What is Reinforcement Learning (RL)? Definition, Algorithms, Examples
The EM360 team was at Tech Show London 2023 to interview business leaders and industry experts about everything Enterprise technology – from AI to cybersecurity to data.
Yesterday we spoke to Zahid Hossain, VP of Business Development for Tiger Analytics about the importance of data analytics within the enterprise and some of the common challenges in the data analytics space.
Stakeholders struggle to understand the value of AI and analytics
When asked about the current challenges data analytics officers (CAOs) are facing in today’s market, Mr Doshan explained that many struggle to build a team and business stakeholders fail to understand the value of AI strategies and data analytics.
“As a CAO, the first challenge is around building his or her team. Essentially there are a lot of people who are doing online education and that comes with a lot of theoretical knowledge. But how many of them actually do understand the business and bring in practical knowledge which is something which is of increasing demand and supply.”
Stakeholders in companies generally struggle with the understanding of what value or analytics can bring to the table. Quite a lot of times expectations are way too high,” Mr Hossain told us.
“Making sure the organisation really knows how what best AI or analytics can do for them. A lot of people I speak with, like the Chief Analytics Officer, and Chief Data Officer spend quite a lot of time not actually delivering projects but making sure that stakeholders actually understand what we expect.”
Unlocking the value of data analytics means starting from the drawing board
When asked about how companies can unlock the hidden avenues of revenue and value for businesses, Mr Hossain explained that companies must start from the beginning and the basics to create a solid strategy powered by analytics.
“The biggest thing we recommend is to start from the drawing board. Don’t really just jump in. The two biggest reasons why analytics initiatives fail, and one is data. It's garbage in, garbage out. If your data is not in proper shape and order quality is not good, AI will throw out all sorts of misleading information,” Mr Hossain said.
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