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By Christian Buckner, SVP, Altair
Anyone that has been following the news in the data analytics and artificial intelligence (AI) market knows that the past couple years have seen considerable change. Large analytics companies like Alteryx and Tableau have been the subject of mergers, acquisitions and privatization.
The rise of open-source language has put pressure on foundational analytics technologies like SAS. Start-ups have burned through cash and learned hard lessons, sometimes without ever achieving sustainable business models. And of course, rapid generative AI adoption has made everyone question if they are doing everything they can to keep up with competition. In all, there has never been more uncertainty in data analytics.
As a result, it’s more important than ever to think long-term about the analytics partnerships you forge. Are you choosing technologies that will stand the test of time? Are you choosing companies with proven track records? What do costs look like at the largest scales? How should my team grow as my data use grows? Can my partners help me when things get difficult? These have always been important questions to ask in analytics partnership decisions, but in today’s constantly changing landscape it is especially important to think ahead.
What to Look for in data and AI technology
Let’s start on the technology side. With this much change in the market, more vendors in a data delivery workflow means more risk. Small, specialized software vendors that satisfy only one link in the chain most often have two outcomes: either they succeed and are ultimately acquired by a company with a broader offering, or they never reach escape velocity. Either way, the outcome for you is disruption.
Instead, organizations need to look for data and AI technology that runs the gamut and can do the job from start to finish. On the technology side, organizations need to look for companies that offer everything, including:
- Data preparation
- Extract, transform, and load (ETL)
- AutoML, auto forecasting, and auto feature engineering
- Generative AI fine-tuning
- Model development
- Workload orchestration
- Data visualization
- Multi-language analytics (including in Python, R, SQL, and the SAS language)
Moreover, when all these tools are offered by the same technology partner, chances are they are woven together much more naturally and elegantly. This means you don’t have to spend half your time cobbling together tools, and when your data workers wear multiple hats, they don’t have to jump from tool to tool trying to piece together the workflow themselves.
“If you want your data solutions to stand the test of time, make sure your data vendors have stood the test of time.”
The cherry on top is a software partner that can offer all these things, offer them in a streamlined workflow, and in addition, offer them in a way that empowers those who have specialized data skills as well as those who do not. That way, the data team doesn’t have to do everything. No-code and low-code tools allow stakeholders outside the data team to tackle the small but important tasks that make up 80% of a data teams work, while freeing up the data team to tackle the toughest projects that require serious data science.
Ideally, the same partner can provide the whole package. End-to-end, seamlessly integrated, no-code to code-first. These are hallmarks of frictionless AI and strong technology partners.
What to look for in data and AI business approaches
However, technology is only half the battle. Many organizations have great technology, but do not project stability. Above all on the business side, when looking for a partner to handle their data analytics and AI needs, leaders and organization must prioritize companies that demonstrate both proven results and stability.
Data is everything to today’s cutting-edge organizations. Interruptions and miscommunications caused by unstable partners are unacceptable delays that jeopardize both short- and long-term success. If you want your data solutions to stand the test of time, make sure your data vendors have stood the test of time.
Additionally, you can minimize uncertainty in your day-to-day by partnering with an organization that has deep domain expertise and a proven track record of world-class customer service. Partners are supposed to be that – partners – not merely vendors. You want someone who will be there by your side to help when things get challenging.
Lastly, market uncertainty means everyone is going to be worried about pricing and value. Prioritize partners whose business model and licensing system is designed for customers – you will know them when you see them. You want to find a partner that gives you more value the more you utilize their offerings.
Do you want to learn more about how to navigate today’s uncertainty-filled data and AI market? Be sure to attend 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. He has spent his decades-long career helping innovative organisations build a better future by elevating data in decision-making and automation.
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- Source: https://www.kdnuggets.com/2024/02/altair-navigating-todays-data-ai-market-uncertainty?utm_source=rss&utm_medium=rss&utm_campaign=navigating-todays-data-and-ai-market-uncertainty
