Maximizing business value with AI-driven data strategies
août 31, 2023 / Christina Mongan
Short on time? Read the key takeaways.
- Data is the most important ingredient of AI, with a well-structured data strategy empowering data-driven decision-making.
- Thoroughly understanding data and its ecosystem is crucial for using internal, synthetic and external datasets to their fullest potential.
- Aligning AI strategy with the organization's overall business strategy and objectives makes it easier to identify business opportunities and revenue streams.
- Start small with an AI project that is manageable and achievable. Then, experiment, iterate and continuously assess the performance of AI models to improve your approach.
Investing in the cloud opens new possibilities with AI but tapping into this potential demands access to high-quality data.
In the modern enterprise, data isn’t just an asset; it’s the key to success, simplifying the path for organizations to achieve business outcomes. A diet of diverse, rich data is necessary to nourish and hone AI models to run, train and integrate them effectively. AI models synthesize examples from this data to generate new insights and drive business outcomes. In fact, the cloud has paved the way for AI, with data serving as its engine.
When fed diverse examples, the AI models become adept at analyzing, learning and identifying patterns and relationships. These patterns and relationships then form the basis for the AI model to make predictions or decisions about what will happen with new data inputs. Without data, the smooth functioning of AI becomes an impossibility.
This move toward a data-centric approach positions data as much more than an operational asset like factories, equipment, IP and real estate. It emerges as the key to unlocking business insights. When handled properly, data can drive growth. Your first step? Start by recognizing data’s inherent value and rescue your legacy data from silos.
Need help turning data into successful business outcomes? After aligning your data strategy with your organization’s overall business strategy, adopt these strategies to make the most of your data.
Adopt a data-driven culture and mindset
When an enterprise successfully builds a data-driven culture, it empowers data-driven decision-making and continuous improvement. A data-driven culture is only fully realized when data analytics skills are common across roles in your organization and not exclusive to the data team. Leverage storytelling to ensure a seamless translation between the science of data and the art of business when championing best practices and sharing news of business wins.
Treat data as an asset
High-quality data is essential for successful AI implementation. By treating data as an asset, organizations can unlock new opportunities, improve operational efficiency and drive innovation. Treating data as an asset means supporting various types of data and knowing where to source it, how to store it and how to ensure its accuracy and integrity.
Your data strategy should be both defensive and offensive, emphasizing governance, regulatory compliance and data control, security, privacy, integrity and quality.
Gain insights from all your data
All too often, data is scattered across the enterprise, trapped in siloed systems and inaccessible to AI models. Acquiring a comprehensive understanding and command of all your data within and outside your organization is crucial for using internal, synthetic and external datasets to their fullest potential. By 2025, Gartner predicts the use of synthetic data will reduce the volume of real data needed for machine learning by 70%. Synthetic data is data artificially created from AI and not data collected from real-world observation.
Organizations often have more data than they can reasonably manage. That’s where AI comes in. As your strategic partner, AI is revolutionizing the way organizations analyze it all for real, actionable insights.
Your data ecosystem should include a flexible data architecture, which lets you access data wherever it is and across various formats, from real-time streaming to unstructured. One example is how a pharmaceutical company running decentralized clinical trials could use the Internet of Medical Things (IoMT), including wearables, to capture patient data remotely and bring together unstructured researcher notes, synthetic data and real-world data to streamline clinical trials. This could accomplish business objectives like streamlining these trials and improving the patient experience.
Make data accessible, actionable and outcomes driven
Organizations that embrace data-driven decision-making and leverage AI as a strategic asset, drive faster, more informed decisions, foster innovation and gain a competitive edge in the ever-evolving business landscape.
Providing self-service access to a common data layer for reliable, consistent and high-quality information enables organizations to use their data actively in decision-making. AI can be integrated as a consumer of self-service data, allowing it to access and use relevant data for every user across the enterprise and beyond.
Interfaces, such as mobile devices, also can be implemented when necessary. This allows end-users to easily interact with the AI to push derived insights to frontline workers and other team members. As a result, you can quickly adapt when market trends change or unpredictable events occur, such as a global pandemic or economic downturn.
To validate your AI models and create value from data, you must have access to high-quality, relevant and diverse data.
Align business and AI strategy
Align AI strategy with your overall business strategy to ensure that AI initiatives support the organization's objectives and contribute to its success. A well-designed AI strategy helps an organization identify new business opportunities and revenue streams.
Take these steps as part of your strategy:
- Consider the ethical, organizational, leadership, cultural and legal implications of AI strategy implementation.
- Create a roadmap for developing, deploying and scaling AI applications, as well as addressing the ethical and legal implications of AI.
- Set up change management for a successful implementation.
- Identify use cases – consider starting with analytic use cases and then evolve to more advanced analytics such as predictive analytics and machine learning models.
Extend your strategy to include more emerging technologies such as generative AI, which allows a more diverse set of employees to engage and use AI. They can ask more dynamic questions and get answers in a human-centered and conversational way without needing special tech knowledge.
Moving through these steps may happen faster than you think because cloud hyperscalers, like AWS and Microsoft, are making generative AI available to both large and small businesses through the cloud.
Some providers provide platforms, like Open AI Studio from Microsoft Azure, that enable organizations to finetune their generative AI models for their enterprise and industry. They can then deploy across their own apps – for example, by adapting it to perform a specific task such as content generation, summarization, semantic search, and natural language to code translation.
How to advance your AI strategy
Ready to use AI to turn your data into a driver for business outcomes? Follow these strategies:
Start small, experiment and iterate
Begin with an AI project that is manageable and achievable. Iterate as you go, testing your AI models and refining them based on the results. Using real-time feedback, adaptive AI continuously retrains models and adapts quickly to changing real-world circumstances based on new data and adjusted goals.
Plan for implementation and ongoing monitoring
Evaluate your AI systems and continuously assess the performance of your AI models so you can make improvements, as needed. Monitor and evaluate the effectiveness of your AI models regularly and use the data to make informed decisions about further development.
Remember, getting started with AI is a journey, not a destination. It takes time, effort and resources to develop effective AI models and integrate them into your business processes. However, by taking a strategic approach and building on your successes, you can realize the benefits of AI and gain a competitive advantage in your industry.
Prioritize transparency
Prioritize transparency and explainability in your AI applications, ensuring that the models are transparent and the decisions they make are understandable and explainable. By taking a responsible and ethical approach to AI, ensure that your AI applications comply with legal standards, such as privacy and data security regulations, and follow Responsible AI principles.
Get more from your data
To learn more about the valuable data and AI connection, explore these points of view: Cloud-Powered AI and Data-Driven Transformation. And to see how Unisys can support your efforts, check out data and analytics solutions and artificial intelligence pages.
