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<h1>Best Roadmap to Learn Generative AI in 2025</h1>
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<p>In the ever-evolving realm of Artificial Intelligence, Generative AI stands as a beacon of innovation, continually pushing the boundaries of creativity and intelligence. As we stride into the promising landscape of 2025, the pursuit of harnessing Generative AI&rsquo;s potential beckons enthusiasts, researchers, and practitioners alike. This article delves into the intricacies of the best roadmap for Generative AI in 2025, charting a course through the dynamic advancements, emerging trends, and transformative applications that define this cutting-edge field.f you&rsquo;re wondering how to learn generative AI, this guide will provide you with a comprehensive generative AI learning path and a detailed generative AI roadmap.</p>
<h2><strong>Generative AI Learning Roadmap:</strong></h2>
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<li><a href="https://www.analyticsvidhya.com/blog/2023/05/from-novice-to-pro-the-epic-journey-of-mastering-generative-ai/#h-generative-ai-learning-roadmap" data-level="2">Generative AI Learning Roadmap:</a></li>
<li><a href="https://www.analyticsvidhya.com/blog/2023/05/from-novice-to-pro-the-epic-journey-of-mastering-generative-ai/#h-roadmap-how-can-i-start-learning-generative-ai" data-level="2">Roadmap: How Can I Start Learning Generative AI?</a>
<ul>
<li><a href="https://www.analyticsvidhya.com/blog/2023/05/from-novice-to-pro-the-epic-journey-of-mastering-generative-ai/#h-1-user" data-level="3">1. User</a></li>
<li><a href="https://www.analyticsvidhya.com/blog/2023/05/from-novice-to-pro-the-epic-journey-of-mastering-generative-ai/#h-2-super-user" data-level="3">2. Super User</a></li>
<li><a href="https://www.analyticsvidhya.com/blog/2023/05/from-novice-to-pro-the-epic-journey-of-mastering-generative-ai/#h-3-developer" data-level="3">3. Developer</a></li>
<li><a href="https://www.analyticsvidhya.com/blog/2023/05/from-novice-to-pro-the-epic-journey-of-mastering-generative-ai/#h-4-researcher" data-level="3">4. Researcher</a></li>
</ul>
</li>
<li><a href="https://www.analyticsvidhya.com/blog/2023/05/from-novice-to-pro-the-epic-journey-of-mastering-generative-ai/#h-conclusion" data-level="2">Conclusion</a></li>
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<h2><strong>Roadmap: How Can I Start Learning Generative AI?</strong></h2>
<p>You can start learning Roadmap for&nbsp;<a href="https://www.analyticsvidhya.com/blog/2024/01/ultimate-list-of-generative-ai-resources/" target="_blank" rel="noreferrer noopener"><strong>Generative AI</strong>&nbsp;</a>through 4 personas: User, Super User, Developer, and Researcher. We will discuss each persona in detail. Before moving ahead, you need to be familiar with terms like Generative AI and Foundation Models.</p>
<ul>
<li>Good understanding of the Generative AI and Foundation models and their infinite use cases.</li>
</ul>
<p>Let&rsquo;s explore different personas now.</p>
<figure><img src="https://cdn.analyticsvidhya.com/wp-content/uploads/2024/10/gen-ai-1.webp" sizes="(max-width: 1050px) 100vw, 1050px" srcset="https://cdn.analyticsvidhya.com/wp-content/uploads/2024/10/gen-ai-1.webp 1050w, https://cdn.analyticsvidhya.com/wp-content/uploads/2024/10/gen-ai-1-300x149.webp 300w, https://cdn.analyticsvidhya.com/wp-content/uploads/2024/10/gen-ai-1-768x380.webp 768w, https://cdn.analyticsvidhya.com/wp-content/uploads/2024/10/gen-ai-1-150x74.webp 150w" alt="generative ai learning path,roadmap for generative ai" width="1050" height="520" /></figure>
<h3><strong>1. User</strong></h3>
<p>There is no better way to learn Generative AI than experiencing it. The first persona is to become a user of the Generative AI tools. Sign up and create an account on any of the Generative AI tools and gain hands-on experience. Get familiar with these Generative AI tools, understand what they are, know their capabilities and features, and experiment with them.</p>
