Data is the lifeblood of modern enterprises. Yet while three-quarters of businesses have experienced an increase in the demand for data, 70% are gathering data faster than they can analyse and use it. This underutilised data incurs high storage costs and impacts an organisation’s carbon footprint, while failing to deliver value. Therefore, the primary challenge is to activate this data and extract actionable insights.
That means organisations need to develop strategies that unlock the potential of their data, turning it into a competitive advantage. This involves leveraging advanced analytics, machine learning, and AI to uncover hidden patterns, predict future trends, and make informed decisions.
Dell Technologies was joined recently by our partners CDW for a session at DTX Manchester, to discuss the key factors organisations must consider, when rolling out their AI innovation.
The Foundation: Getting Data Right
A critical aspect of leveraging AI is ensuring data quality from the outset. Gartner finds that poor data quality costs organisations an average of $12.9 million.
The journey from data input to activation involves several stages. The focus should be on placing the right workloads in the right locations to deliver maximum business value. By tailoring AI solutions to specific use cases, organisations can address their unique challenges and drive meaningful outcomes.
Edge computing plays a crucial role in this process. By processing data closer to its source, edge computing reduces latency and enhances real-time decision-making. For instance, in a Formula 1 race, edge computing can analyse telemetry data in real-time, providing teams with insights to optimise performance. Similarly, in retail, edge computing can enhance customer experiences through personalised recommendations and efficient inventory management.
The Pareto Principle makes the argument that 80% of outcomes come from 20% of causes. Dell and our partners, such as CDW, take this and apply it to data, working with our customers to identify the 20% of data that yields 80% of the value. This involves prototyping and exploring data to pinpoint high-value segments. By doing so, organisations can focus on data that drives significant business outcomes, avoiding the pitfalls of attempting to move or process all data monolithically.
Prototyping involves creating small-scale models of AI solutions to test their feasibility and effectiveness. This iterative process helps identify potential issues, refine algorithms, and ensure the solution aligns with business objectives. By prioritising valuable data, organisations can concentrate their efforts on areas that offer the greatest return on investment, maximising the impact of their AI initiatives.
Cyber resilience: withstand and survive
Data protection is crucial in an AI-driven landscape. Cyber resilience, according to CDW, involves two key aspects: withstanding attacks and recovering from them. This dual focus on resilience helps to safeguard data integrity and business continuity.
To withstand, organisations must look to shrink their technology landscape to reduce attack surfaces and ensure that critical data remains isolated and accessible during breaches. This includes deploying firewalls, zero trust infrastructure, and encryption technologies to protect sensitive data.
Recovery, on the other hand, involves developing contingency plans to restore operations quickly after an attack. This includes regular backups, disaster recovery drills, and establishing incident response teams.
Implementing AI use cases
Successful AI projects require a substantial amount of high-quality data. However, many organisations approach AI without adequately defining use cases or ensuring data quality. Dell and CDW emphasise the importance of aligning AI projects with business strategies and demonstrating initial value to secure further investment.
The application of AI varies across sectors. In retail, AI-powered computer vision technology can help businesses to reduce shrinkage, automate warehouse operations, and seamlessly manage and maintain store technology with minimal downtime. In manufacturing, it can optimise production processes by monitoring equipment performance and predicting maintenance needs.
Governance and ethics also play crucial roles, as legal and reputational concerns can stall AI initiatives. Organisations must balance innovation with ethical considerations to maintain trust and momentum.
To overcome these challenges, organisations should adopt a phased approach to AI implementation. This involves starting with pilot projects to test the feasibility and impact of AI solutions. By demonstrating tangible benefits, organisations can build confidence and secure buy-in from stakeholders. Additionally, it is important to establish clear governance frameworks to ensure AI initiatives align with ethical standards and regulatory requirements.
Pillars of success
Ultimately, the success of leveraging AI for data-driven innovation relies on three key factors:
- Defining business outcomes: Aligning stakeholders and ensuring everyone is on the same page is vital for a successful AI journey. Collaboration between partners and internal teams is crucial. This involves setting clear objectives, defining success metrics, and maintaining open communication throughout the project lifecycle. By aligning AI initiatives with business goals, organisations can ensure they deliver meaningful value.
- Understanding use cases: Engaging experts at an early stage, like Dell Technologies and our partners, CDW, Microsoft and Intel, helps to identify valuable data and ensures high-quality inputs, leading to accurate and beneficial AI outputs. This requires a deep understanding of industry-specific challenges and opportunities. By involving domain experts and data scientists, organisations can develop targeted AI solutions that address their unique needs. This collaborative approach enhances the likelihood of success and accelerates the time to value.
- Run your workloads in the right places: Deploying AI technology efficiently across data centres, cloud, and edge environments is essential. CDW’s complex service capabilities facilitate rapid technology implementation, allowing businesses to focus on deriving value from AI. This involves leveraging scalable cloud platforms, optimising data pipelines, and deploying AI models at the edge to enable real-time insights. By streamlining technology activation and ensuring, organisations can accelerate their AI journey and achieve faster results.
To understand the right use cases for your business and how AI can accelerate your innovation, get in touch with CDW’s Dell Business unit to organise a free accelerator workshop by contacting dellbu@uk.cdw.com.
