Challenges for Startups in Adopting AI and Data Analytics

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In the business world, agility is one of the key differentiators that allow startups to compete against larger enterprises and industry juggernauts. This agility is especially important when it comes to implementing new technologies. Unlike large organizations, in which bloated bureaucracy and rigid hierarchies can make change more difficult, startups can more readily adopt to the latest technologies like big data, advanced analytics, and intelligent machine-learning tools.

Unfortunately, agility is just about the only advantage startups have over large enterprise companies in adopting new technologies. Notable challenges include more limited budgets, the cost of hiring the right talent, and a lack of resources for building, deploying, and maintaining a high-quality data system. But despite these challenges, the immense benefits of adopting a big data infrastructure still make it a worthwhile endeavor.

According to a study by the University of Bridgeport, organizations that use big data and analytics are 36 percent more likely to beat their competitors in revenue growth and operating efficiency. An efficient data analytics infrastructure allows businesses to make more informed decisions on everything from how to design their products or services to how to make supply chains more efficient. 

Simply put, data gives business leaders more confidence in how to steer their business toward success. The only real question is where to start with implementing a high-end but cost-effective data infrastructure system. 

Adopting data analytics

At first, it might be tempting to throw venture capital on the latest whiz-bang analytical tools on the market. But tossing investor money at the problem is not a long-term solution and will only put your startup in a precarious financial position, especially if you haven’t yet attained profitability. Instead, the ideal approach is to start small and slowly scale up your technological investments one piece at a time.

A good place to begin is with a secure and resilient data lake, a centralized repository that allows you to store all your structured and unstructured data at any scale. This means you can store your data as-is, without having to run it through any analytics tools. True, this does mean you’ll be gathering a lot of data that, for now, you can’t analyze or derive any practical value from. But at least by starting with a data lake you can get the ball rolling in gathering the data that you will later use to power the more costly analytical tools that you roll out over time.

Once you begin adding analytical tools, your data scientists and developers can immediately begin accessing your data lake without any need to move that data into a separate analytics system. From there, your team can clean, enrich, and transform the data to provide usable insights that will allow for better business decisions. 

Final thoughts

With the global big data market projected to grow to $103 billion by 2027, it is nearly unavoidable that every business, both big and small, will need to get serious about adopting a high-value data analytics system. While this can be a costly investment, there’s no reason that even a startup can’t be a part of the data transformation that is affecting almost every industry sector today. All it takes is an initial small-scale investment in a reliable data storage platform.

About the Author

Bal Heroor is CEO and Principal at Mactores and has led over 150 business transformations driven by analytics and cutting-edge technology. His team at Mactores are researching and building AI, AR/VR, and Quantum computing solutions for business to gain a competitive advantage.

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