<ul>
<li>Explore&nbsp;<a href="https://www.analyticsvidhya.com/blog/2023/03/everything-you-need-to-know-about-chatgpt/" target="_blank" rel="noreferrer noopener"><strong>ChatGPT</strong></a>,&nbsp;<a href="https://www.analyticsvidhya.com/blog/2023/11/googles-bard-can-now-watch-and-answer-questions-about-youtube-videos/" target="_blank" rel="noreferrer noopener"><strong>BARD</strong></a>,&nbsp;<a href="https://www.analyticsvidhya.com/blog/2023/10/how-to-use-midjourney-ai/" target="_blank" rel="noreferrer noopener"><strong>Midjourney</strong></a>, Dalle 2,&nbsp;<a href="https://www.analyticsvidhya.com/blog/2023/09/image-generation-using-stable-diffusion/" target="_blank" rel="noreferrer noopener"><strong>Stable Diffusion</strong></a>, etc.</li>
</ul>
<p>Now, we know better the pros and cons of Generative AI tools and how they can help us in our work. The next step is to go in deeper and understand how to use it effectively.</p>
<h3><strong>2. Super User</strong></h3>
<p>After gaining hands-on experience with the Generative AI tools, the second step is to improve our knowledge and learn to use the tools better.</p>
<p>Generative AI tools have a lot of potential, which has not yet been explored. We need to learn to apply the right techniques to use them effectively. Most Generative AI tools generate responses based on the natural description known as prompt. Prompt writing is an art. We need to learn about prompt engineering in detail to explore Generative AI to its full potential. Here&rsquo;s what you need to do for it:</p>
<ul>
<li>Learn about&nbsp;<a href="https://www.analyticsvidhya.com/blog/2023/06/what-is-prompt-engineering/" target="_blank" rel="noreferrer noopener"><strong>Prompt Engineering</strong></a>.</li>
<li>Explore the best and most effective prompts for using the Generative AI tools.</li>
<li>Understand the best practices for writing prompts.</li>
</ul>
<h3><strong>3. Developer</strong></h3>
<p>Now, we are comfortable using Generative AI tools effectively. The next phase is learning how these generative AI models work and finetuning these models on our datasets.</p>
<p>You need hands-on experience with machine learning and deep learning to do that. I recommend reviewing the prerequisites below before starting with machine learning and deep learning. Feel free to skip the prerequisites if you are already comfortable.</p>
<h4><strong>Prerequisites</strong></h4>
<ul>
<li>Good understanding of Probability and Statistics concepts.
<ul>
<li><strong>Probability:&nbsp;</strong>Probability, Conditional Probability, Bayes Theorem, etc.</li>
<li><strong>Statistics:</strong>&nbsp;Normal Distribution, Central Limit Theorem, etc.</li>
</ul>
</li>
<li>Good understanding of Linear Algebra concepts like vectors, matrices, and systems of linear equations.</li>
<li>Good knowledge of Calculus concepts like gradients, derivatives, and partial derivatives.</li>
<li>Hands-on experience with programming languages like&nbsp;<a href="https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2/" target="_blank" rel="noreferrer noopener"><strong>Python</strong></a>/<a href="https://www.analyticsvidhya.com/blog/2016/02/complete-tutorial-learn-data-science-scratch/" target="_blank" rel="noreferrer noopener"><strong>R</strong></a>.</li>
</ul>
<h4><strong>3.1 Machine learning</strong></h4>
<ol>
<li>Comfortable with supervised and unsupervised learning algorithms like&nbsp;<a href="https://www.analyticsvidhya.com/blog/2021/10/everything-you-need-to-know-about-linear-regression/" target="_blank" rel="noreferrer noopener"><strong>linear regression</strong></a>,&nbsp;<a href="https://www.analyticsvidhya.com/blog/2021/08/conceptual-understanding-of-logistic-regression-for-data-science-beginners/" target="_blank" rel="noreferrer noopener"><strong>logistic regression</strong></a>,&nbsp;<a href="https://www.analyticsvidhya.com/blog/2021/06/understanding-random-forest/" target="_blank" rel="noreferrer noopener"><strong>random forests</strong></a>,&nbsp;<a href="https://www.analyticsvidhya.com/blog/2019/08/comprehensive-guide-k-means-clustering/" target="_blank" rel="noreferrer noopener"><strong>k means</strong></a>, etc.</li>
