Analytics and business intelligence (BI) have long been understood to be fundamental to business success. Today, powerful technologies, including artificial intelligence (AI) and machine learning (ML), make it possible to gain deeper insights into all areas of business activity in order to drive efficiency, reduce waste and gain a better understanding of customers. Truly benefiting from analytics – particularly the most advanced and powerful analytics techniques involving AI – requires developing a top-to-bottom culture of data literacy throughout an organization and this, in my experience, is where many businesses are still failing. This is highlighted by one particular statistic that came up during my recent webinar conversation with Amir Orad, CEO of Sisense. Orad told me that according to his observations, 80 percent of employees in the average organization simply aren't leveraging the analytics that, in theory, are available to them.

Data is everywhere in business, and teams that are adopting data-driven cultures are making huge strides. Tools and strategies for data-based decision making can be applied in so many different ways: for maximizing operational efficiencies, as a team engagement tool or to help build more effective customer acquisition campaigns. In my coaching practice, I've started to see some truly successful implementations of both business intelligence (BI) and artificial intelligence (AI) as execs learn to lead confident, data-driven teams. There's no question that integrating artificial intelligence and business analytics can have a major impact on your team's performance, but there are some key challenges to making it happen. While data can make your organization more efficient, it can also impact your brand identity, the roles on your team and the customer experience.

Machine learning continues to make serious inroads in big data markets, most notably in the automation of tedious business intelligence tasks. Among the emerging applications is discerning and highlighting patterns in enterprise data, then alerting users in real time to any anomalies. That's the premise of a new analytics platform designed to alert users to data deviations and unveiled this week by business analytics specialist Sisense. Its "Pulse" platform leverages machine learning to analyze complex data sets, and then alerts users to anomalies that can be used to track key performance indicators. New York-based Sisense said the automation of performance tracking would free users from having to monitor multiple dashboards and tasks such as manually running analyses to spot anomalies.

Sisense introduced a new tool today called Pulse, which uses machine learning to trigger an alert when it detects results outside of normal parameters for a particular metric. A user can set a Pulse alert to monitor a metric or KPI such as sales activity or win rate. The machine learning component watches the chosen metric and learns over time what's normal. When it detects an anomaly, it sends an alert to the user. What's more, it can determine how the metric has changed over time, so it doesn't continue to trigger alerts for the new normal.