Optimizing Your Data and AI Operating Model: A Step-by-Step Guide

Optimizing Your Data and AI Operating Model: A Step-by-Step Guide

Introduction:

In Part 1 of this blog series, we discussed how Databricks empowers organizations to extract value from their data and AI. This time, we will dive into the importance of team structure, dynamics, and responsibilities in achieving a successful target operating model (TOM). Collaboration among different teams within an organization is crucial for executing your TOM. Having a platform that allows engineering, data science, and analysts to work together using the same tools and technical language is essential for achieving a positive Return on Data Assets (RODA). This blog will explore the key elements to consider when building the right team for your AI operating model, such as the maturity of your data foundation, infrastructure and platform administration, and MLOps expertise. By understanding these elements and leveraging the right roles within your development team, you can streamline the process of building data and AI applications, reduce friction during collaboration, and accelerate innovation.

Full Article: Optimizing Your Data and AI Operating Model: A Step-by-Step Guide

How Databricks Enables Collaboration in AI and Data Projects

In Part 1 of this blog series, we discussed how Databricks enables organizations to develop, manage, and operate processes that extract value from their data and AI. This time, we’ll focus on team structure, team dynamics, and responsibilities. To successfully execute your target operating model (TOM), different parts and teams within your organization need to be able to collaborate.

The Importance of Collaboration in AI Projects

Prior to joining Databricks, I worked in consulting and delivered AI projects across industries and in a wide variety of technology stacks, from cloud-native to open source. While the underlying technologies differed, the roles involved in developing and running these applications were roughly the same. Notice that I speak of roles and not individuals; one person within a team can take on multiple roles depending on the size and complexity of the work at hand.

Having a platform that allows different teams or people with different roles like engineering, data science, and analysts to work together using the same tools, to speak the same technical language, and that facilitates the integration of work products is essential to achieve a positive Return on Data Assets (RODA).

Key Elements to Consider When Building the Right Team

When building the right team to execute on your operating model for AI, it is key to take into account the following elements:

1. Maturity of your data foundation: Whether your data is still in silos, stuck in proprietary formats, or difficult to access in a unified way will have big implications on the amount of data engineering work and data platform expertise that is required.

2. Infrastructure and platform administration: Whether you need to maintain or leverage ‘as-a-service’ offerings can greatly impact your overall team composition. Moreover, if your data platform is made up of multiple services and components, the administrative burden of governing and securing data and users and keeping all parts working together can be overwhelming, especially at enterprise scale.

3. MLOps: To make the most of AI, you need to apply it to impact your business. Hiring a full data science team without the right ML engineering expertise or tools to package, test, deploy and monitor is extremely wasteful. Several steps go into running effective end-to-end AI applications, and your operating model should reflect that in the roles involved and in the way model lifecycle management is executed – from use case identification to development to deployment to (perhaps most importantly) utilization.

These three attributes inform your focus and the roles that should be part of your development team. Over time, the prevalence of certain roles might shift as your organization matures along these dimensions and on your platform decisions.

The End-to-End Flow of an AI Operating Model

Because the development of data and AI applications is a highly iterative process, it’s critical that accompanying processes enable teams to work closely together and reduce friction when handovers are made. The diagram below illustrates the end-to-end flow of what your operating model may look like and the roles and responsibilities of the various teams.

Use Case Definition: Aligning Data and Technical Capabilities with Business Objectives

When defining your project’s use case, it is important to work with business stakeholders to align data and technical capabilities to business objectives. A crucial step here is identifying the data requirements, thus, having data owners participate is critical to inform the feasibility of the use case, as is understanding whether the data platform can support it, something that platform owners/architects need to validate. The other elements that are highlighted at this stage are geared towards ensuring the usability of the desired solution both in terms of security and user experience.

Solution Development: Driving the ML/AI Development Cycle

This stage focuses primarily on technical development. Here is where the core ML/AI development cycle, driven by the data engineering, data science, and ML engineering teams takes place, along with all the ancillary steps and elements needed to test, validate, and package the solution. This stage represents the inner loop of MLOps where the onus is on experimentation. Data owners and architects remain critical at this stage to enable the core development team with the right source materials and tools.

Scale and Adopt: Enabling End-Users to Consume and Utilize AI Applications

In a business context, an ML/AI application is only useful if it can be used to affect the business positively, therefore, business stakeholders need to be intimately involved. The main objective at this stage is to develop and operate the right mechanisms and processes to enable end-users to consume and utilize the application outputs. And because business is not static, continuous monitoring of performance and KPIs and the implementation of feedback loops back to the development and data teams are fundamental at this stage.

