Data Readiness - The First Step for a Successful AI Project
What is your Data Readiness - Are you AI Ready?
In every circle today, we hear about the fantastic potential and emerging capabilities resulting from AI or Artificial Intelligence. From autonomous vehicles to supply chain automation AI is reshaping several industries and the competitive landscape for many businesses.
One of the most overlooked elements in beginning the investment in artificial intelligence is understanding its readiness.
The quality and size of information is the top consideration when assessing the success of an AI initiative. Many organizations have invested considerable energy and resources in AI initiatives to realize the quality of the underlying data is not sufficient to realize the desired outcome.
Where to begin?
Organizations can take a use case focused approach or a data asset approach to assessing AI readiness. From a use case approach, organizations may wish to identify areas of the organization they want to automate and incorporate AI capabilities. For example, this can include automated customer relationship management, service management, or supply chain management. Isolating the data that is involved with these functions and assessing those repositories is an excellent first step.
Alternatively, the organization can take a data asset approach by reviewing all data sources and deriving potential solutions from those discovery efforts.
Quality Data Sources
As the scope and the number of data sources increase, the difficulty in assessing them grows. Below is a list of items that are critical to a readiness assessment:
Completeness: This includes complete records of information and comprehensive definitions of the data for a standardized understanding.
Randomness: Is the data stored in a structured format that is consistent and centralized?
Repetition: Is the data too repetitive or skewed to create unintended bias?
Quantity: Is there enough data to fulfill the intended use; enough data to inform the desired automated outcome?
Security / Privacy: Is the data secure, and is there consent for using the information within the AI process?
Validation: Is there a process for ensuring the consistent management of the data quality over time. Is there a mechanism to maintain new data creation?
Tools to automate AI Readiness
Given the size of data and the resource time necessary to perform a proper readiness assessment, it is essential to leverage tools to control the cost, quality, and timeliness of the AI readiness assessment. These tools leverage AI and machine learning to assess your organization's specific data and provides insight to all the stakeholders involved in a best-practice approach to AI implementation.
It is essential to report on AI readiness in a readable and actionable format. This allows the entire stakeholder group across operations, finance, sales, and executive leadership to have a shared understanding and ownership of initiatives.
Look for products that enable this democratized view to create alignment. Apption's Datahunter is one such solution that allows organizations to rapidly review data sources for AI readiness and create plans to address shortcomings.
Conclusion
Businesses today need to start to plan and take advantage of the potential that AI brings. A failure to plan now will allow your competitors to gain an unfair advantage. Start with your AI readiness today.
Click on the links to learn more about Datahunter.