Why Data Preparedness is Essential for AI Readiness
In today’s rapidly evolving business landscape, Artificial Intelligence is poised to reshape our businesses and society for generations to come.
For Business owners and officers who are committed to running data-driven operations, the natural question is ‘how can my business benefit from AI?’
We’ll dedicate future blogs to the incremental path that a business can take in the adoption of AI but here we’ll focus on the data preparation required to get the most out of AI.
The goal of all of this is that your AI offers you the best counsel about the decisions to make for your business for growth.
Data Preparedness: The Key to gaining a Competitive Advantage
Benchmarking and Macro-Economic AI counsel is readily available to your business right now and FreshBI’s AI visual for Power BI is and example of this and is one way that you can prompt AI directly from your Power BI Dashboards.
The more difficult aspect of gaining a competitive advantage from AI is in training AI with your Enterprise-Specific Data. Achieving this would enable your business to benefit from AI counsel that is specific to your unique business and consequently the decisions required to grow your business
Structured vs Unstructured Data for Training AI
Structured Data is organized and easily searchable and would require this approach to Data Preparation:
Data Collection: Gather data from databases, spreadsheets, or APIs.
Data Cleaning: Handle missing values, remove duplicates, and correct errors.
Data Transformation: Normalize, scale, or encode data as needed.
Feature Engineering: Create new features from existing data to enhance model performance.
Models for Unstructured Data (e.g., deep learning models) are typically more complex and computationally intensive than those for structured data and would require this approach to Data Preparation:
Data Collection: Gather data from diverse sources such as web scraping, APIs, or media files.
Data Cleaning: Handle noise, irrelevant information, and inconsistencies.
Data Preprocessing:
Text Data: Tokenization, stemming, lemmatization, and stop-word removal.
Image Data: Resizing, normalization, and augmentation.
Audio Data: Feature extraction (e.g., MFCCs), normalization, and noise reduction.
Annotation: Labeling data for supervised learning tasks.
Preparing Your Data Platform for AI
In this blog we’ll focus on the preparation of Structured Data as the surest way to attain a conversational style with your AI that serves the specific needs of your organization. Taking the Structured Data approach gives you control over the tribal language that your AI is trained on. Having a common language and common business terminology between your Data Platform, your AI and all departments and stakeholders is essential to crisp communication and to building trust in the results.
The good news is that if your organization already has Data Governance practices in place then your may be more than half way to having AI ready Enterprise Data.
These are the steps required to attain data-readiness for training AI on your Enterprise Data:
Data Collection:
Identify and gather relevant data sources from various departments.
Ensure data is collected in a structured and standardized format.
Data Quality Assessment:
Check for missing values, inconsistencies, and errors.
Evaluate the accuracy, completeness, and relevance of the data.
Identify and eliminate duplicate data.
Data Cleaning:
Handle missing values through imputation or removal.
Correct data entry errors and standardize data formats.
Remove outliers or errors that may skew analysis.
Data Integration:
Combine data from different sources into a cohesive dataset.
Ensure data from different sources is compatible and aligned.
Data Transformation:
Normalize or scale numerical data.
Encode categorical data using techniques such as one-hot encoding.
Aggregate data to different levels if necessary for analysis.
Data Annotation:
Label data accurately for supervised learning tasks.
Use tools and techniques for data annotation and labeling.
Data Segmentation:
Split data into training, validation, and test sets.
Ensure representative samples in each set to avoid bias.
Data Augmentation:
Generate new data samples to increase dataset size, especially for image or text data.
Apply techniques like rotation, flipping, or scaling for image data augmentation.
Data Privacy and Security:
Ensure data compliance with privacy regulations (e.g., GDPR).
Anonymize sensitive data to protect user privacy.
Implement robust data security measures to protect against breaches.
Data Documentation:
Document the data sources, cleaning processes, and transformations.
Maintain metadata for future reference and reproducibility.
Feature Engineering:
Create new features from existing data to improve model performance.
Select relevant features based on domain knowledge and statistical analysis.
Automate Data Processes:
Use ETL (Extract, Transform, Load) tools for automation.
Implement data pipelines for continuous data integration and cleaning.
Data Governance:
Establish data governance policies to ensure data quality and integrity.
Define roles and responsibilities for data management within the organization.
Model Evaluation and Monitoring:
Continuously evaluate the performance of AI models using the prepared data.
Monitor models in production to ensure they perform as expected and update them as needed.
To support this data quality initiative, it's essential to have the right talent in place. Bring in AI engineers, data scientists, and ethics specialists who understand the intricacies of data management, bias mitigation, and ethical AI practices. Invest in building a strong team capable of making critical decisions and guiding your organization towards successful AI implementation.
Conclusion
Data Preparedness is one small part of the journey that you’ll take your organization on as you continue to transform into a data-driven machine and gain a competitive advantage using your AI to incorporate your Enterprise Data in its counsel to you.
The advantage of making the investment into preparing your Enterprise data for AI is that this exercise serves not only the training of AI when you’re ready to do so but it also has these benefits that you can receive an ROI on right away:
Getting clear on what your standardized business language and measurements are.
Finding and removing noise in your data means that you are forced to do the same for your business workflows and thereby refine and your business workflows and actualize more efficient operations.
Even without AI trained and activated, your prepared data will be ready to serve your Data Analytics needs across the organization, offering you all the benefits of a data savvy organization.
Prepared data offers your organization the benefits of Data Governance, not least of all that your data becomes a recognized asset to your organization rather than just a by-product of activity.
Remember that the goal of this investment is to own trusted data and to have good counsel and both your data and your AI counsel should honor your unique business culture, language and be anchored in the things that are most important to your business - your unique drivers of success.
To achieve this you can run initiatives in parallel. For example, even before your data is fully prepared, you can still have access to meaningful analytics and beautiful dashboards. Also, AI training on macro models is available to you today for benchmarking and macro impacts on your business.
At FreshBI we have many ideas on how organizations can benefit from AI and to take the most agile approach possible for you means that we run initiatives in parallel through rapid prototyping. And we make sure that while the disruptive process of innovation roars, we ensure that your business has usable, releasable and innovative solutions to your ambitions to remaining data-driven and win.