As someone with years of experience in leadership roles, I've consistently stressed the critical nature of staying attuned to trends in the realms of software and data development worldwide. However, it's important to clarify that this goes beyond merely keeping up with the latest 'modern' technology trends. It involves a deeper understanding of how we navigate tasks and projects in both software and data, specifically.
One trend that has emerged is the delegation of data life cycle (DLC) and data operations to non-technical leadership, which I believe is a byproduct of the AI era. While this may seem like a positive development, it often centers on the visualized outcomes, such as measurements or generative Key Performance Indicators (KPIs). While these visualizations are crucial to non-technical stakeholders, they represent only a small part of the broader objective.
Understanding data visualization or even data itself, while important, is insufficient for building a truly scalable and growth-oriented data model. It's relatively straightforward to write a Python script to process vast amounts of data and generate numerical outputs, or even employ machine learning models for predictions. However, ensuring the robustness of the data foundation to support not just immediate visual results but also the evolving needs of the business over time is a different challenge altogether.
One detrimental consequence of this structural shift within organizations is the way we drive change, introduce new features, and even design reporting solutions. It appears that the absence of a sound data foundation, coupled with non-technical leadership driving data solutions, can lead to difficulties in aligning with the Software Development Life Cycle (SDLC). Attempting to force data life cycles (DLC) into the SDLC can result in chaos, as the primary focus tends to be on immediate visual outcomes.
Let's delve into the distinctions between the SDLC and DLC:
The Software Development Life Cycle (SDLC) and the Data Life Cycle (DLC) are both structured approaches used in the IT industry, but they serve different purposes and have distinct areas of focus. Here are the key differences:
Purpose:
SDLC (Software Development Life Cycle): Primarily guides the development of software applications or systems, covering stages like design, coding, testing, and maintenance.
DLC (Data Life Cycle): Concentrates on data management and governance, encompassing processes from data creation to disposal.
Focus:
SDLC: Emphasizes software product development, including coding, testing, and deployment.
DLC: Focuses on the complete lifecycle of data, including collection, storage, access control, quality, integration, and retirement.
Activities:
SDLC: Involves stages such as requirements gathering, coding, testing, and maintenance, guiding software development.
DLC: Encompasses activities like data capture, storage, processing, analysis, reporting, archiving, and disposal, managing data throughout its lifecycle.
Outcome:
SDLC: Results in functional software applications or systems meeting specific requirements.
DLC: Aims for effective management and utilization of data resources, ensuring data accuracy, security, and availability.
Interdependence:
SDLC and DLC: These lifecycles often intersect when software applications involve data processing and storage, requiring careful consideration of data design, storage, and access mechanisms within the software development context.
In summary, when focusing solely on results, which can be akin to features in software development, we may overlook the importance of building a strong data foundation. Implementing a structure resembling the SDLC for the DLC throughout work cycles can lead to data-related issues surfacing too late for effective repair.
Recognizing the differences between software and data requirements is essential. They are interdependent but may not always progress at the same pace. A balanced approach that addresses both aspects is crucial for sustainable growth and data management within an organization.