Data interpretation is the final stage of analysis, where information makes sense and begins to “speak”. This is where the answers to the key questions are formulated: “what to do?” and “how to do?” (Fig. 2.2-5). This stage allows summarizing results, identifying patterns, establishing cause-and-effect relationships and drawing conclusions based on visualization and statistical analysis.
Perhaps the time is not far off when the realization will come that to fully become an effective citizen of one of the new great complex new world states that are now developing, it is as necessary to be able to calculate, to think in terms of averages, maxima and minima, as it is now necessary to be able to read and write (CauseWeb, “Wells/Wilks on Statistical Thinking,”).
– Samuel S. Wilkes, quoted in a 1951 presidential address to the American Statistical Association
According to the report “Data Analytics and Artificial Intelligence in the Implementation of Government Projects” (2024) published by the UK government (gov.uk, “Data Analytics and AI in Government Project Delivery,” 20 Mar. 2024), the implementation of analytics data and artificial intelligence (AI) can significantly improve project management processes, increasing the accuracy of time and cost forecasting, as well as reducing risk and uncertainty. The paper emphasizes that public organizations that use advanced analytical tools achieve higher performance in infrastructure initiatives.
The modern construction business operating in the highly competitive and low margin environment of the fourth industrial revolution can be compared to military operations. Here, the company’s survival and success depend on the speed of obtaining resources and quality information – and thus on timely and informed decision-making (Fig. 7.1-7).
If data visualization is the “intelligence” that provides the overview, then data analytics is the “ammunition” needed for action. It answers the questions: what to do? and how to do it?, forming the basis for gaining a competitive advantage in the market.
Analytics turns disparate data into structured and meaningful information on which to base decisions.
The task of analysts and managers is not just to interpret information, but to offer informed decisions, identify trends, determine relationships between different types of data and classify them in accordance with the goals and specifics of the project. Using visualization tools and statistical analysis methods, they turn data into a strategic asset for the company.
In order to make truly informed decisions in the analytics process, it is necessary to learn how to correctly formulate the questions that are asked of the data. The quality of these questions directly affects the depth of insights gained and, as a consequence, the quality of management decisions.
The past exists only insofar as it is present in the records of today. And what these records represent is determined by the questions we ask. There is no other history than this one (Ministrymagazine, “How science discovered Creation,” January 1986).
– John Archibald Wheeler, physicist 1982.
The art of asking deep questions and thinking critically is a critical skill in working with data. Most people tend to ask simple, superficial questions that require little effort to answer. However, true analysis begins with meaningful and thoughtful questions that can uncover hidden relationships and cause-and-effect relationships in information that may be hidden behind multiple layers of reasoning.
According to the study “Data-Driven Transformation: Accelerating at Scale Now” (BCG, 2017)(BCG, “Data-Driven Transformation: Accelerate at Scale Now,” May 23, 2017), successful digital transformation requires investments in analytic capabilities, change management programs, and alignment of business goals with IT initiatives.Companies that create a data-driven culture should invest in data analytics capabilities and launch change management programs to instill new thinking, behaviors, and ways of working.
Without investment in developing an analytical culture, improving data tools and training professionals, companies will continue to risk making decisions based on outdated or incomplete information – or relying on the subjective opinions of HiPPO managers (Fig. 2.1-9).
Realizing the relevance and the need to constantly update analytics and dashboards inevitably leads management to understand the importance of automating analytical processes. Automation increases the speed of decision-making, reduces the influence of the human factor and ensures data relevance. With the exponential growth of information volumes, speed becomes not just a competitive advantage, but a key factor for sustainable success.
Automation of data analysis and processing processes in general is inextricably linked to the topic of ETL (Extract, Transform, Load). Just as in the automation process we need to transform data, in the ETL process data is extracted from various sources, transformed according to the necessary requirements and loaded into target systems for further use.
