In today’s fast-paced and fiercely competitive business landscape, success hinges on the ability to harness the wealth of data available. Those who have mastered this, have gained a significant edge over their counterparts and are now poised to embrace cutting-edge technologies such as AI and ML to further enhance their operations. These forward-thinking organizations have established a solid data foundation, equipped with the necessary tools, technologies, and skilled personnel. This robust infrastructure not only supports their current AI initiatives but also paves the way for advancements in generative AI, predictive analytics, and beyond. However, to many data leaders, these initiatives seem scary because of lack of strategic priorities. DataOps best practices speed up data tasks, aligning IT and business teams for smoother operations. As businesses become more data-driven, especially with AI goals, the spotlight is on efficient data use. Unfortunately, this aspect is often neglected. To succeed in AI endeavors, timely access to well-managed data is critical. Here’s where DataOps is crucial in quickly providing compliant data and fostering collaboration between business and data teams. In essence, adopting DataOps practices ease the path to AI success and rapidly bring valuable business transformations through improved data handling.
What’s Scary in the Absence of DataOps: AI initiatives become complex if they don’t fulfill the expectations of data leaders. What scares them the most is- Siloed data. It prevents data scientists and data analysts from utilizing the right data sets at the right time which results in slower data processing, delays in decision-making and reduced agility in responding to market changes. Consequently, it’s important to ground AI and ML initiatives in realistic expectations, and here DataOps comes into picture! Realistic expectations are key to managing excessive demands effectively. With a clear understanding of what is possible and achievable, you can navigate challenging situations with ease and confidence. DataOps embodies the philosophy of treating data as a product, emphasizing continuous testing, monitoring, and feedback loops to ensure high-quality, reliable data for strategic decision-making.
DataOps Dynamics: Catalyzing AI Evolution Across Industries: When discussing DataOps across different industries, it is essential to establish a connection between data consumers and data creators. This connection promotes collaboration and drives innovation. DataOps best practices ensure seamless data flow by automatically detecting and managing data drift. This, in turn, enhances the quality and speed of end-to-end data pipelines, enabling organizations to deliver analytical solutions quickly. Some crucial industries, like the finance industry, face certain challenges that require an agile mindset, skill sets, and tools to deal with financial data to enhance customer experience. If a financial organization has a customer base of around 20 million and wishes to establish a modern data lab to optimize data and advanced analytics for its business, it would require a deep understanding of its vast customer base. The organization’s primary objective would be to facilitate increased customer interactions by leveraging its financial products. This encompasses a wide range of financial data, including customer transaction information, account details, customer demographics, and interaction records. These data sets require integration into a centralized platform for analytical purposes. To achieve this, a central data repository with a comprehensive strategy is required to achieve its goals by seamlessly integrating various disciplines, including full-stack data engineering and AI. An interdisciplinary team is necessary to foster collaboration among experts from diverse fields.
Generative AI is, without question, the darling of the IT industry. Its ability to answer complex questions, create new output and handle tasks quickly and accurately opens up a new world of applications. The possibilities seem endless. Analysts are bullish on its economic potential. Bloomberg expects the generative AI market to exceed $1.3 trillion by 2032, representing 12 percent of total technology spending. McKinsey has estimated that generative AI will be worth $4.4 trillion in a decade. That’s 4 percent of world GDP. Most organizations are only beginning to experiment with AI. Those that have implemented it primarily use productivity tools such as Microsoft 365 Copilot via the cloud. The development of more customized AI applications will require significant changes to the IT environment. That’s why the hybrid IT model is ideal for AI. Hybrid IT allows organizations to distribute AI workloads among cloud platforms and edge devices, reducing costs, improving performance and increasing security.
The IT Requirements of AI: The deep learning models used for generative AI mimic the activity of the human brain by predicting patterns in data. The model, comprising layers of complex algorithms, is trained by analyzing massive datasets. The result of the training is AI inference, which executes the model. AI remained in the realm of science fiction until computer hardware became powerful enough to handle the computations involved. Graphics processing units (GPUs) are ideal for AI because they have thousands of cores that can handle thousands of threads simultaneously. However, GPUs require up to 15 times as much electricity as traditional CPUs. Until recently, both AI training and inference have largely been constrained to the cloud. However, as AI moves into mainstream adoption, inferencing scales faster than training. The cost of running in the cloud can quickly become unsustainable.
