science[1].” Data science today is an umbrella term spanning the use of data mining, predictive analytics, advanced analytics, statistical modeling, decision science, knowledge discovery in databases (KDD), machine learning, pattern recognition, and even AI to make better decisions based on vast amount of data of all kinds. Data science started its rise then where there were real business interests in specific industries about advanced analysis of data for business decision making. Data science has been the cornerstone of many applications for more than two decades, e.g., in financial (real-time fraud detection) and retail (recommendation systems) verticals. However, the popularity of web products since early ‘2000s helped it become a household name today.
In the last three decades, the genuine interest in treating data as an asset[2] and advanced uses of it for analytical decision making has been the driving force for lots of technology innovations. Figure 1 is an illustration of this goal that has survived the test of time —the desire to collect and process all types of data in granular detail and in conjunction with each other to gain valuable novel insights.
As is typical, such interests started and grew out of specific industries that had realized the need and led by innovators (technology enthusiasts) and early adopters (visionaries). Technologies involved in Figure 1 have been continuously changing at a very fast pace. For example, due to the exponential growth in volume/velocity of data and data variety due to internet connectivity and explosion of web products, data technologies have been under both evolutionary and revolutionary changes. Analytics tools and related technologies have also been continuously improving at great pace with the most significant change being the availability and richness of open-source analytics tools/software. At the same time, the possibilities for classic and new companies to innovate/improve their products and services by leveraging analytics has significantly increased. This has created great new market opportunities. The use of analytics for product innovation and improvement is the norm today. In a nutshell, the general theme of getting from detailed granular data of all kinds to intelligent business decisions has stayed unchanged in the last three decades while the technologies, processes, and implementation details on how to achieve it has been in continuous change.
For original blog, click here
[1] We define data science as “the practice of extracting insights from data using a multitude of disciplines and technologies for the purpose of creating new data products and services or improving the existing ones [1].” Based on this definition, data science is nothing new but the term itself and the recent level of interest in it.
[2] As you have heard, data is the new Oil.
