Technology has created an ocean of opportunities in the advertising and marketing world as every day, by every minute, there is a new idea, a new innovation happening. Be it the world of artificial intelligence, virtual reality or automation, every day there is something new to be worked with. These tools are even more integral for small-sized businesses and startups who can use them to get the recognition and reach the masses before their competition. But are they using it to their full capacity? We asked a few experts.

The intense focus on the exponential potential of AI and related tools for businesses has thrown up questions around their relevance for the SMEs and whether they would stand to gain from investments in this area. In the early 70s, personal computers were new to business domains with only large-scale companies being able to justify the investment. To a great extent, the scenario with respect to AI and businesses is very similar. However, due to the hype created, small businesses are made to consider AI as probably essential to their survival. Just as in the 80s, when there were exceptions and some businesses considered investment in computers for the future of business, SMEs who see value in such investment to maintain their edge and are able to afford it, should go ahead and evaluate their investment decision based on their business objective and not just because of AI's novelty value.

Data generated by manufacturing or process operations, especially time series data, is very rich in information that can provide actionable insights on the current and future health of the production systems and the products they create. As companies start to digitize their industrial operations, they are learning that they are rich in operational data but poor in the ability to analyze such massive amounts of it. As a result, much of this data goes underutilized as traditional approaches such as regression models, statistical process control (SPC) and optimization have limitations to effectively leverage multivariate trends and uncover new insights for improving operations. A key source of insights in time series data comes from multivariate trends, also called patterns, which are often reviewed forensically to understand past system behavior. Such patterns are often hard to describe and cannot be easily captured by traditional analytics approaches.