In the contemporary world, in the midst of the information age, the ability to base decisions on data represents a crucial competitive advantage for any organization.
However, data has a fluid nature, taking shapes according to the perspective of the analysis. Therefore, for this data to become actionable information, it is imperative to organize it precisely and coherently, considering both the perspective of the analyst and the decision maker, referenced to the objectives outlined by the organization they serve.
Faced with this challenge, over eight years of experience developing Sigalei, we explored various approaches to organizing data in a way that makes sense to our customers. This effort culminated in the methodology that will be presented in this article.
We named this methodology PDIO, an acronym that refers to a checklist of the four essential questions we must address to establish the parameters necessary to create a logical structure for intelligence generation, aiming to sustain the data-driven decision-making process.
Essential Questions of the PDIO Methodology:
[P] What are the Information Products?
Information Products refer to the concrete and formatted results that are generated from the processing, analysis and interpretation of data. They are the ways in which information is presented and delivered to recipients, such as decision makers, managers, technical staff or any other relevant audience.
These products can take various forms, such as detailed reports, interactive dashboards, charts, tables, automated alerts, visual presentations, and other data visualization formats. The choice of Information Product format depends on the nature of the decisions to be made, end-user preferences, and communication goals.
In short, Information Products are the means by which insights and conclusions derived from data are communicated clearly, understandably and relevantly to stakeholders, assisting them in making informed and reasoned decisions.
[D] Who are the Decision Makers and what are the Decisions?
Today's data and information infrastructure is being developed for the purpose of serving decision makers, individuals who bear the responsibility of guiding their organizations toward desired goals. Consequently, identifying these decision makers is a crucial step to ensuring appropriate allocation of each piece of information generated.
Clear identification of decision makers is essential, since information needs to be tailored in the information product precisely, considering a series of essential variables:
Regarding decision makers:
- Hierarchy: The decision maker's hierarchical position influences authority, but often, this power is accompanied by time limitations. Therefore, information must be condensed and direct, focusing on crucial points.
- Individual Preferences: Personal preferences vary; some decision makers prefer visual information, while others opt for more textual formats. Understanding the decision maker's preference regarding the presentation of information increases the probability of an accurate interpretation.
- Level of Knowledge, Experience and Context: Each decision maker has a distinct level of knowledge regarding different types of information. Assessing this level is critical to determining the need for contextualization, reducing the risk of misinterpretation.
Regarding decisions:
- Frequency: Decisions can vary in frequency, occurring daily, annually or as requested. Explicating frequency helps determine the extent of the infrastructure needed to produce information, influencing associated costs.
- Impact: Decisions can generate more or less significant consequences. Understanding this impact is crucial to establish acceptable margins of error and determine the maximum justifiable cost to acquire specific information.
- Urgency: The urgency of decisions varies depending on the timing of demand. This limits the viable amount of data to be collected.
[I] What Information is Needed?
Understanding what information is essential for each decision-making process is a fundamental stage in structuring the data and information infrastructure. From this definition, we establish the necessary parameters to guide the next phases of data modeling:
- Collection Phase: During the data collection stage, we identify the essential elements to build the planned analysis and locate their existence in specific databases. At this stage, it is very important to evaluate the data's adherence to reality, to determine its relevance and usefulness. Concurrently, we calculate the cost associated with collection, which can vary depending on the database's quality and its conformity to necessary requirements.
- Structuring: A significant portion of the available data is unstructured or outside the proper format for the information we need to extract. Therefore, we develop structuring algorithms to mold them to our needs. Depending on the level of structuring required, this stage significantly impacts the cost of generating information.
- Filtering Process: During the filtering phase, we establish precise criteria for selecting relevant data, discarding those that do not contribute to building the desired information. A point of extreme importance is the determination of the tolerable margin for data loss, since the cost associated with this loss grows exponentially as this margin is reduced, which is not always accompanied proportionately by benefits.
Annotation Stage: In certain analyses, it becomes essential to introduce annotations in the data based on predefined references. This procedure highlights specific traits of the data, such as an assessment of the degree of impact of a particular data point. In this context, it is of utmost importance to establish the desired level of precision for such annotations, since the greater the accuracy desired, the greater the cost associated with the algorithm employed to perform the annotations.
- Categorization Process: To confer meaning and operational viability to the data, it is essential to categorize it coherently with the perspectives of the team and/or organization. The pinnacle of importance in this phase lies in the selection of the categories that best align with both the team and the organization. It is crucial that these categories are founded on the objectives embraced by the team, which are connected to the “Management Objects”, which we will discuss below.
- Aggregation Stage: In order to achieve a comprehensive view and mold a cohesive panorama from the data, we proceed to aggregate the elements. This approach resembles creating an image on a monitor, where countless pixels join to form the full image. Once all the previous steps have been carried out accurately, this phase tends to flow without major hurdles. The main challenge lies in discovering the appropriate and intuitive visual representation to portray the aggregated data.
- Analysis: Finally, the analysis stage marks the moment when data is made available to the analyst, enabling the formulation of pertinent judgments and interpretations aligned with the decision maker's demands.
[O] What are the Management Objects?
The term "management objects" encompasses the idea of the targets that the team or organization aims to achieve in relation to stakeholders, themes, issues, regulatory requirements, among others. It is around these management objects that the team manages its objectives, tasks and information. Naturally, we already refer to these management objects in our communications. Therefore, this stage corresponds to the moment when we explicate these objects and employ them as the basis for data categorization, ensuring that they take on the meaning outlined by the team, increasing efficiency and effectiveness during the information transmission process.
Sigalei's PDIO Methodology presents itself as a vital track in the journey of transforming data into intelligent decisions. In today's information age, the ability to make data-driven decisions not only provides a competitive advantage, but has also become imperative for organizational success.
The fluidity of data, which can take various forms depending on the analytical approach, reinforces the need for precise and coherent organization. PDIO, forged from eight years of experience at Sigalei, offers a structured and robust approach to this organization, serving both the analyst's and decision-maker's perspectives.
The methodology's key questions – What are the Information Products? Who are the Decision Makers and Decisions? What is the Necessary Information? What are the Management Objects? – sculpt the path that guides the transformation of data into actionable intelligence. From planning to visualization, from collection to analysis, each step is meticulously outlined, considering the nuances of decisions and stakeholders' goals.
Ultimately, the PDIO Methodology translates to more than a mere process. It is the essence that infuses meaning and direction into data, empowering organizations to glean meaningful insights and make informed, strategic decisions. At the heart of PDIO lies the understanding that well-managed data turns into intelligent decisions, transforming how we face challenges and embrace the opportunities of the contemporary world.