Benefits of Hybrid AI: A hybrid IT environment helps reduce these costs. AI inferencing has already been moving to smartphones, laptops and other edge devices. By taking advantage of these devices, organizations can scale their AI implementations while reducing the strain on cloud environments and accompanying costs. Edge devices are also more energy efficient, enabling organizations to meet their sustainability goals. Moving some AI processing to edge devices can also improve performance and reduce latency. In addition, local processing enables AI to operate even if a device lacks connectivity. Because data isn’t transferred to and from the cloud, security and privacy are improved. AI apps can also be personalized according to each user’s unique needs and characteristics.
Different Hybrid AI Models: AI workloads can be distributed across a hybrid IT environment in several ways. Processing can occur primarily on the edge device, with more compute-intensive tasks offloaded to the cloud. This handoff is seamless to the user. In other models, the cloud and local device share the AI workload. Edge devices can also serve as the “eyes and ears” of the AI application, collecting data and sending it to the cloud for processing. Which model you choose depends on the applications. Digital assistants, generative AI search and productivity tools are best suited for a device-centric model. Augmented reality/virtual reality applications work best in the shared-workload environment, with inference occurring in the cloud and the XR headset rendering the 3-D images. Many IoT applications use the device-sensing model.
Conclusion: A hybrid IT environment is essential to the deployment and use of AI. Whether organizations use SaaS solutions or train an existing AI model using their data, hybrid IT can reduce cost and risk while providing a better user experience. Hybrid IT can also be used for custom AI development — once the model is developed and trained in the cloud, inference occurs on edge devices. Technology has a practice dedicated to the architecture, design and implementation of hybrid IT environments. Let us help you capitalize on the value of hybrid AI.
There’s no question that Software-as-a-Service has revolutionized the IT environment. SaaS gives organizations access to applications that enhance the user experience and deliver advanced capabilities. Cloud-based generative AI tools are the latest examples. However, SaaS also creates security risks. One of the best known is shadow IT, which is associated with data loss, data exposure and a lack of control over user access. Misconfigurations, including excess user permissions and improper security settings, are another big problem. Data breaches and regulatory compliance gaps are also associated with SaaS. These risks are amplified when generative AI is integrated into SaaS platforms. In a recent survey by Snow Software, 23 percent of IT leaders said generative AI was their No. 1 SaaS security concern. Cloud access security brokers (CASBs) help organizations reduce SaaS risks. CASBs sit between the organization and SaaS applications, protecting users and sensitive information. CASBs also help IT teams detect shadow IT applications and ensure that security policies are enforced in the cloud environment.
The Four Pillars of CASBs: CASBs have four equally important pillars. The first is visibility, not just to monitor user activity within authorized cloud applications but also to detect the presence of shadow IT services. IT teams can control access to services and activity permitted within those services at a granular level. The second is compliance. Organizations must be able to protect the privacy of sensitive data and apply data loss prevention policies according to data governance and regulatory compliance requirements. The third is data security, which includes sophisticated functionality such as encryption, tokenization and document fingerprinting. The fourth is threat protection. This involves using threat intelligence, dynamic malware analysis and other capabilities to detect and respond to suspicious activity, insider threats, privileged user threats, session hijacking and compromised accounts. CASBs help ensure that users aren’t spreading malware and other threats, whether intentionally or unintentionally.
CASBs and SASE: CASBs are an essential component of secure access service edge (SASE). SASE is not a product but a network architecture that combines edge security controls with software-defined WAN services. These capabilities are delivered as cloud-based services, shifting the emphasis from network hardware to the cloud. SASE incorporates zero-trust security principles. Every user and device attempting to access IT resources is considered suspect until authenticated and authorized. SASE also moves security controls to the edge of the network, closer to users and devices. This reduces latency and enhances threat protection. At the same time, SASE simplifies policy enforcement, ensuring that security controls are applied uniformly across the environment. CASBs apply these capabilities to cloud services and applications. They complement SASE, providing robust cloud security within the unified SASE framework. Without CASBs, organizations lack the visibility and data protection they need in the cloud.
