Navigating the Future of Data and AI

3 min read
Jun 28, 2023

With today’s unprecedented speed of innovation, organizations are tasked with managing a staggering amount of change. And while it’s impossible to predict the future, we can glean some insights into where things are heading based on the top trends.

In its 2023 Data and AI Trends Report, Google revealed its top data and AI trends and discussed how they interconnect and evolve in the cloud. Here are some of our key takeaways on data, analytics, and AI trends—and what to consider if you’re incorporating them into your strategic plans.

1. Unify your data cloud platform

Most organizations still deal with siloed data storage and warehouses—even siloed clouds. From databases and data lakes to business intelligence (BI) and AI, organizations need a common infrastructure underpinning their data. 

A unified data cloud platform allows the integration of data into workflows, so your teams can spend less time managing data and more time leveraging it. For example, with a common infrastructure, a manufacturer can bring in data from connected sensors, machines, and systems to better understand, anticipate and mitigate supply chain issues.

Takeaway: Get rid of data silos by unifying your data cloud platform. Ensure your operational and analytical systems are working together for data-driven applications.

2. Build an open data ecosystem that spans clouds

If data is at the heart of innovation, storing it across multiple platforms, point solutions, or closed clouds will significantly limit your capabilities. It will be hard to leverage that data unless you create an open ecosystem that allows you to use and integrate any data format across any platform or cloud. 

An open data approach could involve adopting open source software and open APIs that make it easier to share data from internal and external sources and embracing multi-cloud—and making sure those clouds talk to each other.

Takeaway: Consider adopting open architectures to avoid vendor lock-in, such as SQL-based relational databases like PostgreSQL. These solutions will allow you to more easily share data across platforms.

3. Embrace AI in the cloud

AI is at a tipping point, and by 2025 at least 90% of new enterprise application releases will include embedded AI functionality. In the past, organizations would have considered AI clouds and data clouds separate entities, creating more siloes. Instead, we recommend bringing everything together in a single interface or portal. 

At the same time, it’s important to democratize access and empower employees—for example, by offering low-code training or self-serve capabilities—so everyone can benefit from advanced analytics more broadly across the organization.

AI also brings us a variety of new capabilities, including Generative AI, Conversational AI, and others. Pick the one that’s right for your organization as a starting point, accepting that the technology will evolve and you can pivot with advanced use cases later.

Takeaway: Even if you don’t have data scientists on staff, there are plenty of ready-to-use assets, such as templates and models, that can get you started. Start with small, quick wins, and consider working with a partner to scale your efforts.

4. Rethink your BI strategy 

Business intelligence isn’t new. Organizations have been investing in analytics for years—and yet it hasn’t gained widespread adoption. 

According to Google, this lagging adoption “stems from a lack of trust in the reports and the tools themselves. Traditional reports often deliver inconsistent or inaccurate data because they’re created using stale data copies, siloed tools, and non-standard calculations.”

Embedding analytics into enterprise applications that employees use daily can help drive adoption and quick wins. And by feeding data into BI models, it’s possible to leverage predictive analytics to improve decision-making.

Takeaway: To build trust in reports and tools, you need a single source of truth. Tools like Looker offer a real-time view of business data, and Looker Modeler—a standalone metrics layer—creates a single source of truth by defining metrics across cloud databases and popular BI tools.

Which trends are right for you? 

Organizations vary in willingness to embrace new or trending technology, but for any company, the important question is, which tools will help us achieve our strategic goals more effectively? In some companies, a complete overhaul and entirely new platforms are needed to collect and analyze data—other companies may have all the pieces but lack the capacity or capability to put them together. 

If you know your goals but aren’t sure how to proceed, bringing in a partner can provide much-needed perspective. They will evaluate your existing ecosystem and make recommendations based on their expertise.

As a global IT services company and Google Cloud partner, Pythian helps organizations like yours transform by leveraging data, analytics, and Google Cloud. Get in touch with a Pythian Google Cloud expert and see how our team can help.

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