AI can make successful businesses

1. Data Culture

Culture is something that develops, not something that is fictitious. Initially start small, use the collected data to make data driven decisions. Bringing people on board and educating them that the decisions made, backed by data, equips everyone and helps in tackling complex business challenges. Asesh explains, based on a study, business who leverage machine learning and artificial intelligence algorithms have observed benefits, not only to serve their customers or users, but also for employee welfare like what food should be served in cafeteria, what perks should be provided to employees, project tracking, and others valuable insights that are proven to support successful. “It’s the Data Culture in which organizations need to live in. Aligning data to business outcomes, prioritizing tasks and plans based on recommendations from machine learning models, everyone needs to unite and have a shared mission to improve and grow with the power of data”.

2. Self Help Analytics and Learning from Past

Alone we can do so little; together with the help of data and technology, we can do so much. Asesh said, “Time Series Analysis had majorly helped businesses during COVID to explore new sources of revenue, address supply chain issues, cater surge in orders, etc. It is a powerful tool which helps you to be on top of things.” By creating a workflow for data to insights can help bring transparency across all levels of organizations. Having unified data from across the business can become the single source of truth. Using tools like his algorithm of Time Series Analysis and Forecasting, businesses can keep track of their metrics and monitor anomalies as detected. With the ongoing advancements it doesn’t seem like technology is the barrier anymore, it’s more about using the available technology in best ways.

3. Measurable Success

If everyone is moving forward by making informed decisions, then success will take care of itself. How can well defined goals be related to success? Mr. Aishwarya answers – “Defined set of goals can empower a team to create any data assets. Like, interactive visualizations can help monitor key business metrics and thus stakeholders can observe the health of the business. To serve users better, we need to keep track of multiple metrics and address challenges as faced to avoid any breakdowns”.

4. Data Centric AI and Advanced Analytics
Fundamentally, change is almost impossible without collaboration, cooperation, and consensus. Asesh shared, the volume of data which businesses generate these days is massive and processing it manually or by traditional means is not sufficient. By using automated algorithms like multivariate time series analysis, created by scientists like Aishwarya Asesh can unanimously, without any human help, detect shifts in patterns and send alerts when needed the most. Businesses need to quickly address the barriers of entry to use data science capabilities in-order to succeed and be ahead of their competitors. Whether through work automation, insight generation, or other use cases, AI is without a doubt transforming how businesses function. The experts accept that artificial intelligence is a complicated field, and that it’s crucial to evaluate AI honestly and distinguish between science fact and opinion.

5. Data Literacy

People who don’t read have no advantage over those who cannot read. With so much experience in the field of time series analysis and forecasting, what do you feel has become more important? “As crucial as it is to educate the future generations, I also believe that it is crucial to educate the current ones, so that they can grasp how to use AI to support them and their responsibilities.” Mr. Aishwarya said. His most important take on data – “Once organizations start having a data-first mindset to make decisions, businesses can become much more useful and empathetic for customers. The key milestone in the data journey should be celebrated in the small wins, which will be inspiring others to create and solve challenging use cases with their own data and insights”.