<li>Know to build machine learning models on tabular datasets.</li>
</ol>
<h4><strong>3.2 Deep Learning</strong></h4>
<ol>
<li>Good understanding of&nbsp;<a href="https://www.analyticsvidhya.com/blog/2021/12/a-guide-on-deep-learning-from-basics-to-advanced-concepts/" target="_blank" rel="noreferrer noopener"><strong>deep learning</strong></a>&nbsp;architectures like Multi-Layer Perceptron,&nbsp;<a href="https://www.analyticsvidhya.com/blog/2022/03/a-brief-overview-of-recurrent-neural-networks-rnn/" target="_blank" rel="noreferrer noopener"><strong>Recurrent Neural Networks (RNNs)</strong></a>,&nbsp;<a href="https://www.analyticsvidhya.com/blog/2021/03/introduction-to-long-short-term-memory-lstm/" target="_blank" rel="noreferrer noopener"><strong>Long Short Term Memory models (LSTMs)</strong></a>,&nbsp;<a href="https://www.analyticsvidhya.com/blog/2021/03/introduction-to-gated-recurrent-unit-gru/" target="_blank" rel="noreferrer noopener"><strong>Gated Recurrent Units (GRUs)</strong></a>, and&nbsp;<a href="https://www.analyticsvidhya.com/blog/2018/12/guide-convolutional-neural-network-cnn/" target="_blank" rel="noreferrer noopener"><strong>Convolutional Neural Networks (CNNs)</strong></a>.</li>
<li>Have hands-on experience with at least one of the deep learning frameworks like Keras, Tensorflow, Pytorch, or FastAI.</li>
<li>Be able to train deep learning models using any of the deep learning frameworks mentioned above. For example:
<ul>
<li>Train Multi-Layer Perceptron on the tabular datasets.</li>
<li>Build RNNs and CNNs for unstructured data, i.e., text and image.</li>
</ul>
</li>
<li>Knowledge of pretrained models for image data and their different types. For instance, know how to finetune them on the downstream tasks.</li>
<li>Learn about language models and build them with LSTMs/GRUs.</li>
<li>Gain knowledge of Attention Mechanisms and know the limitations of using LSTM for working with longer sequences.</li>
<li>Understand the architectures of Autoencoders and GANs and be able to train these models on our datasets.</li>
</ol>
<h4><strong>3.3 Generative Models for NLP and Computer Vision</strong></h4>
<p>Now, you can customize your generative AI learning path depending on your interest. If you want to learn and build Generative models like ChatGPT, you can choose the generative models for NLP. If you are interested in building models like Midjourney and DALL-E 2, you can select generative models for computer vision.</p>
<h4><strong>3.3.1 Generative Models for NLP</strong></h4>
<p>If you choose&nbsp;<a href="https://www.analyticsvidhya.com/blog/2017/01/ultimate-guide-to-understand-implement-natural-language-processing-codes-in-python/" target="_blank" rel="noreferrer noopener"><strong>NLP</strong></a>&nbsp;as your area of focus, the following learning path will guide you to mastery of Generative Models for NLP.</p>
<ol>
<li>Discover the power of&nbsp;<a href="https://www.analyticsvidhya.com/blog/2023/03/an-introduction-to-large-language-models-llms/" target="_blank" rel="noreferrer noopener"><strong>Large Language Models (LLMs)</strong></a>, the foundation models of Natural Language Processing (NLP).</li>
<li>Learn about popular LLMs like Transformers, BERT, GPT 3.5, PaLM 2, and many more.</li>
<li>Understand how to use Large Language Models (LLMs) for downstream tasks: Finetuning and In-context learning i.e., Zero-shot, one-shot, and few-shot learning.</li>
<li>Uncover the best practices for training LLMs, including challenges, scaling laws, and efficient training mechanisms like parallel and distributed architectures.</li>
<li>Learn how to pretrain LLM on your domain-specific data.</li>
<li>Understand and implement different techniques to fine-tune LLM for downstream tasks.</li>
<li>Learn different optimization techniques to accelerate model finetuning like Adapters, LoRA, QLoRA, etc.</li>
<li>Know LLMops: How do you deploy LLM in production?</li>
