Machine Learning & AI: Unleash the Value of Your Data Assets!

An increasing number of modern-day businesses are storing large volumes of data as a part of their operations. However, not all of them manage to tap into its true potential. Many factors prevent companies from leveraging their data assets most efficiently. Rapidly changing environments, disparate data consolidation, and lack of expertise are some of these factors. They, fortunately, can be solved with technologies like artificial intelligence (AI) and machine learning (ML).  

Tapping into the potential of ML and AI

Today both ML and AI have established themselves as the prime tools for unlocking value from data. Right from the self-driving cars of Google to movie recommendations made by Netflix, all of these are examples of solutions powered by AI and ML. However, these technologies are still in a nascent stage, to an extent. As a result, many industry leaders are still struggling to integrate them into their operations and set the correct priorities for their businesses.  

The following steps can be seen as a strategic roadmap for developing AI and ML capabilities at your company, and making their use to deliver tangible value for your business.

• Develop your organization’s data fabric: Even though we live in an economy driven by data, we hardly give data the importance it deserves. Most people do not realize its value till a catastrophic data breach takes place. One of the prime steps of seamlessly deploying ML and AI solutions would be to create a data fabric, or an ecosystem that facilitates well-governed data integration across your firm. In the absence of an efficient data fabric, there is a good chance that valuable data will get stuck in silos, and won’t get democratized, organized, and monetized to the optimal extent.

• Hire the perfect talent: At its very core, AI and ML are team sports. Therefore, you will need a cross-functional team featuring data engineers, data scientists, statisticians, and domain experts. In addition to creating internal talent pipelines, bringing external talent on board can be beneficial for the proper evangelization of AI and ML across your firm.

• Scale up for company-wide adoption: Even though value creation within a company can take multiple forms, at the end of the day, it all boils down to making the core business processes cheaper, faster and/or better. Hence, you need to carry out a comprehensive audit of your essential processes, and subsequently identify potential opportunities for both unsupervised and supervised ML algorithms.

To conclude

Today we are witnessing a massive shift in the business landscape as the popularity of AI and ML contributes to creating a better customer experience, deeper consumer insights, and more predictive models. Therefore, creating and following a systematic roadmap is needed to derive a long-term competitive advantage from AI and ML for your business.

Knoldus team of data scientists can especially help you to build powerful ML models with spark implementation. We aim to complement your business functions and use AI and ML to enhance your enterprise's productivity.