Build or Buy? What to Consider When Looking at Predictive Models
In today’s digital age, predictive models have become an indispensable tool for businesses seeking to anticipate trends, enhance performance, and drive strategic decision-making. Whether it’s for forecasting sales, streamlining operations, or identifying potential risks, these sophisticated algorithms leverage historical data to predict future outcomes with a remarkable degree of accuracy. However, the decision to implement predictive models in an organization often leads to a critical crossroads: should the company invest in building these models in-house or opt to buy pre-built solutions? This article seeks to explore this ‘build versus buy’ conundrum in predictive modeling, helping business leaders make informed decisions based on their unique needs and resources.
The ‘build versus buy’ decision presents a complex challenge for many organizations. On one hand, building predictive models internally allows for customization and control over the end product. However, this option requires significant investment in terms of time, resources, and specialized skills. On the other hand, buying pre-built models can expedite the process, reduce upfront costs, and leverage the expertise of vendors specializing in predictive modeling. However, this may limit customization and may not integrate seamlessly with existing systems or data structures. Navigating this decision requires careful consideration of various factors such as the availability of in-house expertise, the complexity of the task at hand, the state of pre-built models in the market, ongoing maintenance needs, and cost implications. Understanding these elements will provide businesses with a solid foundation for making an informed ‘build or buy’ decision in predictive modeling.
The Build Option: In-House Expertise and Complexity
Building a predictive model internally is a multi-faceted process that involves gathering and cleansing data, selecting the appropriate algorithm, training the model, and validating its performance. This intricate procedure often requires multiple iterations to refine the model and optimize its predictive power.
One of the critical elements in this undertaking is the availability of in-house expertise. Developing predictive models requires more than basic statistical knowledge – it necessitates data scientists, engineers, and analysts who are proficient in machine learning techniques, data infrastructure, and algorithm development. Furthermore, domain expertise is key to understanding the data and the specific business problem at hand, which directly influences the quality of the predictive model. The necessity for data scientists and other specialized roles underscores the depth of expertise needed. These professionals understand the nuances of different predictive modeling techniques, and how to select and apply the most effective method for the data and problem at hand. The need for understanding your data and domain knowledge can’t be understated. The best predictive models are built on a foundation of strong data understanding and a firm grasp of the business problem to be solved.
When it comes to the complexity of building predictive models, one must consider the significant amount of time and resources required for data preparation, model development, and testing. It’s not uncommon for the process to take several months or more, depending on the complexity of the model and the quality of the available data. It’s also crucial to consider the ongoing maintenance and updating required for in-house predictive models. As market conditions and business needs change, models will need regular tweaking and adjustment. This necessitates a commitment to ongoing resources to ensure your models remain effective and accurate.
A successful example of building predictive models in-house is Netflix’s recommendation engine. The streaming giant has leveraged its team of data scientists and engineers to build an algorithm that accurately predicts viewer preferences, significantly enhancing user engagement and content discoverability.
The Buy Option: Availability and Advantages of Pre-Built Models
With the rapid advancements in technology and data science, there is an increasing availability of robust, pre-built predictive models in the market. These models are designed by specialized vendors who have deep domain expertise and advanced technical capabilities. They cover a broad range of applications, from customer behavior prediction to risk assessment, and are ready for immediate deployment in diverse business scenarios.
One of the significant advantages of buying pre-built models is the reduced time-to-value. Unlike building models from scratch, pre-built models can often be deployed rapidly, allowing businesses to start reaping the benefits of predictive analytics much sooner. This quicker time frame can be especially valuable in fast-paced industries where businesses need to make quick, data-driven decisions. Pre-built models also come with the benefit of easy integration with existing systems. Many vendors design their models to be compatible with common business systems and software, reducing the complexity of implementation. This allows businesses to seamlessly incorporate predictive analytics into their existing workflows. Furthermore, buying a pre-built model can potentially result in a lower total cost of ownership. The cost of licensing a pre-built model can often be less than hiring a team of data scientists and maintaining the infrastructure necessary to build and maintain a model internally.
