October 06, 2023

Cloud infrastructure spending is growing

Although I love to be right about the strong cloud spending, that does not mean it’s suitable for all enterprises. Indeed, the trend will be to overspend, even after net-new finops deployments that closely monitor where the dollars are spent. We must focus on accountability, automation, and discipline around allocating and paying for cloud resources. I suspect many cloud deployments are hugely underoptimized and need a tune-up. Even though some of this shared infrastructure spending is unavoidable, CIOs need to review how the spending occurs and look for opportunities to save dollars without reducing the value generated by these systems. I suggest companies consider all other options, such as bringing some processing into enterprise data centers. Those prices have been falling while they have been stable or rising on the public cloud side. Also, many systems function in isolation and don’t benefit much from existing within a public cloud. Simple storage is one example, and many enterprises are putting those systems on-premises these days.

What is a business analyst? A key role for business-IT efficiency

BAs are responsible for creating new models that support business decisions by working closely with finance and IT teams to establish initiatives and strategies aimed at improving revenue and/or optimizing costs. Business analysts need a “strong understanding of regulatory and reporting requirements as well as plenty of experience in forecasting, budgeting, and financial analysis combined with understanding of key performance indicators,” according to Robert Half Technology. ... Business analysts need to know how to pull, analyze and report data trends, share that information with others, and apply it to business goals and needs. Not all business analysts need a background in IT if they have a general understanding of how systems, products, and tools work. Alternatively, some business analysts have a strong IT background and less experience in business but are interested in shifting away from IT into this hybrid role. The role often acts as a communicator between the business and IT sides of the organization, so having extensive experience in either area can be beneficial for business analysts.

AI Needs Data More Than Data Needs AI

While data plays a foundational role in AI, the reverse is not true. Data doesn't inherently need AI to exist or be valuable. Data, in various forms, has been collected and analyzed for centuries without the need for sophisticated AI algorithms. Data on its own can provide valuable insights and inform decision-making processes. Therefore, organizations should not blindly chase the AI hype at the cost of ignoring the importance of data management and data quality. The role of AI is to take the computation and insights of good quality data to the next level and not necessarily attempt to fix the decades-old data management processes. ... While AI relies heavily on data for its operation and evolution, data can benefit from AI in several ways. Data Management: AI can help automate data management tasks, making it easier to process, clean and organize large datasets. Predictive Insights: AI can uncover patterns and insights in data that may not be immediately apparent to humans, enhancing the value of the data.

Enterprises see AI as a worthwhile investment

Despite prior industry research indicating that 90% of AI initiatives fail to produce substantial ROI and roughly half never leave the prototype stage, the overwhelming majority of respondents to this survey (92%) find business value from their models in production and 66% feel their models have delivered results that are outstanding or exceed expectations. Common use cases for AI among these leading-edge organizations include personalizing the customer experience, fraud detection, optimizing sales and marketing and improving real-time decision making. Their success of this group offers a basic roadmap that other organizations should consider when developing their own best practices, including: Approach: A majority of responding organizations have a robust, defined approach and a dedicated team for monitoring ML models in production. In fact among larger enterprises, 71% have at least 100 people working in ML while over half have more than 250.?

5 Strategies for Cloud Security in Health Care

Adopting data security in the cloud doesn’t mean merely uploading patient data to S3 and enabling encryption. There are many security controls that need to be in place before a single patient record is migrated. For instance, there is particular concern about data security on medical devices and wireless body area networks (devices that are embedded in a patient’s body). Obviously, it’s vital to secure such devices from exploits. When running services on the cloud, you should review all relevant data privacy considerations and encryption controls, including data encryption, public-key encryption, identity-based encryption, identity-based broadcast encryption and attribute-based encryption. Then adopt a framework for achieving secure and controlled identity access using federation (like OpenID Connect, which is not the same as OpenID, or SAML). Finally, you should ensure that monitoring and audit controls are in place to maintain confidentiality. You should also have an incident response plan in place to handle crisis scenarios in the event of an incident.?

Financial Institutions Turn to AI and Cloud to Solve Data Challenges

In data management, the potential uses of GenAI, powered by large language models, has been recognised by many financial institutions, including State Street. For instance, it can help in the cross-mapping of datasets, the classifying of data and more generalist applications such as summarising reports and responding to plain English inquiries. ... The Alpha platform uses GenAI with Snowflake as a strategic partner providing the data foundation of the platform. Snowflake’s cloud-native architecture streamlines data sharing and governance, enables faster time to market for data-centric applications, and offers a rich environment of AI and machine learning-based capabilities for data scientists, quants and engineers. “Every few years, the technology landscape re-sets, creating a small window of opportunity that in turn enables a giant leap in innovation; GenAI is the opportunity that will define the new set of industry leaders over the next decade,” State Street Executive Vice President and Chief Architect Aman Thind tells A-Team Group.

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