More than a decade into the Internet of Things (IoT) era, the immense potential of IoT is becoming real. We’re moving from proof of concepts and pilots to projects at scale. What’s become increasingly clear is the vast complexity of deploying IoT solutions at scale and the necessity to do so to become a data-driven business.
Digital transformations are fueling the equivalent of a global data gold rush. We know from leading analyst firms that 90% of all data was created in the last two years and it will grow by a factor of two over the next five years. This has resulted in a changed perception of business, one that requires a shift from conventional business models to those that leverage data to gain actionable insights to improve operations, enhances customer experience and accelerates revenue.
Organizations with the best Artificial intelligence (AI) capabilities are poised to deliver better customer outcomes, maximize revenue opportunities, and leave the competition behind. AI-driven insights can help companies achieve many business objectives such as improving customer loyalty, delivering effective training, reducing the impact of outages or providing better healthcare to patients. Applications are aplenty.
“Water, water, everywhere, nor any drop to drink.” The famous line from Samuel Taylor Coleridge’s epic poem “The Rime of the Ancient Mariner” has a fitting application to today’s data problem. Enterprises are deluged with data, but they often have no way to leverage it. According to most experts, only a small percentage of data is usable and made useful, and most of it is in the dark — thus the term, “dark data.”
As an estimated 92% of enterprises have adopted hybrid and multicloud strategies, according to the 2021 State of the Cloud Report from Flexera, cloud operations (CloudOps) teams face increasing pressure to simultaneously manage costs while improving business outcomes. What levers can CloudOps teams pull to achieve operational objectives such as reducing hybrid and distributed cloud complexity, enhancing security, and automating processes?
The following is Part III of a three-part series. Welcome to the final installment of a three-part series discussing the areas to take seriously when you want to drive business with analytics. In Part I of this series, I discussed how to prioritize data accessibility and how to address the challenges that come with it. Those challenges include: Part II discussed where the disconnect is and addressed how organizations can bridge the gap.
One of the most challenging and complex elements of operating a financial services institution is compliance. Managing risk, security and privacy to earn customers’ trust has long been at the core of financial services, but this foundation has been shaken over recent years.
Organizations in the financial services sector face a unique set of challenges as they consider how to wrangle and process the vast amount of data they collect. During our Financial Services Summit, I was lucky enough to speak to Brian Anthony, chief data officer for the Municipal Securities Rulemaking Board (MSRB), to learn how the MSRB is integrating technologies such as artificial intelligence (AI) and machine learning to modernize its data.
Financial products are no longer characterized by the steps of filling out a form, waiting for a credit decision and, if successful, watching the monthly payments leaving your account.
Where and when should a utility trim vegetation near power lines to best reduce the risk of wildfires? When is the most cost-effective time to take a wind turbine out of service for general maintenance? How can customers be convinced to charge their electric vehicle or consume energy at off-peak times that relieve pressure from the power grid?