A notable example of a successful implementation of a pre-built model is the use of FICO scores by lending institutions. Developed by Fair Isaac Corporation, FICO scores are a pre-built predictive model that lenders use to assess the credit risk of potential borrowers. This model allows lenders to make quick, consistent, and objective credit decisions, highlighting the power and efficiency of pre-built models.
Cost Implications: A Deeper Dive
A critical component of the build versus buy decision in predictive modeling lies in the cost implications. Both options come with unique financial considerations that can significantly impact your return on investment and the total cost of ownership.
Direct costs are a primary consideration. Building predictive models in-house requires hiring or training staff with the necessary skills, investing in the required hardware and software, and dedicating resources to ongoing maintenance. These expenses can quickly add up, particularly for complex models or those requiring extensive data preparation. On the other hand, buying pre-built models entails licensing fees or subscription costs. While these can seem high upfront, they often include updates, support, and other services that can offset the initial investment.
Indirect costs are equally important. The time and resources spent on building a model in-house could be directed elsewhere in the business. This opportunity cost is a significant factor that’s often overlooked. Conversely, pre-built models could lead to cost savings by accelerating decision-making, improving efficiency, and reducing losses. For instance, an insurance company utilizing a pre-built risk assessment model like Verikai’s could significantly reduce its loss ratio, resulting in substantial cost savings.
Ultimately, the financial implications of the build versus buy decision should be evaluated in the broader context of your business strategy. Balancing immediate costs with long-term value is key. The right decision will depend on your company’s specific circumstances, including available resources, existing infrastructure, and strategic goals.
The Verikai Solution: A Case Study
In the landscape of predictive modeling, Verikai stands out as a prime example of a robust, pre-built solution. The company offers a powerful predictive risk platform that leverages machine learning and AI to provide data-driven risk assessment insights, focusing particularly on the insurance industry.
Verikai’s platform offers insurers a comprehensive risk analysis on over 330 million individuals, utilizing predictive models combined with medical, pharmaceutical, and behavioral data. These insights allow insurers to streamline the underwriting process, improve business outcomes, and grow their business efficiently.
Opting for Verikai’s solution comes with numerous advantages that tie back to points discussed earlier in this article:
- Expertise: Verikai has a team of specialized data scientists and engineers who have developed and refined their predictive models, ensuring high accuracy and performance.
- Reduced Complexity: With a ready-to-use solution like Verikai, businesses can bypass the complexities of developing a predictive model from scratch.
- Time-to-Value: Verikai’s solution can be deployed rapidly, allowing businesses to start leveraging the power of predictive analytics in their decision-making process sooner.
- Integration: Verikai’s platform easily integrates with existing underwriting, policy management, and other systems, making deployment quick and hassle-free.
- Cost: In comparison to building an in-house model, using Verikai’s platform can lead to significant cost savings, from reduced hiring needs to lowered loss ratios.
Overall, the Verikai case study serves as an exemplar of how buying a pre-built predictive model can bring substantial benefits to businesses, emphasizing the importance of carefully considering the build versus buy decision.
Concluding Thoughts: Navigating the Build vs Buy Decision
The decision to build or buy a predictive model is multifaceted, involving a careful evaluation of in-house expertise, the complexity of building a model, the availability and advantages of pre-built models, and the cost implications of both options. Building a predictive model in-house gives businesses control over the model and its development process, but it requires a high level of expertise, significant time, and a commitment to ongoing maintenance. On the other hand, buying a pre-built model, such as the solution provided by Verikai, offers immediate access to expertly developed models, quick deployment, seamless integration, and potential cost savings.
As with any significant business decision, the choice between building or buying a predictive model should align with your company’s strategic goals, resources, and capabilities. Both paths have their merits and challenges, and the best choice varies depending on the unique circumstances of each organization. Ultimately, whether you choose to build or buy, adopting predictive models in your decision-making process can yield significant benefits, from improved efficiency and reduced risk, to data-driven growth and enhanced business outcomes. It’s an investment in your company’s future, ensuring your business stays ahead in the increasingly data-driven world.