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Roundtable Forum Data is the New Currency:

Data in the Cloud, Data Protection and Data with AI/ML

Elevate Xchange Jan 18, 2024, 5:30-8:30p


  1. Decision-making: Data enables businesses to make informed decisions by providing insights into market dynamics, customer behavior, and operational efficiency. By analyzing data, companies can identify trends, patterns, and opportunities that inform strategic planning and resource allocation.

  2. Customer satisfaction: Data allows businesses to understand their customers better and tailor their products, services, and experiences accordingly. By collecting and analyzing customer feedback data, companies can identify pain points, preferences, and emerging needs to enhance customer satisfaction.

  3. Efficiency and profitability: Data-driven insights help companies optimize their operations, streamline processes, and identify cost-saving opportunities. By leveraging data analytics, businesses can improve supply chain management, inventory forecasting, resource allocation, and pricing strategies to increase efficiency and profitability.

  4. Innovation: Data serves as a foundation for innovation by enabling companies to identify emerging trends, develop new products or services, and explore untapped markets. By leveraging data analytics and market intelligence, businesses can gain a competitive edge through innovation and stay ahead of changing customer demands.

  5. Risk management: Data helps businesses identify potential risks and mitigate them effectively. By analyzing historical data and market trends, companies can anticipate risks such as supply chain disruptions, cybersecurity threats, or regulatory changes. This proactive approach enables corporations to develop contingency plans and minimize the impact of unforeseen events.

How Data in the Cloud?

  1. Data marketplaces housed either within the Enterprise Infrastructure or through third-party platforms centralize sources for cleansed, standardized data, which can be easily integrated and accessed by enterprises. Data marketplaces can help enterprises respond more quickly and effectively to changes in customer needs, global markets, and crises such as pandemics or natural disasters.

  2. Cloud-based software is hosted online and typically available on a subscription basis through third-party platforms. Enormous resource benefits exist for Enterprises since Third-party providers manage all potential technical issues, such as data, middleware, servers, and storage, minimizing IT resource expenditures and streamlining maintenance and support functions. For example, enterprises can use cloud-based software to manage customer relationships, optimize supply chain operations, and gain insights into their operations, customers, and market trends.

  3. Cloud security is essential for ensuring the confidentiality, integrity, availability, and privacy of enterprise data in the cloud. Typically provided are a set of policies, technologies, and controls that protect cloud-based systems, applications, data, and infrastructure from cyber threats. Implementing the new SEC regulations and Texas Privacy Act will be Top of mind for 2024, Third-party providers offer various cloud security services such as identity and access management (IAM), encryption, threat detection and response, compliance management, and disaster recovery. Can they help with the new regulations and if so, how?

How Data Protection?

  1. Zero Trust Security Frameworks: Zero Trust, which assumes no one inside or outside an organization can be trusted, will continue to gain traction. This approach emphasizes identity and access management, continuous monitoring, and strict access controls to protect sensitive data.

  2. Privacy Regulations and Compliance: As privacy concerns grow, governments around the world may introduce or update data protection regulations. Organizations will need to invest in compliance strategies to ensure they are adhering to these evolving standards, such as the GDPR (General Data Protection Regulation) along with implementing the New SEC Regulations and Texas Privacy Act.

  3. Cloud Security and Hybrid Environments: With more businesses adopting cloud services and hybrid IT environments, data protection strategies will need to adapt. Securing data in the cloud and ensuring a seamless transition between on-premises and cloud-based systems will be essential.

  4. AI-Driven Threat Detection and Response: Artificial intelligence and machine learning will continue to play a crucial role in data protection. AI-powered tools will become even more sophisticated in identifying and mitigating security threats in real-time.

  5. Data Encryption and Quantum-Safe Cryptography: As quantum computing advances, the potential for breaking current encryption standards increases. Organizations may start adopting quantum-safe cryptographic methods to ensure the long-term security of their data.

Why & Why Not AI?

Why?

  1. Process automation: from paperwork to cybersecurity to maintenance, the consistency and scalability of AI allow for many low-level processes to be fully automated, saving hours of manual work. Automation of basic tasks then leaves space for humans to spend time on the more valuable work that still requires a human touch.

  2. AI-Driven Search: Search functions that are driven by AI give a new perspective to companies looking to provide their customers with a better, more personalized experience. AI-driven search helps companies understand factors such as context or the reasoning behind searched items in addition to trends that their customers are driving. This means that instead of simply knowing what people search for, they get insight into why they search for it, improving customer experience and understanding demand.

  3. Chatbots: Chatbots are an example of process automation that was discussed above, but they are in a category of their own in how AI can help businesses. Using chatbots as a first line point of contact in customer experience and even internal HR processes provides a valuable communication tool for customers and internal stakeholders, automating a large, time-consuming part of business processes.

  4. Data-driven decision making: According to a study by Gartner, by 2030, decision support and augmentation will account for 44% of business value derived from AI, on a global scale. As companies work to make decisions based on data, AI and ML can provide those insights to create truly impressive business strategy.

  5. Data privacy and governance: As the amount of data available to businesses increases, so do concerns of data privacy and governance. Enterprise AI solutions enhance an organizations’ management of large amounts of potentially sensitive data with its ability to sort and classify data efficiently. In response to recent (and future) privacy laws such as the European Union’s General Data Protection Regulation (GDPR), AI can be used to handle data privacy requests from individuals, which require a quick response and create a large administrative burden.

Why Not?

  1. Finding the right talent: Developing and launching a robust Enterprise AI strategy requires the right team. Businesses need data scientists, machine learning engineers, and other AI professionals with strong automation and digitalization skills to execute a new strategy. As a result, demand for these professionals is high, and the war for talent is on.

  2. Getting alignment across the organization: CTOs, and technical decision makers within any organization understand the high importance of adopting AI. However, getting alignment across the organization poses a bigger issue. Many Enterprise AI projects end as individual efforts or only supported by technical departments, as effecting change across an entire organization proves difficult, and time-consuming.

  3. Unrealistic expectations of AI capabilities: Rather than being a quick fix, or a single solution, Enterprise AI efforts must be a part of a larger strategy that incorporates various AI solutions in multiple phases. In many cases, when an AI initiative doesn’t produce immediate results, companies lose interest in pursuing Enterprise AI further.


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