Data Science Hierarchy of Needs - Explained

March 25, 2023
Table of content

The Data Science Hierarchy of Needs can be well explained by Data Science Pyramid that focuses on the firm data foundation mandatory to attain good data science stability. The pyramid starts with the raw data itself, which may come from many sources, in different formats, and massive amounts. Data Engineers add the context and layout to turn this data into information. Data Management and Governance ensure coordination and quality before this data reaches the final phase. Reporting and Business Intelligence are equally important as they provide a foundation for insight gathering, where information is collected, categorized, and processed to provide analytical outcomes. Finally, Data Science showcases the summit of data into action, depending upon all the foundational phases while also providing a fresh set of robust statistical methodologies.

The data science pyramid is not necessarily a linear approach, meaning that an organization does not need to attain perfection in each phase before transitioning to the next. Instead, a certain level of expertise is required in each phase before moving ahead, and each consecutive transition to the advanced level informs improvements to previous ones. For instance, an organization with a confident grasp on its Data Management and Governance advance towards Reporting and BI, only to figure out different areas for improving data quality. It is essential to know that the data science pyramid depends on the initial value potential. If a company has not already developed a firm data foundation, it is not rational to jump levels in most cases. Instead, organizations would likely enrich more initial value by improving their fundamental and foundational basis before advancing towards data science maturity. The performance of a statistical model directly depends on the value and purity of information it is trained on. Other primary drivers like significant sources, infrastructure, governance, and dashboards come into frame.

Perspectives in Data Science

To utilize your data completely, you have to consider two different perspectives while looking at and handling any data. First of all, there are two perspectives people hold while looking at the data. Either they can see from the perspective of a developer, data scientist, or Machine Learning Engineer, or they may see it from the lens of a business owner. All of these perspectives and viewpoints are very equally critical in deriving benefits from data. Most engineers look at it from the bottom up. It means they focus on how the data will be collected, stored, accessed, and then analyzed to extract actionable insights and patterns. They primarily focus on the engineering aspect of data science to fetch insight and valuable patterns.

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On the other hand, an enterprise owner or business person shows interest in the profits they are likely to gain from the data. They are more interested in the profits they can drive from the data. The best approach to implement a data science pyramid is to merge both perspectives. You need to know how the data is collected, the data roadmap, and the different types of data analytic methodologies to fetch valuable and profitable insight and then how to use these insights to influence your decision-making process and boost profits.

The Data Science Pyramid of Needs

Let’s discuss the hierarchy of needs needed to add value, context, and perspective to the raw data and transform it into valuable insights.

Data Science Hierarchy of Needs

1. Data Acquisition

Data Acquisition focuses on many raw data sources, ranging from various traditional data sources, including ERP systems, Legacy Data Stores, and Operational Systems, to more dynamic and advanced runtime sources such as social media platforms and natural language. Data science has provided immense opportunities and possibilities in data acquisition, as previously seemingly absurd data types can now be used for different purposes using advanced methodologies.

2. Data Engineering

Data Engineering possesses all the activities linked with processing, moving, and storing data. Data Engineering can range from conventional tool-based ETL to custom-built data pipelines, which develop the underlying infrastructure through which data flows and is controlled. It is crucial as it provides the tools and methodologies necessary for the ETL workflows that enable data to move efficiently for advanced processes further up the pyramid.

3. Data Management and Governance

It ensures that intense scrutiny and check mechanisms are being placed on the meta-attributes of data such as data types, cardinality, and value distribution. This phase controls the various activities linked with improving the quality and usability of data by cleaning it and adding useable features. Data Management is a vital middle component because of the algorithms that enable AI and Machine Learning to learn and analyze data.  Therefore, data must be organized, free from errors, up-to-date, and useable.

4. Reporting and Business Intelligence

It includes the tools and methodologies linked with making information readily available to organizations for the analytical processes. It focuses on showcasing information compellingly and understandably to use various decision-making processes; and possesses different data and OLAP data schemas. Reporting and BI add value because it effectively represents your data science outcomes and results to the rest of the organization and non-technical department in the most understandable way possible.  It serves as a medium that connects data science to the primary decision-makers who can then make rational and data-driven decisions to boost the business’s business’s overall performance and profit margin.

5. Data Science

Data Science can be instrumental at the intersection of advanced mathematics, statistics, computer science, and domain expertise. It is an interdisciplinary approach to creating diagnostic, predictive, or contextual insights from massive, complex, and exotic data sources using approved, attentive, and reproducible methodologies.


The overall concept of the pyramid lies in the question of why and how we use data. To turn data into information, then into insight, you need to build massive IT systems to turn raw and seemingly useless and scattered data into organized information to derive actionable insights. Every step you go up the pyramid, you stream or improve some portion of the data, information, or insight process. For instance, data infrastructure & engineering is intended to transform the raw information into something with more context & organization onwards.  The transition from Reporting & BI to Data Science represents the last step of this automation drive.

Also Read: A Basic Guide on Cross-Entropy in Machine Learning

Keep in mind, in the end, if the foundation is weak and based on noisy, incomplete, and unorganized data, the solution will not be optimized. The outcomes could be downright devastating. Instead of jumping steps or avoiding the mandatory internal challenges, ensure the foundation is as strong as possible. By doing so, even if you don’t attain the highest level of the data pyramid, your business will still enjoy the perks of the processed data and analytics for more satisfactory solutions.

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