**2.2 Design thinking and data analytics for developing OPMS**

To approach these OPMS issues, the authors explored two different theoretical backgrounds which could be combined to provide an adequate solution for the prior OPMS issues, at the same time they could be leveraged to overcome each theory's intrinsic limitations. As such, the author initially summarizes each theory's advantages and limitations on **Figure 2**.

This diagram depicts both DT and DA positive and negative points and it can be noticed there are two divergent ways of analyzing the real world according to these methodologies. To the left, Design Thinking takes a broad approach to problemsolving, where it explores different problem-solution frames, through intensive validation and by gathering a vast business knowledge allied with a user-centric approach.

As limitations, some argue over the designer's intrinsic subjectivity on choosing the procedure's iterations and artifacts used, which affects the traditional business' requirement for milestones' definition and compliance. Furthermore, there is a fundamental difference on design thinking and manager thinking methodologies for problem-solving, which can sometimes lead to misunderstandings and attrition on the organizational sphere.

On the other hand, Data Analytics is more focused on analyzing a specific perceived problem or system, with great power for creating valuable and meaningful

**101**

**Figure 4**.

and effortless way.

**Figure 3.**

win-win methodology.

**3. Methodology proposal**

*A Hybrid Human-Data Methodology for the Conception of Operational Performance…*

information for supporting decision-making. It uncovers trends and patterns through immense computing power and algorithmic data processing which could

*Overall representation of DT and DA leveraging points for fulfilling OPMS requirements.*

As limitations, there is a fundamental need for exploring the problem-space to prevent tacking the wrong problem and to lead to the development of a faithful and robust model. Moreover, this type of analysis is very influenced by the intrinsic data quality and the reliable representation of the reality through the captured data. Finally, it needs a dedicated data pipeline to capture, clean and structure the collected data so that different analytic tools can be explored and tested on a flexible

Concluding, the authors propose a combination of these two methodologies to allow for a complete and reliable way of improving the operational performance management systems. The relations and leveraging points between both methodologies and OPMS needs are portrayed on **Figure 3**. Thus, it illustrates that DT is intended to provide the holistic and deep business knowledge, while DA is supposed to provide a data-driven and fact-based management. Additionally, their limitations are conversely minimized by the complementary methodology, making this a

After exploring the perceived topics' strengths and limitations and presenting the rationale for combining DT and DA to enhance the operational performance management systems, the methodological stages are laid down on

not be accomplished with mere human capabilities.

*DOI: http://dx.doi.org/10.5772/intechopen.93631*

**Figure 2.** *Pros and cons of design thinking and data analytics methodologies.* *A Hybrid Human-Data Methodology for the Conception of Operational Performance… DOI: http://dx.doi.org/10.5772/intechopen.93631*

**Figure 3.** *Overall representation of DT and DA leveraging points for fulfilling OPMS requirements.*

information for supporting decision-making. It uncovers trends and patterns through immense computing power and algorithmic data processing which could not be accomplished with mere human capabilities.

As limitations, there is a fundamental need for exploring the problem-space to prevent tacking the wrong problem and to lead to the development of a faithful and robust model. Moreover, this type of analysis is very influenced by the intrinsic data quality and the reliable representation of the reality through the captured data. Finally, it needs a dedicated data pipeline to capture, clean and structure the collected data so that different analytic tools can be explored and tested on a flexible and effortless way.

Concluding, the authors propose a combination of these two methodologies to allow for a complete and reliable way of improving the operational performance management systems. The relations and leveraging points between both methodologies and OPMS needs are portrayed on **Figure 3**. Thus, it illustrates that DT is intended to provide the holistic and deep business knowledge, while DA is supposed to provide a data-driven and fact-based management. Additionally, their limitations are conversely minimized by the complementary methodology, making this a win-win methodology.
