There are two common approaches to adopting data and AI strategies today: Buying and Building.  

Buying, just like how it sounds, means that companies will outsource commercial data and AI platforms with ready-made solutions to manage the complexities of system deployment and maintenance. 

Building, on the other hand, means that instead of relying on a third-party vendor, companies will dedicate resources such as time, talent, and infrastructure to designing and developing their custom solutions in-house.  

Most companies these days rely on one or the other to stay competitive in the AI space. However, while building a comprehensive system in-house provides deep customization and complete control over your data, it requires significant resources to execute. Buying, on the contrary, delivers immediate results and access to cutting-edge tools but often at the cost of flexibility, upfront investment, and the possibility of vendor dependency. It is safe to say that while both approaches have strategic implications, each comes with its unique challenges and limitations. 

But here’s the question: why limit yourself to just one?

The world of data and AI is evolving rapidly, and so must our approach to data strategy. Today, we’ll be examining the challenges faced by both the building and buying approach to data, and propose an ultimate alternative: a hybrid model that gives you the best of both worlds. 

The Limitations of “Buy vs. Build” 

Most companies today choose to purchase a commercial data and AI infrastructure solution because of the “leave it to the professionals” mentality. Vendors who specialize in data management and AI systems have enough resources and expertise whenever complex technical support is needed. However, such a decision is prone to high costs and dependency, especially when customization or long-term scalability is imperative to the company growth. 

Limitations of Buying: Customization, Vendor Lock-In, and Integration

Limited Customization Options

The number one downside of buying is probably the fact that companies no longer have the option to customize their infrastructure to fit specific needs. While most of the commercial data platforms offer promising capabilities, the tools they provide often come as a versatile bundle targeted at companies at all stages of development. This may be ideal for mature and large-scale companies, yet overkill for those who are only looking to improve one area of their operations or workflows. 

Vendor Lock-in

Vendor lock-in happens when an organization becomes too dependent on a third-party platform to meet its operational needs, making it challenging to switch providers or adapt to new technologies over time. 

Integration Challenges

Since different organizations may have adopted different forms of legacy systems, they may be naturally incompatible with proprietary data platforms during the integration process. This might lead to technical blockages and other forms of inefficiencies since one tiny change to the system may lead to disruption of the core functionality within the infrastructure.  

Data Privacy and Security

On the other hand, although most third-party vendors pride themselves in using the most secure and up-to-date encryption protocols, relying on external platforms inherently increases the risk of data breaches or unauthorized access. This becomes crucial for companies dealing with sensitive data such as those in the healthcare or financial industries, because one simple mistake can jeopardize the privacy and trust of their customers, as well as their compliance with regulatory standards. 

On the other hand, building an in-house data infrastructure can offer significant control and customization, but it also comes with a set of challenges that organizations must take into consideration. 

Limitations of Building: Resources, Standardization, and Costs

Lack of Resources

Building a data and AI infrastructure from scratch requires significant resources such as engineers, infrastructure, time, and money. For companies that are just getting started, that’s just another upfront commitment they’re risking to overextend their capabilities without guaranteed returns.

Lack of Standardization

As if building an in-house data solution weren't difficult enough, a lack of experience can lead to a lack of standardized processes during the infrastructure build. This can result in fragmented systems that are inefficient to manage and may not align with industry best practices, potentially hindering the company’s long-term growth.

Cost-effectiveness

Building an in-house data and AI infrastructure may initially appear less costly, but hidden costs often emerge over time. These can include ongoing support and maintenance effort after the infrastructure's been adopted, especially when it comes to hardware updates and upscaling. For smaller organizations, the cumulative expenses can far exceed the expected original cost, making in-house development less viable in the long run. 

Understanding the Hybrid Approach 

A hybrid approach to data strategy leverages pre-built solutions to kickstart the implementation process while still allowing room for customization and scaling as the organization grows.

Here’s a quick overview of how a hybrid model would look like: 

To start, a company should purchase a data and AI operating system that provides the essential tools for its basic data processing needs. 

This basic system should include most out-of-the-box features such as data ingestion, metadata management, data quality insurance, access control, etc. The purpose of having these as a start is to quickly establish a robust foundation for your data infrastructure, saving time and resources while ensuring essential functionalities are in place to support immediate business operations. 

If you’re looking for a foundational operating layer that offers a comprehensive suite of essential data and AI tools right out of the box, Shakudo is an ideal choice. The platform operates on a subscription basis and integrates over 170 best-in-class data tools. You simply select the tools your business currently needs, avoiding unnecessary expenses on unused features. With basic features like automated data ingestion, built-in compliance workflows, customizable machine learning pipelines, and advanced security protocols, this is a perfect foundational system that empowers organizations to deploy AI capabilities efficiently and with minimal operational overhead. 

After you have purchased the foundational operating system, the next step is to build custom features and integrations, specifically targeting your organization's current demand. This phase leverages the flexibility of the hybrid approach, allowing you to utilize the platform’s out-of-the-box capabilities and establish a tailored solution that meets your unique business requirements. 

Possible features you might want to consider include industry-specific data transformations, custom ML model pipelines, specialized reporting, and integration with internal systems or custom compliance workflows.

To explore the specific steps on how to implement such a hybrid approach with real-world applications, read our comprehensive white paper on "Buy, then Build: Maximizing Value with a Hybrid Approach to Your Data and AI OS" where we delve into how businesses can efficiently combine foundational operating systems with customized in-house solutions to address core business needs and scale with flexibility. 

Highlight of the Hybrid Approach 

Cost and Resources

1. A foundational system often costs less than a pre-built solution, reducing upfront investment in human resources and infrastructure. 
2. Companies can get fast access to all the basic data functionalities they need with a foundational system.

Flexibility and Control

1. Organizations can scale up, down, or even extend their data solutions as the demand grows and evolves, minimizing wasted resources on unnecessary capabilities while maximizing cost-efficiency. 
2. The systems can be evolved over time without overhauling all existing investments. 
3. Organizations will have full control over what data solutions they use and how they’d like to use them, ensuring full alignment with business goals while maintaining ownership and security of all data.

Expertise and Experiences 

1. Purchasing an established platform grants immediate access to the expertise and knowledge that comes with the system. 
2. Foundational platform vendors offer robust platforms that are constantly updated by experts with extensive knowledge in areas like data management, machine learning, cyber security, and regulatory compliance,
3. Organizations can leverage the most up-to-date data solutions while spending less time being on the constant outlook for tool and system updates.  

Maintenance and Support 

1. Companies will receive ongoing support through the deployment and maintenance process as the vendor ensures the system remains up-to-date and fully operational. 
2. For companies without a dedicated in-house team, this ongoing support can be a huge benefit when they encounter issues such as software updates and troubleshooting.

Why Shakudo Matters

As a foundational operating layer, Shakudo provides organizations with a flexible infrastructure that seamlessly bridges the gap between pre-built solutions and custom development. The platform currently integrates over 170 best-in-class data and AI tools, giving businesses the flexibility to tailor their data strategy with a wide range of options. 

Operating on a subscription-based model, companies have the freedom to select and pay only for the tools they currently require. By offering a robust framework that enables deep customization while maintaining operational efficiency and scalability, Shakduo lets you rapidly deploy and adapt your technological ecosystems without compromising performance or flexibility.  

Ultimately, Shakudo’s approach allows companies to strike the perfect balance between flexibility and cost-efficiency, offering full control over customization with minimal cost and operational overhead.