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Aug 28 2024
How Data is Revolutionizing Transportation and Logistics

In today’s fast-paced world, the transportation and logistics industry is the backbone that keeps the global economy moving. Logistics is expected to be the fastest-growing industry by 2030. As demand for faster, more efficient, and cost-effective services grows, you’ll need to be able to connect, manage, and analyze data from all parts of your business to make fast, efficient decisions that improve your supply chain, logistics, and other critical areas.  

Siloed data, poor data quality, and a lack of integration across systems can hinder you from optimizing your operations, forecasting demand accurately, and providing top-tier customer service. By leveraging advanced data integration, management, and analytics, you can transform these challenges into opportunities, driving efficiency, reliability, and customer satisfaction. 

The Challenges: Harnessing Data in Transportation and Logistics 

One of the most significant hurdles in the transportation and logistics sector is accessing quality data across departments. Data is often scattered across multiple systems—such as customer relationship management (CRM), enterprise resource planning (ERP), telematics systems, and even spreadsheets—without a unified access point. This fragmentation creates data silos, where crucial information is isolated across individuals and business units, making it difficult for different departments to access the data they need. For instance, the logistics team might not have access to customer data stored in the CRM, which can hinder their ability to accurately plan deliveries, personalize service, proactively address potential issues, and improve overall communication.   

Furthermore, the lack of integration across these systems exacerbates the problem of fragmented data. Different data sources often store information in varied and incompatible formats, making it challenging to compare or combine data across systems. This leads to inefficiencies in several critical areas, including demand forecasting, route optimization, predictive maintenance, and risk management. Without a unified view of operations, companies struggle to leverage customer behavior insights from CRM data to improve service quality or optimize delivery schedules, and face other limitations.  

The Impact: Inefficiencies and Operational Risks 

The consequences of these data challenges are far-reaching. Inaccurate demand forecasts can lead to stockouts, overstock, and poor resource allocation, all of which directly impact your bottom line. Without cohesive predictive maintenance, operational downtime increases, negatively impacting delivery schedules and customer satisfaction. Inefficient routing, caused by disparate data sources, results in higher fuel costs and delayed deliveries, further eroding profitability and customer trust. 

Additionally, the lack of a unified customer view can hinder your ability to provide personalized services, reducing customer satisfaction and loyalty. In the absence of integrated data, risk management becomes reactive rather than proactive, with delayed data processing increasing exposure to risks and limiting your ability to respond quickly to emerging threats. 

The Solution: A Unified Data Platform 

Imagine a scenario where your transportation and logistics operations are no longer bogged down by data fragmentation and poor integration. With a unified view across your entire organization, you can access accurate, real-time insights across the end-to-end supply chain, enabling youto make data-driven decisions that reduce delays and improve overall efficiency. 

A unified data platform integrates fragmented data from multiple sources into a single, accessible system. This integration eliminates data silos, ensuring that all relevant information—whether from CRM, ERP, telematics, or GPS tracking systems—is available in real-time to decision-makers across your organization.

For example, predictive maintenance becomes significantly more effective when historical data, sensor data, and telematics are integrated and analyzed consistently. This approach minimizes unplanned downtime, extends the lifespan of assets, and ensures that vehicles and equipment are always operating at peak efficiency, leading to substantial cost savings.  

Similarly, advanced route optimization algorithms that utilize real-time traffic data, weather conditions, and historical delivery performance can dynamically adjust routes for drivers. The result is consistently on-time deliveries, reduced fuel costs, and enhanced customer satisfaction through reliable and efficient service. 

A unified data platform also enables the creation of a 360-degree customer view by consolidating customer data from various touchpoints—such as transactions, behaviors, and support interactions—into a comprehensive and up-to-date profile. This holistic view allows you to offer personalized services and targeted marketing, leading to higher customer satisfaction, increased loyalty, and more successful sales strategies. 

Proactive risk management is another critical benefit of a unified data platform. By analyzing real-time data from multiple sources, you can identify potential risks before they escalate into critical issues. Whether you’re experiencing supply chain disruptions, regulatory compliance challenges, or logistical issues, the ability to respond swiftly to emerging risks reduces potential losses and ensures smooth operations, even in the face of unforeseen challenges. 

Face the Future of Transportation and Logistics With Confidence  

As the transportation and logistics industry continues to evolve, the role of data will only become more critical. The Actian Data Platform can help you overcome the current challenges of data fragmentation, poor quality, and lack of integration in addition to helping you position yourself at the forefront of innovation in the industry. By leveraging data to optimize operations, improve customer service, and proactively manage risks, you will achieve greater efficiency, cost-effectiveness, and customer satisfaction—driving greater success in a competitive and dynamic market.

The post How Data is Revolutionizing Transportation and Logistics appeared first on Actian.


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Author: Kasey Nolan

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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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