Navigating Today’s Data and AI Market Uncertainty

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Navigating Today’s Data and AI Market Uncertainty

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By Christian Buckner, SVP, Altair

Anyone following the news in the data analytics and artificial intelligence (AI) market knows that significant changes have occurred in recent years. Large analytics companies like Alteryx and Tableau have been the subjects of mergers, acquisitions, and privatization.

The rise of open-source languages has put pressure on foundational analytics technologies like SAS. Start-ups have faced challenges and faced the reality of exhausting cash reserves without achieving sustainable business models. Rapid generative AI adoption has also raised concerns about competitiveness. In this environment, uncertainty in data analytics has reached unprecedented levels.

As a result, it’s crucial to consider the long-term implications of the analytics partnerships you establish. Are you choosing technologies that will endure? Are you selecting companies with proven track records? What are the costs at scale, and how will your team grow with increasing data use? Can your partners support you during difficult times? These have always been essential questions in analytics partnership decisions, but they are particularly crucial in the constantly changing landscape of today.

What to Look for in Data and AI Technology

With the rapid market changes, the involvement of multiple vendors in data delivery increases the risks. Small, specialized software vendors tend to either succeed and get acquired by companies offering a broader range of services or fail to achieve escape velocity. In either case, the outcome for you is disruption.

Instead, organizations should seek data and AI technology providers that offer end-to-end solutions, including data preparation, ETL, autoML, auto forecasting, auto feature engineering, generative AI fine-tuning, model development, workload orchestration, data visualization, and multi-language analytics (Python, R, SQL, and the SAS language).

Furthermore, when these tools are provided by the same technology partner, they are likely to be more naturally and elegantly woven together, saving time and effort in tool integration. This allows data workers to manage workflows more efficiently and wear multiple hats without having to jump from tool to tool to piece together a workflow.

The ideal software partner should provide a seamless, end-to-end solution with both no-code and code-first options, setting the standard for frictionless AI and strong technology partnerships.

What to Look for in Data and AI Business Approaches

However, technology is only part of the equation. While many organizations have excellent technology, they may lack stability on the business side. When choosing partners for handling data analytics and AI needs, organizations must consider companies that demonstrate proven results and stability.

Interruptions and miscommunications caused by unstable partners are unacceptable and pose risks to both short-term and long-term success. Therefore, it is critical to ensure that data vendors have a proven track record. Additionally, partnering with organizations that have deep domain expertise and a commitment to world-class customer service can minimize uncertainty and challenges in day-to-day operations.

Lastly, prioritize partners whose business model and licensing system are designed for customer value, offering more value as their offerings are utilized more. This will ensure a sustainable and mutually beneficial partnership.

To learn more about navigating today’s uncertain data and AI market, join Altair’s free Future.Industry 2024 virtual event, where industry experts converge to discuss the future of frictionless data and AI.

Christian Buckner is SVP of data analytics at Altair. Throughout his career, he has helped innovative organizations elevate data in decision-making and automation to build a better future.

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