Analyzing the Complexities of Data Analytics

Tuesday - December 26 2023, 15:13 UTC - 11 months ago

Data analytics is a broad field involving data, tools and processes that help to extract meaningful insights from processed data to make data-informed decisions. Problem-centric thinking is the key to successful outcomes; it involves a deliberate shift in perspective, with the primary emphasis on identifying and solving real-world challenges.

In today's data-driven world, data analytics has become a cornerstone of decision-making. However, despite the transformative potential, a significant number of analytics projects fail.

In a recent article published in Significance, we delved into the intricacies of data analytics, emphasizing the importance of problem-centric thinking in achieving successful outcomes.

A comprehensive analytics framework .

Data analytics is a broad field involving data, tools and processes that include both computational and management processes to extract meaningful insights from processed data to make data-informed decisions. The challenges in data analytics are magnified in the era of big data, when datasets are voluminous, frequently updated and diverse in type and quality.

The image above depicts a comprehensive, orientation-driven framework. Built upon five types of analytics—descriptive, diagnostic, predictive, prescriptive and cognitive—this framework offers a unique lens through which we can navigate complexity, difficulty, and ultimately, value. Complexity is equal to computational complexity, referring to the resources that algorithms consume (time and memory requirements). Difficulty stems from the intricacy of the problem being studied. Value is the ultimate outcome, encompassing better decision-making, improved performance and competitiveness, and organizational success, among others.

It should be noted that the various types of analytics, though presented in a sequence, are not mutually exclusive; they are interconnected and can operate concurrently based on the specific problem at hand. Furthermore, the visual representation of analytics in this manner reflects the notion that as analytics advance, increased complexity and difficulty should be introduced, requiring more resources—both computational and human. However, reality may differ, as there are instances where the implementation of more advanced analytics levels is not necessarily justified.

With so many options to choose from, it makes one wonder: why is having more data or sophisticated data analytics tools not a guarantor of success? There are many reasons why data analytics projects may fail. One reason is that actions are not necessarily taken based on the insights gained, as a result of inertia/management style, resistance, a lack of leadership support, internal politics, or an unwillingness to change. But the answer may also lie in the lack of a problem-centric thinking approach.

Understanding problem-centric thinking .

Problem-centric thinking involves a deliberate shift in perspective, with the primary emphasis on identifying and solving real-world challenges. It encourages organizations to view data analytics not merely as a technological solution but as a means to address specific problems.

The essence of our argument lies in the importance of timely decision-making and bespoke analytics solutions tailored to specific problems. It's not about drowning in an ocean of data or wielding the most advanced tools; it's about an in-depth understanding of the problem and the strategic deployment of computational resources. By embracing this approach, organizations can improve their data-driven decision-making processes, drive agile workflows towards critical insights, and stay well ahead of the competition.