Is your data ready to match the pace of emerging AI use cases? has been saved
Perspectives
Is your data ready to match the pace of emerging AI use cases?
The evolving role of data, from business backbone to AI catalyst
As organizations pivot toward a more digital, data-driven model, the influence of data on decision-making and customer-centricity has become increasingly pronounced. The journey ahead is not merely about amassing vast quantities of data; it's about securing the right data at the precise moment in an accessible format, which is integral to preparation of data and its readiness for artificial intelligence (AI) adoption.
The emergence of AI and the challenges faced
The development of an AI solution is a meticulous process that intertwines technical, strategic, and ethical considerations at each step. AI, at its core, is a sophisticated and multifaceted concept, intricately woven from three fundamental components: business context, technique/algorithm, and data, each having its unique challenges and risks.
These elements play a crucial role in the functioning and impact of AI and have specific risks and challenges that need to be mitigated through an effective set of implemented AI governance requirements.
Setting the right foundation before building defenses
Before diving into the specific stages of this process, it's imperative to understand the foundational aspects that influence its successful execution, from the initial problem definition to the final model deployment.
- Defining the business problem is the first crucial step in creating an AI model. This definition serves as a guide, helping articulate the business requirements the AI model aims to address. However, data availability is a typical challenge for many organizations. Questions can arise around whether the necessary data is available within the organization and, if so, understanding the details of data availability can inform decisions on data collection and processing for AI and data analytics.
- Identifying the appropriate algorithm or technique for the AI solution is crucial once business requirements are established. This step requires considerations such as scalability, interpretability, and computational efficiency, laying the groundwork for subsequent phases of model development.
- Effectively managing data-specific challenges is another critical aspect of the AI model development process. These challenges are increasing in complexity as organizations are exploring possible use cases for AI solutions and achieving consistency in the approach to using AI. Remediation of these challenges directly affects the model's ability to provide meaningful insights and predictions.
AI data readiness: A strategic imperative
What is AI data readiness?
An organization’s preparedness in implementing strategies to help guide effective AI deployment by reasonably determining that its data is available, high quality, properly structured, and aligned with its AI use cases.
Steps to implement the readiness of AI data
Defining data scope represents a critical first stage in the AI journey for financial institutions. This process involves an in-depth assessment of:
- Risk tolerance;
- The fostering of strategic collaborations with use case owners; and
- The identification of key characteristics that clearly articulate the problem the AI model aims to resolve
Note: The AI data readiness (AIDR) assessment can be for a single use case like fraud detection or a broader enterprise AI adoption.
Evaluating data readiness involves a structured process to assess the preparedness of a client’s data landscape across five critical dimensions:
- Data availability
- Data volume and diversity
- Data quality and integrity
- Data governance
- Data ethics and responsibility
Improving AI data readiness is important because high-quality and well-structured data is one of the foundations of successful AI models and algorithms. It involves:
- Identifying key areas of improvement based on data readiness assessment results;
- Determining risk tolerance;
- Identifying which AI data readiness dimensions need to be addressed (via go/no-go workshops);
- Developing improvement plans; and
- Communicating and monitoring progress
In the data-driven world, AI has become a transformative force. Data readiness helps organizations invest in data infrastructure and formulate AI strategies. Addressing data-related concerns is key to harnessing AI's potential, making AI data readiness the foundation for unlocking AI’s wide-ranging applications and turning data into a resource for leveraging AI solutions.
How can we help?
1. AI Data Readiness (AIDR) approach: Deloitte’s AIDR approach is a tool for assessing data readiness in preparation for AI implementation.
2. AIDR Assessment Tool: The Data Readiness Assessment Tool is leveraged to evaluate the current state of an organization’s data environment in the five key dimensions of data readiness.
3. AIDR Assessment Tool outcome: The AIDR Assessment Tool aggregates the responses from each question to show a score for each dimension, rolled up into an aggregate score.
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Vic Katyal | Cory Liepold |
Satish Iyengar | Akiva Ehrlich |
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