<li>Explore cutting-edge models like ChatGPT and BARD and understand their training process, including concepts like Reinforcement Learning from Human Feedback (RLHF), Supervised Fine Tuning, and Prompt Engineering.</li>
<li>Know how to finetune ChatGPT on your dataset.</li>
<li>Get hands-on with LLM frameworks like LangChain, AutoGPT, Vector DBs, etc.</li>
</ol>
<h4><strong>3.3.2 Generative Models for Computer Vision</strong></h4>
<p>If you choose to delve into&nbsp;<a href="https://www.analyticsvidhya.com/blog/2021/06/everything-happening-in-computer-vision-that-you-should-know/" target="_blank" rel="noreferrer noopener"><strong>computer vision</strong>,</a>&nbsp;this learning path will guide you in mastering generative models for computer vision.</p>
<ol>
<li>Learn about foundation models in computer vision: diffusion models and their different types.</li>
<li>Understand how to finetune diffusion models for downstream use cases.</li>
<li>Learn about stable diffusion models, including model architecture and training process.</li>
<li>Learn how to finetune stable diffusion models on your datasets.</li>
<li>Explore Mid Journey, DALLE 2, and any other similar models.</li>
</ol>
<h3><strong>4. Researcher</strong></h3>
<p>The last stage is intended for researchers. To build your career in Generative AI research, you need to understand how to build these generative models from scratch. You should be well-versed in various concepts and techniques to build these generative models.</p>
<p>To be a researcher in NLP, you need to:</p>
<ol>
<li>Learn and implement attention models, Key Query Value (KQV) attention, layer normalization, positional encoding, etc.</li>
<li>Get hands-on experience building your own GPT architecture from scratch.</li>
<li>Understand the workings of reinforcement learning algorithms from basics to advanced.</li>
<li>Learn about Proximal Policy Optimization (PPO).</li>
<li>Implement RLHF from scratch.</li>
<li>Build ChatGPT from scratch</li>
<li>Stay updated with the current trends and research in Generative AI for NLP</li>
</ol>
<p>To continue research in Computer Vision:</p>
<ol>
<li>Build diffusion models from scratch.</li>
<li>Learn how to implement stable diffusion from scratch.</li>
<li>Stay updated with the current trends and research in Generative AI for Computer Vision.</li>
</ol>
<h2><strong>Conclusion</strong></h2>
<p>As we draw the roadmap for mastering Generative AI in 2025 to a close, this journey has illuminated the diverse pathways available to enthusiasts, researchers, and professionals eager to delve into the realms of creativity and intelligence. The personas of User, Super User, Developer, and Researcher serve as guiding lights through this transformative expedition, offering tailored routes for various levels of expertise and aspirations. This comprehensive roadmap for charts a course that aligns with the evolving landscape of artificial intelligence, providing a structured guide for those navigating the exciting intersections of technology and creativity. This engineer roadmap will help guide you to mastery, whether you&rsquo;re a beginner or looking to specialize in a professional role.</p>
<p>Remember, this roadmap is not just a linear path; it&rsquo;s a guidepost offering flexibility, adaptability, and room for exploration. Embrace the challenges, engage in continuous learning, and stay attuned to the evolving trends in Generative AI. As 2025 unfolds, this roadmap stands as a compass, guiding you toward mastering the art and science of Generative AI, unveiling new vistas of creativity and innovation in the year ahead.</p>

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In the ever-evolving realm of Artificial Intelligence, Generative AI stands as a beacon of innovation, continually pushing the boundaries of creativity and intelligence. As we stride into the promising landscape of 2025, the pursuit of harnessing Generative AI&rsquo;s potential beckons enthusiasts, researchers, and practitioners alike.