Finding the Right Solution: Many organizations are missing out on the benefits of CASBs, however. A new report from the Cloud Security Alliance finds that just 21 percent of organizations use CASBs. At the same time, 83 percent say they need to improve cloud security, and half lack the in-house resources to do the job. The survey found that 27 percent of organizations are conducting initial research on CASBs, and 15 percent are evaluating vendors. Those are critical first steps, as not every CASB will be right for a particular environment. Organizations should assess their cloud ecosystem to ensure the CASB they choose protects every application, service and storage repository. The CASB should also detect all shadow IT apps and unmanaged devices, and control access based on user and device behavior. Data loss prevention tools enable the CASB to protect sensitive data without impacting productivity. Technology can leverage our cloud, security and generative AI disciplines to help you select and implement the right CASB. Let our experts show you the intersection of cloud, SASE and SaaS in your environment.
IT automation can significantly reduce costs for organizations by streamlining processes, minimizing errors and increasing efficiency. Gartner says automation can lower operational costs by 30 percent, and Forbes estimates it can save companies nearly $5 million annually. It’s estimated that more than two-thirds of U.S. businesses have fully automated at least one key business process. In doing so, they eliminate many tedious and repetitive manual tasks, which reduces errors and operating costs while boosting productivity and efficiency. Here are five ways automation impacts IT economics.
Time Savings: Automation tools can perform tasks much faster than humans, plus they can work around the clock. That relieves IT professionals of the need to spend countless hours on routine and repetitive tasks, allowing them to focus on more strategic and complex activities. For example, automation makes endpoint management much faster and more efficient. Manual endpoint provisioning typically requires technicians to image each device individually. With automation, a deployment script can be created to install software on multiple machines simultaneously. This has been shown to reduce device setup time by 90 percent and cut the provisioning cycle time by more than 85 percent.
Fewer Mistakes: It’s estimated that up to 90 percent of security breaches and data-loss events result from human error, such as data entry mistakes, misconfigurations, accidental file deletions, policy violations and other missteps and oversights. Fatigue, stress and a lack of training can all be contributing factors. Automated processes are much less prone to mistakes. For example, automating backup processes ensures that data is regularly and consistently backed up, reducing the risk of data loss and downtime. Backups occur on schedule and data is automatically compressed, deduplicated and encrypted. Automation can also be used to test backups and ensure that all data and applications can be recovered.
Cost-Efficient Scalability: A significant benefit of IT automation is the ability to swiftly provision and manage resources such as servers and storage to accommodate growing workloads without a proportional increase in human labor. This scalability allows organizations to meet increased demands without incurring additional costs. For example, auto-scaling in cloud computing environments automatically adjusts the number of virtual machines or containers based on traffic or workload. During a sudden surge in traffic, additional server instances are automatically spun up, ensuring that the system can handle the increased load without manual intervention. Conversely, surplus resources are automatically terminated when traffic decreases, optimizing cost efficiency.
Enhanced Security: According to IBM, it takes companies an average of 197 days to identify a data breach and another 69 days to contain it. Security automation platforms enable far more consistent and timely responses to security incidents. They can ingest vast amounts of threat intelligence and rapidly triage alerts to identify genuine threats. They can also immediately launch countermeasures to contain or eliminate threats without human involvement. For instance, when a potential breach is detected, automation can instantly trigger predefined responses, such as isolating affected systems or blocking malicious IP addresses.
More Consistency: Automation enforces standardization with predefined configurations and settings, ensuring that tasks are executed consistently and minimizing the costs of resolving discrepancies or irregularities. Most IT workloads involve a complex sequence of tasks, and automation ensures that each step is executed in the correct order, reducing inconsistencies resulting from error or oversight. This consistency enhances patch management, software updates, user account provisioning, network configurations and more. IT automation is a proven means of driving down costs while increasing the quality and efficiency of your operations. Contact Technology to learn about the many other ways you can leverage IT automation to improve your bottom line.
