Navigating the Data Landscape in 2024: 2 Key Trends to Watch

What are 2024’s crucial data and analytics concepts and solutions? Which new approaches and tools are businesses adopting, and for what reasons?

Our experts provide insights on these emerging trends, equipping you to transform your business and stay ahead in the fast-paced world of data and analytics.

Here are the 2 Key Data and Analytics Trends to Think About This Year

• Generative AI ↵
• Data Contracts ↵

Trend #1: Make Sure your Generative AI Use Cases are Grounded in Strategy

Companies without a clear strategy for Generative AI will quickly fall behind their more innovative competitors.

The Generative AI craze is here to stay. The widespread adoption of LLMs in 2023 — notably accelerated by the release of ChatGPT — marked a significant shift in the business landscape. Major tech companies like Microsoft, Google, and Meta accelerated the adoption of AI even more by embedding Generative AI capabilities into their products — and their product roadmaps tell us that Generative AI is more than a trend.

As AI becomes more ingrained in business operations, it’s leading to a re-examination of data usage, ethics policies, and the need for AI-specific employee training. Companies are now recognizing that failing to adapt to this trend could leave them at a competitive disadvantage.

In 2023, the groundwork was laid for a transformative 2024, where AI will shift from a specialized tool to an integral, mainstream feature in business operations. This transition will necessitate a rethinking of data usage, ethics, and employee training to harness AI’s full potential responsibly. Companies slow to adapt risk being outpaced in an increasingly AI-driven landscape.

– Patrick Vinton, Chief Technology Officer

What does this mean for businesses seeking to use Generative AI?

With Generative AI more approachable than ever, businesses must figure out how to leverage it effectively and responsibly.

To navigate this landscape, your Generative AI strategy should focus on several key areas to ensure comprehensive planning and effective implementation:

• Identify the right use cases: Implement AI to enhance existing operations, ensuring it complements and augments current processes.
• Enable your teams: Conduct extensive training for employees to effectively utilize AI tools, focusing on ethical usage and alignment with company policies.
• Prioritize strong data governance: This is critical to address AI-specific challenges, including the accuracy and security of AI-generated insights, and the enforcement of data privacy practices.
• Manage costs and licensing effectively: Be mindful of the significant cost differences between individual use and organizational scaling of Generative AI, including licensing fees. Develop a cost-optimized strategy to avoid overruns during initial production rollouts.
• Start your R&D in your cloud platform: Begin the process of R&D in testing AI capabilities within cloud hyperscaler platforms, and plan for how you can roll-out AI oriented applications at scale within your organization.

Proceed with Caution

While integrating AI into business operations offers numerous advantages, it’s crucial to approach this transformation with care.

• Be aware of over-reliance on AI without proper understanding and oversight. This can lead to ethical dilemmas, data privacy issues, and biases in AI-driven decisions. Ensure robust data governance and balance Generative AI automation with human judgment. Regular monitoring and assessment are crucial to prevent operational inefficiencies, reputational damage, and legal challenges.
• Recognize the rapid evolution of Generative AI. Familiarize yourself with these platforms and their scalable implementation, ensuring alignment with your business goals and your data strategy, to ensure the success of your Generative AI projects.

Adopting AI in business is more than just a technological upgrade; it’s a strategic transformation. As you contemplate how to effectively implement AI, you must focus on integration, not just adoption. Start with a solid data strategy that includes employee training, ethical AI usage, and robust data governance.

– Kevin Lobo, Managing Director

Trend #2: Data Contracts are the Modern Approach to Data Sharing & Usage

Businesses leveraging streamlined data contracts can rapidly adapt to changes, ensuring data is always up-to-date and accurate.

Gone are the days of broken data integrations due to poor communication between application and analytics teams. Evolving from archaic processes dependent on static Word or Excel documents, data contracts now integrate dynamically into data team’s workflows, bringing much-needed structure and clarity to organizational data practices.

Software developers have been using APIs seemingly forever to accomplish clear integration rules – data teams can think of data contracts as a similar concept applied to their data as it moves between applications and teams.

These formal agreements — actively forged between data producers and consumers — outline dependencies, rules, and standards for data sharing and usage. They specify essential details such as column names, data types, acceptable values, update frequencies, and other critical information, ensuring clear communication across application boundaries.

This shift from standalone, manual documents to code-based, version-controlled contracts addresses the growing complexity of data environments and the need for more accountable data management. Key vendors like dbt have been instrumental in embedding these contracts into workflows, enhancing testing, integration, and change control.

What does it mean for business?

For data consumers, the frustration of logging into dashboards in the morning only to find that they are broken or that data is stale is all-too common. Formalizing data contracts in development and testing workflows means more structured and reliable data interactions, crucial for effective data management.

A modern approach to data contracts also acts as a foundational element in supporting larger distributed frameworks, like data mesh, ensuring higher data integrity and consistency.

Are you ready to get started with data contracts?

To effectively adopt this trend, you should focus on:

• Developing clear standards for data contracts that outline the terms of data exchange, the minimum viable content they must contain, and the expectations of integration in CI/CD and testing workflows. This will ensure consistency that aligns with business needs and compliance standards.
• Clear change controls. Teams should be able to access old versions of interfaces until they are ready to migrate to newer versions by simply referring to a data contract version in their integration.
• Training teams on the importance of data contracts and their role in maintaining data quality and consistency.
• Implementing tools and technologies that support the creation and enforcement of data contracts, such as dbt, to automate and streamline the process. The critical aspect is that data contracts are not standalone but baked into development team’s workflows in receiving and providing data across applications and teams.

Proceed with Caution

As your organization transitions to using data contracts, you must be mindful of the additional complexity and documentation this approach entails. While data contracts bring structure and clarity, they require careful drafting to avoid constraining data flexibility and innovation. You should ensure these contracts are comprehensive yet adaptable, accommodating future data requirements and changes in technology or business strategy. Neglecting these factors might lead to rigid data frameworks that hinder, rather than help, data-driven decision-making.

Data teams suffer from poor communication with upstream application teams. Data consumers suffer from poor communication with data engineers. Data contracts are a great way to proactively communicate cross-team dependencies and to mitigate the risk of changes breaking things.

Definitely don’t do this in Word docs though. Data contracts must be part of a team’s natural workflow – and they must be captured in code, to be easily integrated into testing and easily version controlled.

– Tony Dahlager, VP of Account Management

Don’t Forget About the Core Principles of Data and Analytics

Before considering how you can incorporate these approaches into your data solutions, ensure you have a solid foundation in place: a current data strategy, a well-equipped data stack, and a solid approach to your data and analytics projects.

While there will always be something new and it’s easy to get caught up in the hype, don’t forget that there are core principles of data and analytics that will always serve you well. Every data initiative should be approached with usability, speed, security, stability, and scalability in mind. If you follow these core principles, you will be set up to navigate any worthwhile trend with ease while getting the most value out of your data initiatives.