Generative AI Roadmap

<h1>Generative AI use cases with growth potential</h1>
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<p><strong>What you&rsquo;ll learn in this article:&nbsp;</strong></p>
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<li>Discover how&nbsp;Generative AI&nbsp;is poised to add up to $4.4 trillion to the global economy annually and why you should care.&nbsp;</li>
<li>Learn about the transformative applications of Generative AI that enhance productivity, customer experiences, and research and development.</li>
<li>Dive into real-world examples, from Pepsi&rsquo;s personalized marketing campaigns to DoorDash&rsquo;s AI-driven customer service solutions.</li>
</ul>
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<p>Regardless of our personal opinions on&nbsp;<a href="/what-we-do/generative-ai">Generative AI</a>, there&rsquo;s no denying its influence.&nbsp;McKinsey&nbsp;estimates that it has the potential to contribute between $2.6 trillion to $4.4 trillion annually across 63 use cases. This is an impressive figure when you consider that the GDP of the United Kingdom is $3.1 trillion.&nbsp;</p>
<p>What&rsquo;s in it for you? Any business could benefit from capturing a microscopic fraction of GenAI&rsquo;s potential. For instance, if you could channel .001% of the estimated $3 trillion, you&rsquo;d experience an influx of $30 million. While it&rsquo;s hard to predict the exact financial gains from GenAI, most businesses would welcome even a small part of the pie. And potential profit isn&rsquo;t the only reason Generative AI matters &mdash;&nbsp;consider a few more ways its changing the business landscape.&nbsp;</p>
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<h2>Reasons why you should use generative AI</h2>
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<p>Imagine a runner being offered shoes that are scientifically proven to make them 30% faster. And to make matters more urgent, the competition is already using them. If the runner decides not to use the shoes, they&rsquo;d be at a major disadvantage. While shoes like this don&rsquo;t exist, this analogy illustrates the&nbsp;<a href="/insights/generative-ai-roadmap">impact of GenAI on modern businesses</a>. Those who ignore it stand to lose out on its many benefits.</p>
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<li><strong>Innovation:</strong>&nbsp;Generative AI can speed up creativity by automating idea generation across various industries. In the pharmaceuticals field, for example, GenAI can help model and predict the effects of new drugs before they&rsquo;re produced, leveraging techniques like the &ldquo;lab in a loop&rdquo; strategy.</li>
<li><strong>Enhance productivity and scalability:&nbsp;</strong>GenAI automates routine content generation tasks like report writing, summarizing data, and generating code. It also enables users to quickly scale tasks that typically require creative human input without a proportional increase in resources.</li>
<li><strong>Research and development:</strong>&nbsp;GenAI speeds up data analysis by analyzing vast amounts of data to extract patterns and predict outcomes. It can also simulate scenarios in seconds&mdash;like traffic patterns in urban planning or molecular structures in materials science&mdash;helping researchers and engineers find practical solutions.</li>
<li><strong>Accessibility:&nbsp;</strong>GenAI can help make technology more accessible to people with disabilities, for example, by generating real-time descriptive audio for videos.</li>
</ol>
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<h3>Explore ScriptStory&rsquo;s Generative AI Services</h3>
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<div><a href="/what-we-do/generative-ai">Learn more</a></div>
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<h2>Top four ways businesses use Generative AI</h2>
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<div><img src="https://www.nerdery.com/wp-content/uploads/2024/05/Businesses-Use-Generative-AI-image.jpg" sizes="(max-width: 1000px) 100vw, 1000px" srcset="https://www.nerdery.com/wp-content/uploads/2024/05/Businesses-Use-Generative-AI-image.jpg 1000w, https://www.nerdery.com/wp-content/uploads/2024/05/Businesses-Use-Generative-AI-image-300x194.jpg 300w, https://www.nerdery.com/wp-content/uploads/2024/05/Businesses-Use-Generative-AI-image-768x496.jpg 768w" alt="Generative AI artwork" width="1000" height="646" /></div>
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<h3>By integrating GenAI into their core strategies, companies can streamline processes while opening up new avenues for growth and customer engagement. The practical implementation of these technologies can improve a business&rsquo;s ability to compete and thrive in a digital-first world. Consider these four common use cases.</h3>
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<h3>1. Personalization</h3>
<p><strong>Application:</strong>&nbsp;Enhance&nbsp;<a href="https://www.nerdery.com/user-experience-design-services/">user experience</a>&nbsp;and engagement by tailoring digital interactions to individual user preferences and behaviors.</p>
