Striking a Balance between People, Data and Technology

The ability of an organization to manage and balance the three main pillars of people, data, and technology has a significant impact on how successful a brand can be. Companies must be able to concentrate on inclusion at work, utilizing AI tools and data resources, and staying connected to both their staff and customers as the workplace becomes more varied, dynamic, and dispersed than ever.

Learn how to create a customer data foundation to enable flawless customer experiences in today’s rapidly changing business environment from the chief revenue officer, SAP CX. Moving from an ad hoc machine learning method to a more production line-based approach can be facilitated by businesses having a clear understanding of the phases required and how obstacles might be handled at each level. The manufacturing and critical infrastructure sector is growing more vulnerable to dangers that may be more serious than data breaches as everything becomes internet-facing and cloud-managed. It’s challenging to keep the same attitude concerning CV gaps in the post-pandemic era. The stigma associated with summarize gaps is examined by Christina Gialleli, head of people operations at Epignosis, along with possible benefits of doing so.

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75% of Brands are Changing their CX Data Strategy

While businesses are aware of how important customer experience is, most struggle to offer the experiences that customers want. Companies are reevaluating their CX data strategy and technologies to achieve it. The chief revenue officer of SAP CX, Jen Bailin, talks about how to create a customer data foundation that links front-office interaction with back-end business operations. As industry experts in technology, we are aware that unlocking revenue, lowering churn, and achieving growth all depend on the provision of a personalized customer experience. It’s difficult to please the customers of today. To get it right, people, procedures, and technology must work together. And while businesses are aware of the significance of the customer experience, many of them are using consumer data ineffectively.

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There’s a Stigma Around Resume Gaps

A professional break on a candidate’s resume was once considered a stain that needed to be skillfully swept under the rug. Others would make sure to disclose it in their application before using techniques like non-chronological resumes. Of course, a year without a job does not automatically indicate that an applicant is unqualified or has a bad character. But up until recently, hiring supervisors would make that assumption – candidates were aware of it. The dominant work culture demanded that elite talent could only be recognized by always being in demand; by never going longer than perhaps a few months without work. This was especially true in a business as competitive as the software industry.

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OT Environments Are Getting Attacked

As usual, the primary driver of cyber attacks on functional technology is financial gain. Ransomware schemes affected over 80% of OT environments last year. Etay Maor, senior director of security strategy for Cato Networks, talks about how OT systems are vulnerable due to aged technology, infrequent patching that is made challenging by work stoppages, and insufficient security resources, as well as how organizations may address these issues. In the past ten years, operational technology (OT) has seen a significant transformation. The development of internet-enabled OT systems, which include industrial control systems and other OT systems like supervisory control and data acquisition systems, distributed control systems, remote terminal units, and programmable logic controllers, is a result of the growing demand for improved system connectivity , quicker equipment maintenance, and better insights into resource utilization.

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The Magic Behind AI-Powered Feature Engineering

Artificial intelligence is essentially the process of going from raw data to machine learning models. The founder and CEO of DotData, Ryohei Fujimaki, delves more into the path, the processes and difficulties, and how new technologies are upending it. When switching from raw data to ML models, businesses encounter many issues. However, moving from an ad-hoc machine learning method to a more production line-based strategy can help organizations. This requires understanding the phases involved and how obstacles can be solved at each level. The data journey is being disrupted, and important new tools are creating new possibilities like automated feature discovery and evaluation.

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