What are the ways to deal with the current uncertainty in the data and artificial intelligence markets?

For those who have been paying attention to data analytics and artificial intelligence (AI) market news, there have been huge changes in the past few years. The rise of open source languages ​​has put pressure on traditional analytics techniques, and companies like SAS have had to face new competition. Startups also go through tough times, burning through cash and learning valuable lessons, sometimes without even finding a sustainable business model. Additionally, the rapid adoption of generative AI has everyone worried about whether they can keep up with the competition. Overall, uncertainty in the world of data analytics has increased as never before.

Therefore, it is more important than ever that analytics partnerships are built with a long-term perspective in mind. Will the chosen technology stand the test of time? Are you choosing a business with a proven track record? What will the cost be at maximum scale? How should teams evolve as data usage grows? When things get tough . Can a partner help me? These have always been important questions to ask when analyzing partner decisions, but in today's ever-changing environment, thinking ahead is even more important.

What to look for in data and AI technologies

On the technology side, as the market changes, the more vendors involved in the data delivery workflow, the greater the risk. Therefore, organizations need to look for a data and AI technology provider that can cover the entire spectrum and provide complete services. Such a business should be able to get the job done from start to finish and provide the following services:

● Data preparation

● Extract, Transform, and Load (ETL)

● Automation, automatic Predictive and automated feature engineering

● Generative AI fine-tuning

● Model development

● Workload orchestration

● Data visualization

● Multi-language analysis (including Python, R, SQL and SAS languages)

Additionally, when all these tools are provided by the same technology partner, they are likely to be woven together more naturally and elegantly. This means half the time is not spent cobbling together tools, and when data workers wear multiple hats, they don’t have to jump from tool to tool trying to cobble together workflows on their own.

The most important thing is a software partner that can provide all these things, provide them in a streamlined workflow, and furthermore, in a way that supports people with professional data skills and those who do not way to provide these things. This way, the data team doesn't have to do everything. No-code and low-code tools enable stakeholders outside the data team to handle the small but important tasks that make up 80% of the data team’s work, while freeing up the data team to tackle the toughest projects that require serious data science.

Ideally, the same partner can provide the entire service package. End-to-end, seamless integration, no-code to code-first. These are the hallmarks of frictionless AI and strong technology partners.

What to look for in a data and AI business approach

However, technology is only half the battle. Many organizations have great technology but lack stability. Most importantly, on the business side, when looking for partners to meet their data analytics and AI needs, leaders and organizations must prioritize those with proven results and stability.

For today’s cutting-edge organizations, data is everything. Disruptions and miscommunication caused by unstable partners are unacceptable delays that jeopardize short- and long-term success. If you want your data solution to stand the test of time, make sure your data provider has.

Additionally, day-to-day uncertainty can be minimized by partnering with an organization that has deep domain expertise and a proven track record of world-class customer service. Partners should be partners, not just suppliers. When things get challenging, you want someone there to help.

Finally, market uncertainty means everyone is worried about pricing and value. Prioritize partners whose business models and licensing systems are designed specifically for their customers.

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