<p><strong>Real-world example:&nbsp;</strong>In 2021, Pepsi collaborated with Synthesia on its&nbsp;Messi Messages&nbsp;campaign. They used GenAI technology to create a staggering 650 million customized video variations, delivered in eight languages, that appeared to be coming from Messi himself. The results were compelling: 1.43 messages per second were delivered in the first 24 hours, and the average time on site during that period was 3.53 minutes.&nbsp;</p>
<p><strong>How it benefits businesses:</strong><strong><br /></strong></p>
<ul>
<li><strong>E-Commerce:&nbsp;</strong>GenAI can help online retailers increase sales by generating personalized product recommendations, tailored advertisements, and customized promotions based on shopper&rsquo;s preferences, boosting conversion rates and customer loyalty.</li>
</ul>
<p><strong>Content platforms:</strong>&nbsp;Streaming services and content platforms can deploy GenAI to curate personalized content lists that adapt to viewing or reading histories, keeping users engaged for longer.</p>
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<h3>2. Gen AI Chatbot</h3>
<p><strong>Application:</strong>&nbsp;GenAI chatbots can conduct conversations with customers, provide information, and resolve issues through text or voice-based interfaces.&nbsp;</p>
<p><strong>Real-world example:&nbsp;</strong>DoorDash&nbsp;noticed user frustration with its outdated interactive voice response (IVR) system. In response, it partnered with CloudCookies to develop an AI-driven chatbot that would reinvent its self-service systems. This collaboration resulted in the full automation of eight voice self-service solutions, bringing over $2 million in annual savings. Moreover, it helped DoorDash identify the top 18 most common customer issues, delivering an additional annual savings of over $3 million.&nbsp;</p>
<p><strong>How it benefits businesses:</strong><strong><br /></strong></p>
<ul>
<li><strong>Cutting-edge tech:&nbsp;</strong>Popular implementations use Dialogflow and AI Agent Builder in conjunction with Google&rsquo;s VertexAI to generate real-time responses.&nbsp;</li>
<li><strong>Google Cloud Platform:</strong>&nbsp;From VertexAI and Google&rsquo;s LLM technologies to Google Gemini, users have industry-leading tools to build upon.</li>
<li><strong>Scalability:</strong>&nbsp;AI chatbots can handle thousands of interactions simultaneously, ensuring customers receive instant responses, which is crucial during peak traffic.</li>
<li><strong>Cost efficiency:</strong>&nbsp;Automating customer interaction reduces the need for large customer service teams, cutting operational costs while maintaining high service quality.</li>
<li><strong>24/7 service:</strong>&nbsp;Unlike human agents, AI chatbots can operate around the clock, improving customer experience by providing constant access to support.</li>
</ul>
<p><strong>Data collection:</strong>&nbsp;Each interaction with a chatbot is an opportunity to collect data, which can be analyzed to improve services, tailor marketing strategies, and better understand customer needs.</p>
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<h3>3. Document Discovery (Summarization)</h3>
<p><strong>Application:</strong>&nbsp;GenAI can automatically summarize long documents, extracting relevant information and presenting it in a condensed form.</p>
<p><strong>Real-world example:&nbsp;</strong>It&rsquo;s no secret that the legal system is overflowing with paperwork, which is why GenAI is a perfect match for law firms. Gordon Calhoun, a partner at Lewis Brisbois, talks about how their collaboration with&nbsp;Everlaw&nbsp;and the use of Generative AI benefit their business. One statistic he shared is how GenAI helps slash document analysis by 30-50%, speeding up the time to deposition. Another benefit is getting younger associates out of documents and into more exciting and more productive legal activities.&nbsp;</p>
<p><strong>How it benefits businesses:</strong><strong><br /></strong></p>
<ul>
<li><strong>Knowledge management efficiency:</strong>&nbsp;In industries where large volumes of documentation are common, such as law and academia, AI summarization helps professionals quickly organize and locate important information.</li>
<li><strong>Accuracy and relevance:</strong>&nbsp;Training AI models to recognize and prioritize information that is most relevant to specific queries or needs reduces human error in information retrieval.</li>
</ul>
<ul>
<li><strong>Enterprise toolsets:</strong>&nbsp;With tools like GCP enterprise search users can summarize documents at scale and merge documents with structured databases.&nbsp;</li>
<li><strong>Beyond data analysis:</strong>&nbsp;AI-powered software like VertexAI and Google Gemini can generate insights, predictions, and content based on your operational data.</li>
</ul>
</div>
</div>
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<h3>4. Content Generation</h3>
