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As businesses increasingly rely on data-driven strategies, tech leaders must carefully consider how and where to deploy analytics solutions.
You know the stakes—your organization relies on you to implement a framework that not only works today but evolves with future needs. This article is designed to help you navigate these challenges and build a data analytics framework that delivers real value. This includes evaluating on-premises, cloud, and hybrid systems, selecting the right tools, and ensuring sufficient technical support.
Where should you deploy? On-premise vs cloud vs hybrid cloud
When it comes to where to deploy, there are three dominant options: on-premises, cloud, and hybrid cloud option—which leverages infrastructure and components of both on-premises and the public cloud.
Quick differences
- On-premises: Traditional deployment where the company manages infrastructure, security, and scaling. Requires skilled IT staff and can be resource-intensive, especially when scaling compute and storage together.
- Cloud: Fully managed by the service provider, offering scalable resources (compute and storage) independently. Lower maintenance overhead but relies on the provider for security and infrastructure management.
- Hybrid cloud: Combines on-premises and cloud solutions, offering flexibility. Sensitive data can remain on-premises while leveraging the cloud for scalability, though it requires integration and more complex management.
On one end of the spectrum, legacy architectures are generally deployed to an on-premises data center where the responsibilities of maintenance, security, and administration are handled by in-house resources that possess specialized skills in these tasks. On the other end of the spectrum, most modern data architectures are fully cloud-based. With a fully cloud-based architecture, maintenance, security, and administration tasks are typically managed by a service provider and can be an attractive option for teams that do not have the resources that have the specialized skillsets required for maintaining an on-premises system. Many businesses choose to implement a hybrid approach where some framework components are deployed in the cloud with some remaining on-premises.
At a minimum, each option differs in maintainability and effort involved in scaling as more processing power and storage space are required. Data volumes continue to grow, as do their complexity and veracity. Even so, larger volumes of data, regardless of their complexity, are a requirement for businesses to deploy in decision-making processes.
With on-premises systems, compute and storage are tightly coupled. If scaling compute power is required, storage will generally need to be scaled as well since there is no method to scale these resources independently. If a business has a compute-intensive process, it typically has to over-provision for storage and vice versa for storage-intensive processes. Maintenance and administration of on-premises systems require around-the-clock supervision by personnel skilled in systems and network administration, database administration, and cyber-security and incident response.
Fully cloud-based architectures are not generally plagued by resource coupling and can scale required resources independently. However, with cloud-based architectures, there are many options to choose from—each with varying degrees of operational complexity. On a positive note, infrastructure management and maintenance, and cyber-security are handled by the service provider alleviating the need for businesses to manage these tasks.
Questions to consider:
- What are our privacy and security requirements and how may they impact our choice of deployment?
- How much do we anticipate our volume of data will grow?
- Do we have capacity to maintain our own environment?
Identify constraints and evaluate tools or methods for handling data
Consider which tools, and methods will be leveraged for data to be collected, cleansed, and consolidated into a single source of truth. These decisions generally hinge on a few key constraints that you should evaluate first. As a leader, you may have to navigate the tension between ambitious goals and practical constraints which is why it’s critical to identify any barriers early on.
Leadership buy in
Questions to consider:
- Does company leadership possess a data-driven mindset and a willingness to invest in decision systems?
- Have you identified champions or advocates to drive your initiative forward?
- Who needs to be involved and in what capacity?
Lack of backing and direction from leadership can cause the project to lose steam rapidly. When executives aren’t fully committed, they may hold back on allocating resources or make choices resulting in pushbacks and possible setbacks. To gain the support of leaders, you must outline the advantages of adopting a data-driven mindset and demonstrate how it aligns with business objectives. Establish a clear change management strategy for keeping everyone informed about developments and promptly handling any issues that arise. Having support from executives will offer the guidance and resources needed for achieving positive outcomes.
Requisite technical skills
Questions to consider:
- Are resources on hand that have the requisite skills in data engineering, profiling, analysis, and modeling?
- Is your team prepared to manage these disciplines over time?
- Where are there gaps in your team?
If there exists a technical skill gap, there must be a commitment to its closure, or the project could be doomed or fail to live up to its goals. Organizations know they need future-ready talent but often focus on current problems. This leads teams to take shortcuts and keep tech staff maintaining old systems, stalling growth. This cycle makes it harder to meet long-term goals and forces talent to focus on the present instead of the future. As a result, organizations are constantly trying to catch up and shift priorities to achieve data-driven growth. To address technical talent shortages, businesses can, by training existing employees, enabling remote work and partnering with academic institutions for talent development, or tap into fractional technical leaders with the necessary skills to build and maintain the data analytics platform.
Sources of data
Questions to consider:
- Where they are located?
- What is the data type, format, and availability?
Understanding the sources of data, where it resides, the type of data and other related characteristics that affect how the data can be integrated into your analytics system is crucial to identify technological constraints. With different sources potentially having different characteristics, it’s important to answer these high level questions early on. Streaming data and data that is updated daily, weekly, or monthly will require different strategies for import.
Subject matter expertise
Questions to consider:
- Does this exist and is there a thorough understanding of the key metrics that drive operational excellence and financial performance?
- Does the data team have access to these subject matter experts?
Subject matter experts bridge the gap between the data, insights and what you’re building. They help ensure that data analytics frameworks have added business context and usually have a deep understanding of key metrics that drive operational excellence and good financial performance.
Get ready to bring the framework to life
Establishing a data analytics framework involves balancing technology, talent, and strategy to create a foundation that drives success. Leadership buy-in and a commitment to closing technical skills gaps are essential to ensuring the framework’s sustainability and effectiveness. Once these critical elements are in place, the focus shifts to implementing a data analytics strategy that brings your vision to life, aligning each step with organizational objectives.
Need support in building out your data analytics framework? Green Leaf’s offers support to guide tech leaders through every step with expert consulting, strategy and execution. Learn more by reaching out or exploring our data service offerings.