Conclusion

Developing data and AI projects and applications requires a diverse team and roles. Moreover, new organizational paradigms centered around data exacerbate the need for an AI operating model that can support the new roles within a data-forward organization effectively.

A (multi-cloud) platform that can help simplify and consolidate the whole gamut of infrastructure, data, and tooling requirements, as well as support the required business processes that must run on top of it while at the same time facilitating clear reporting, monitoring, and KPI tracking, is a huge asset. This allows diverse, cross-functional teams to work together more effectively, accelerating time to production and fostering innovation.

If you want to learn more about the principles and how to design your operating model for Data and AI, you can check out Part 1 of this blog series.

Summary: Optimizing Your Data and AI Operating Model: A Step-by-Step Guide

In Part 1 of this blog series, we discussed how Databricks enables organizations to extract value from their data and AI. In Part 2, we will focus on team structure, dynamics, and responsibilities. Collaboration is essential for successfully executing your target operating model (TOM). Having a platform that allows different teams with different roles to work together using the same tools and technical language is crucial for achieving a positive Return on Data Assets (RODA). When building the right team for AI, consider the maturity of your data foundation, infrastructure and platform administration, and MLOps. These attributes inform your team’s focus and the roles involved. The development process should enable teams to work closely together and reduce friction. The operating model may include stages such as use case definition, solution development, and scale and adoption. Each stage requires a combination of roles to extract the most value. An AI operating model that supports diverse roles within a data-forward organization is crucial. A platform that simplifies infrastructure, data, and tooling requirements is a significant asset. By facilitating collaboration, it accelerates time to production and fosters innovation.

Frequently Asked Questions:

1. What is data science and why is it important?

Data science is an interdisciplinary field that involves extracting knowledge and insights from large sets of structured and unstructured data. It combines mathematics, statistics, programming, and domain expertise to uncover patterns, make predictions, and guide decision-making. Data science is essential in various industries as it helps organizations gain a competitive advantage, optimize operations, identify trends, and develop innovative products or services.

2. What are the key skills required for a successful data scientist?

A successful data scientist possesses a combination of technical skills and domain knowledge. Some key skills include proficiency in programming languages like Python or R, strong statistical and mathematical knowledge, data cleaning and preprocessing techniques, data visualization, machine learning algorithms, database querying, and proficiency in tools such as TensorFlow or Hadoop. Additionally, effective communication, problem-solving abilities, and a curious mindset are also crucial for success in data science.

3. What are the steps involved in the data science process?

The data science process typically involves the following steps:
– Defining the problem: Clearly identify the business problem or question that needs to be addressed.
– Data acquisition and exploration: Gather relevant data from various sources and explore its properties, quality, and structure.
– Data preprocessing: Clean, transform, and normalize the data to remove inconsistencies, missing values, or outliers.
– Feature engineering: Select or create meaningful variables that will be used to build models or make predictions.
– Model building: Apply appropriate statistical or machine learning techniques to develop predictive or descriptive models.
– Model evaluation: Assess the performance and accuracy of the models using appropriate evaluation metrics.
– Deployment and monitoring: Implement and deploy the model in a production environment, continuously monitoring its performance and updating as required.

4. What are some common challenges in data science projects?

Data science projects often face various challenges, including:
– Data quality and availability: Limited or poor-quality data can hinder analysis and negatively impact the results.
– Data privacy and security: Protecting sensitive or confidential data while ensuring it is accessible for analysis can be challenging.
– Bias in data or models: Biased data or models can lead to unfair predictions or decision-making, so it’s crucial to address and mitigate biases.
– Scalability and computational complexity: Large datasets or complex models may require significant computational resources, making scalability a challenge.
– Interpreting and communicating results: Extracting actionable insights from complex models and effectively communicating them to stakeholders is essential but can be difficult.

5. How does data science relate to artificial intelligence and machine learning?

Data science is closely tied to artificial intelligence (AI) and machine learning (ML). Data science encompasses the broader field of using data to gain insights, while AI focuses on creating systems with human-like intelligence. Machine learning is a subset of AI that involves training models to predict or classify data without being explicitly programmed. Data science often employs machine learning techniques to analyze and extract patterns from data, which can be used to make predictions, automate tasks, and improve decision-making, contributing to the development and advancement of AI applications.