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Mar 11 2024
The Future-Proof Data Preparation Checklist for Generative AI Adoption

Data preparation is a critical step in the data analysis workflow and is essential for ensuring the accuracy, reliability, and usability of data for downstream tasks. But as companies continue to struggle with data access and accuracy, and as data volumes multiply, the challenges of data silos and trust become more pronounced.

According to Ventana Research, data teams spend a whopping 69% of their time on data preparation tasks. Data preparation might be the least enjoyable part of their job, but the quality and cleanliness of data directly impacts analytics, insights, and decision-making. This also holds true for generative AI. The quality of your training data impacts the performance of gen AI models for your business.

High-Quality Input Data Leads to Better-Trained Models and Higher-Quality Generated Outputs

Generative AI models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), learn from patterns and structures present in the input data to generate new content. To train models effectively, data must be curated, transformed, and organized into a structured format, free from missing values, missing fields, duplicates, inconsistent formatting, outliers, and biases.

Without a doubt, data preparation tasks are a time-consuming and repetitive process. But, failure to adequately prepare data can result in suboptimal performance, biased outcomes, and ethical, legal, and practical challenges for generative AI applications.

Generative AI models lacking sufficient data preparation may face several challenges and limitations. Here are three major consequences:

Poor Quality Outputs

Generative AI models often require data to be represented in a specific format or encoding in a way that’s suitable for the modeling task. Without proper data preparation, the input data may contain noise, errors, or biases that negatively impact the training process. As a result, generative AI models may produce outputs that are of poor quality, lack realism, or contain artifacts and distortions.

Biased Outputs

Imbalanced datasets in which certain classes or categories are underrepresented, can lead to biased models and poor generalization performance. Data preparation ensures that the training data is free from noise, errors, and biases, which can adversely affect the model’s ability to learn and generate realistic outputs.

Compromised Ethics and Privacy

Generative AI models trained on sensitive or personal data must adhere to strict privacy and ethical guidelines. Data preparation involves anonymizing or de-identifying sensitive information to protect individuals’ privacy and comply with regulatory requirements, such as GDPR or HIPAA.

By following a systematic checklist for data preparation, data scientists can improve model performance, reduce bias, and accelerate the development of generative AI applications. Here are six steps to follow:

  1. Project Goals

  • Clearly outline the objectives and desired outcomes of the generative AI model so you can identify the types of data needed to train the model
  • Understand how the model will be utilized in the business context

  1. Data Collection

  • Determine and gather all potential sources of data relevant to the project
  • Consider structured and unstructured data from internal and external sources
  • Ensure data collection methods comply with relevant regulations and privacy policies (e.g. GDPR)

  1. Data Prep

  • Handle missing values, outliers, and inconsistencies in the data
  • Standardize data formats and units for consistency
  • Perform exploratory data analysis (EDA) to understand the characteristics, distributions, and patterns in the data

  1. Model Selection and Training

  • Choose an appropriate generative AI model architecture based on project requirements and data characteristics (e.g., GANs, VAEs, autoregressive models). Consider pre-trained models or architectures tailored to specific tasks
  • Train the selected model using the prepared dataset
  • Validate model outputs qualitatively and quantitatively. Conduct sensitivity analysis to understand model robustness

  1. Deployment Considerations

  • Prepare the model for deployment in the business environment
  • Optimize model inference speed and resource requirements
  • Implement monitoring mechanisms to track model performance in production

  1. Documentation and Reporting

  • Document all steps taken during data preparation, model development, and evaluation
  • Address concerns related to fairness, transparency, and privacy throughout the project lifecycle
  • Communicate findings and recommendations to stakeholders effectively for full transparency into processes

Data preparation is a critical step for generative AI because it ensures that the input data is of high quality, appropriately represented, and well-suited for training models to generate realistic, meaningful and ethically responsible outputs. By investing time and effort in data preparation, organizations can improve the performance, reliability, and ethical implications of their generative AI applications.

Actian Data Preparation for Gen AI

The Actian Data Platform comes with unified data integration, warehousing and visualization in a single platform. It includes a comprehensive set of capabilities for preprocessing, transformations, enrichment, normalization and serialization of structured, semi-structured and unstructured data such as JSON/XML, delimited files, RDBMS, JDBC/ODBC, HBase, Binary, ORC, ARFF, Parquet and Avro.

At Actian, our mission is to enable data engineers, data scientists and data analysts to work with high-quality, reliable data, no matter where it lives. We believe that when data teams focus on delivering comprehensive and trusted data pipelines, business leaders can truly benefit from groundbreaking technologies, such as gen AI.

The best way for artificial intelligence and machine learning (AI/ML) data teams to get started is with a free trial of the Actian Data Platform. From there, you can load your own data and explore what’s possible within the platform. Alternatively, book a demo to see how Actian can help automate data preparation tasks in a robust, scalable, price-performant way.

Meet our Team at the Gartner Data & Analytics Summit 2024 

Join us for Gartner Data & Analytics Summit 2024, March 11 – 13, in Orlando, FL., where you’ll receive a step-by-step guide on readying your data for Gen AI adoption. Check out our session, “Don’t Fall for the Hype: Prep Your Data for Gen AI” on Thursday, March 12 at 1:10pm at the Dolphin Hotel, Atlantic Hall, Theater 3.

The post The Future-Proof Data Preparation Checklist for Generative AI Adoption appeared first on Actian.


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Author: Dee Radh

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Mar 11 2024
Data Preparation Guide: 6 Steps to Deliver High Quality Gen AI Models

Data preparation is a critical step in the data analysis workflow and is essential for ensuring the accuracy, reliability, and usability of data for downstream tasks. But as companies continue to struggle with data access and accuracy, and as data volumes multiply, the challenges of data silos and trust become more pronounced.

According to Ventana Research, data teams spend a whopping 69% of their time on data preparation tasks. Data preparation might be the least enjoyable part of their job, but the quality and cleanliness of data directly impacts analytics, insights, and decision-making. This also holds true for generative AI. The quality of your training data impacts the performance of gen AI models for your business.

High-Quality Input Data Leads to Better-Trained Models and Higher-Quality Generated Outputs

Generative AI models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), learn from patterns and structures present in the input data to generate new content. To train models effectively, data must be curated, transformed, and organized into a structured format, free from missing values, missing fields, duplicates, inconsistent formatting, outliers, and biases.

Without a doubt, data preparation tasks are a time-consuming and repetitive process. But, failure to adequately prepare data can result in suboptimal performance, biased outcomes, and ethical, legal, and practical challenges for generative AI applications.

Generative AI models lacking sufficient data preparation may face several challenges and limitations. Here are three major consequences:

Poor Quality Outputs

Generative AI models often require data to be represented in a specific format or encoding in a way that’s suitable for the modeling task. Without proper data preparation, the input data may contain noise, errors, or biases that negatively impact the training process. As a result, generative AI models may produce outputs that are of poor quality, lack realism, or contain artifacts and distortions.

Biased Outputs

Imbalanced datasets in which certain classes or categories are underrepresented, can lead to biased models and poor generalization performance. Data preparation ensures that the training data is free from noise, errors, and biases, which can adversely affect the model’s ability to learn and generate realistic outputs.

Compromised Ethics and Privacy

Generative AI models trained on sensitive or personal data must adhere to strict privacy and ethical guidelines. Data preparation involves anonymizing or de-identifying sensitive information to protect individuals’ privacy and comply with regulatory requirements, such as GDPR or HIPAA.

By following a systematic checklist for data preparation, data scientists can improve model performance, reduce bias, and accelerate the development of generative AI applications. Here are six steps to follow:

  1. Project Goals

  • Clearly outline the objectives and desired outcomes of the generative AI model so you can identify the types of data needed to train the model
  • Understand how the model will be utilized in the business context

  1. Data Collection

  • Determine and gather all potential sources of data relevant to the project
  • Consider structured and unstructured data from internal and external sources
  • Ensure data collection methods comply with relevant regulations and privacy policies (e.g. GDPR)

  1. Data Prep

  • Handle missing values, outliers, and inconsistencies in the data
  • Standardize data formats and units for consistency
  • Perform exploratory data analysis (EDA) to understand the characteristics, distributions, and patterns in the data

  1. Model Selection and Training

  • Choose an appropriate generative AI model architecture based on project requirements and data characteristics (e.g., GANs, VAEs, autoregressive models). Consider pre-trained models or architectures tailored to specific tasks
  • Train the selected model using the prepared dataset
  • Validate model outputs qualitatively and quantitatively. Conduct sensitivity analysis to understand model robustness

  1. Deployment Considerations

  • Prepare the model for deployment in the business environment
  • Optimize model inference speed and resource requirements
  • Implement monitoring mechanisms to track model performance in production

  1. Documentation and Reporting

  • Document all steps taken during data preparation, model development, and evaluation
  • Address concerns related to fairness, transparency, and privacy throughout the project lifecycle
  • Communicate findings and recommendations to stakeholders effectively for full transparency into processes

Data preparation is a critical step for generative AI because it ensures that the input data is of high quality, appropriately represented, and well-suited for training models to generate realistic, meaningful and ethically responsible outputs. By investing time and effort in data preparation, organizations can improve the performance, reliability, and ethical implications of their generative AI applications.

Actian Data Preparation for Gen AI

The Actian Data Platform comes with unified data integration, warehousing and visualization in a single platform. It includes a comprehensive set of capabilities for preprocessing, transformations, enrichment, normalization and serialization of structured, semi-structured and unstructured data such as JSON/XML, delimited files, RDBMS, JDBC/ODBC, HBase, Binary, ORC, ARFF, Parquet and Avro.

At Actian, our mission is to enable data engineers, data scientists and data analysts to work with high-quality, reliable data, no matter where it lives. We believe that when data teams focus on delivering comprehensive and trusted data pipelines, business leaders can truly benefit from groundbreaking technologies, such as gen AI.

The best way for artificial intelligence and machine learning (AI/ML) data teams to get started is with a free trial of the Actian Data Platform. From there, you can load your own data and explore what’s possible within the platform. Alternatively, book a demo to see how Actian can help automate data preparation tasks in a robust, scalable, price-performant way.

Meet our Team at the Gartner Data & Analytics Summit 2024 

Join us for Gartner Data & Analytics Summit 2024, March 11 – 13, in Orlando, FL., where you’ll receive a step-by-step guide on readying your data for Gen AI adoption. Check out our session, “Don’t Fall for the Hype: Prep Your Data for Gen AI” on Thursday, March 12 at 1:10pm at the Dolphin Hotel, Atlantic Hall, Theater 3.

The post Data Preparation Guide: 6 Steps to Deliver High Quality Gen AI Models appeared first on Actian.


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Author: Dee Radh

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Feb 15 2024
How to Optimize Data In Any Environment

New demands, supply chain complexity, truly understanding customers, and other challenges have upended the traditional business landscape and forced organizations to rethink their strategies and how they’re using data. Organizations that are truly data-driven have opportunities to gain new market share and grow their competitive advantage. Those that don’t will continue to struggle—and in a worst-case scenario, may not be able to keep their doors open.

Data Is Needed to Drive and Support Use Cases

As organizations face the threat of a recession, geopolitical instability, concerns about inflation, and uncertainty about the economy, they look to data for answers. Data has emerged as a critical asset for any organization striving to intelligently grow their business, avoid costly problems, and position themselves for the future.

As explained in the webinar “Using Data in a Downturn: Building Business Resiliency with Analytics,” successful organizations optimize their data to be proactive in changing markets. The webinar, featuring William McKnight from McKnight Consulting Group, notes that data is needed for a vast range of business uses, such as:

  • Gaining a competitive advantage
  • Increasing market share
  • Developing new products and services
  • Entering new markets
  • Increasing brand recognition and customer loyalty
  • Improving efficiency
  • Enhancing customer service
  • Developing new technologies

McKnight says that when it comes to prioritizing data efforts, you should focus on projects that are easy to do with your current technology set and skill set, those that align with your business priorities, and ones that offer a high return on investment (ROI).

Justifying Data and Analytics Projects During a Downturn

The webinar explains why data and analytics projects are needed during an economic downturn. “Trusted knowledge of an accurate future is undoubtedly the most useful knowledge to have,” McKnight points out. Data and analytics predict that future, giving you the ability to position your company for what’s ahead.

Economic conditions and industry trends can change quickly, which means you need trustworthy data to inform the analytics. When this happens, you can uncover emerging opportunities such as products or features your customers will want or identify areas of risk with enough time to take action.

McKnight explains in the webinar that a higher degree of accuracy in determining your future can have a significant impact on your bottom line. “If you know what’s going to happen, you can either like it and leave it, or you can say, ‘I don’t like that, and here’s what I need to do to tune it,’ and that’s the essence of analytics,” he says.

Applying Data and Analytics to Achieve High-Value Results

Not surprisingly, the more data you make available for analytics, the more precise your analytics will be. As the webinar explains, artificial intelligence (AI) can help with insights. AI enhances analytics, provided the AI has the robust and quality data sets needed to deliver accurate and actionable results. The right approach to data and analytics can help you determine the next best step you can take for your business.

You can also use the insights to drive business value, such as creating loyal customers and repeat buyers, and proactively adjusting your supply chain to stay ahead of changing conditions. McKnight says in the webinar that leading companies are using data and customer analytics to drive ROI in a myriad of ways, such as optimizing:

  • Product placement in stores
  • Product recommendations
  • Content recommendations
  • Product design and offerings
  • Menu items in restaurants

All of these efforts increase sales. Likewise, using data and analytics can drive results across the supply chain. For example, you can use data to optimize inventory and ensure fast delivery times, or incorporate real-time data on customer demand, inventory levels, and transportation logistics to have products when and where they’re needed. Similarly, you can take a data-driven approach to demand forecasting, then optimize product distribution, and improve visibility across the entire supplier network.

Data Best Practices Hold True in Soft Economies

Using data to drive the business and inform decision-making is essential in any economy. During an economic downturn, you may need to shift priorities and decide what projects and initiatives to pursue, and which to pause or discontinue.

To help with these decisions, you can use your data foundation, follow data management best practices, continue to use data virtualization, and ensure you have the ability to access accurate data in real time. A modern data platform is also needed to integrate and leverage all your data.

The Actian Data Platform offers integration as a service, makes data easy to use, gives users confidence in their data, improves data quality, and more. The platform empowers you to go from data source to decision with confidence. You have the ability to better utilize data in an economic downturn, or any other market conditions.

The post How to Optimize Data In Any Environment appeared first on Actian.


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Author: Actian Corporation

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