<p><strong>Application:&nbsp;</strong>Generative AI can autonomously produce diverse types of content, including written articles, marketing copy, graphics, videos, and even music, tailored to specific audiences or purposes.</p>
<p><strong>Real-world example:</strong>&nbsp;Lionbridge, a global translation company, ran an experiment to prove the value of GenAI-driven content optimization using four generative AI tools, including Amazon, ChatGPT, Google Bard, and Meta&rsquo;s Llama. They found that most of the platforms could produce usable content when keeping a human reviewer in the loop. Their experiment also concluded that GenAI-powered content generation should be a critical element in any optimization project.&nbsp;</p>
<p><strong>How It Benefits Businesses</strong>:</p>
<ul>
<li><strong>Scalability:&nbsp;</strong>Using VertexAI, GCP users can generate text summaries and other natural language interfaces to drive widespread adoption of LLM-based applications.</li>
<li><strong>Marketing and advertising</strong>: Businesses can use tools like Google Gemini to quickly generate high-quality, creative content for marketing campaigns, social media posts, and advertisements.</li>
<li><strong>Cost efficiency</strong>: Reducing the reliance on human writers and designers for every piece of content can significantly cut costs, especially for businesses that require large quantities of content.</li>
<li><strong>Personalized experience</strong>: AI can customize content to match the preferences and behaviors of individual users. For instance, e-commerce sites can leverage GenAI to automatically create personalized product descriptions.</li>
</ul>
</div>
</div>
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<div>
<h4><a href="/insights/generative-ai-roadmap">Learn how to get started in our our recent article, Getting Started with Generative AI.</a></h4>
</div>
</div>
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<div>
<h3>What are your plans for Generative AI?</h3>
</div>
</div>
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<div>
<p>Generative AI offers transformative benefits across a myriad of applications. Its capabilities are not only reshaping industries but also setting a new standard for operational efficiency and market competition. Companies that embrace this technology can expect substantial growth, improved customer satisfaction, and a significant competitive edge.&nbsp;</p>
<p>As Generative AI continues to evolve, its integration into business strategies is becoming essential for those looking to thrive in a rapidly changing digital landscape.&nbsp;</p>
<p>Are you interested in learning how you could use GenAI to amplify performance and cost savings? Reach out to one of our Nerds to learn more.</p>
</div>
</div>
</div>
</div>

Learn about the transformative applications of Generative AI that enhance productivity, customer experiences, and research and development. Dive into real-world examples, from personalized marketing campaigns to AI-driven customer service solutions

Generative Use Cases Worth Its Potential

<h1><strong>Designing and Building BI Dashboards: Leading Practices</strong></h1>
<p><strong>Business Intelligence (BI) dashboards</strong> enable companies to gain deeper insights into their data by presenting statistics in chart- and graph-based formats that are easy to understand. While data visualization is a core element, these dashboards also streamline reporting and communication. A well-organized dashboard will be simpler to interpret, helping users better grasp and act on the information it provides.</p>
<p>Below, we&rsquo;ll outline best practices for designing a BI dashboard that is clean, concise, and visually appealing.</p>
<h2><strong>What Is a Business Intelligence (BI) Dashboard?</strong></h2>
<p>A <strong>BI dashboard</strong> is a visualization tool that presents key business metrics in one centralized location. By consolidating data from a variety of sources, it helps organizations monitor, track, and analyze essential performance indicators. Which metrics you track will depend on your organization&rsquo;s specific objectives.</p>
<h2><strong>Why Build a BI Dashboard?</strong></h2>
<p>BI dashboards are incredibly useful for those monitoring critical metrics like sales performance, marketing initiatives, lead generation, and more. The best BI platforms make it possible for anyone to create a dashboard, even without deep expertise in data analytics or Excel.</p>
<p>Because dashboards display only the most current information in a single, user-friendly view, users can quickly spot trends and take action. Gone is the need to sift through separate tools or generate multiple reports&mdash;the relevant information is right there, ready to be explored.</p>
<h2><strong>What to Track in Your BI Dashboard</strong></h2>
<p>Each organization will have unique data requirements. Before building a dashboard, first clarify your <strong>business goals</strong> or the <strong>key question</strong> you&rsquo;re trying to answer. That way, you&rsquo;ll focus on gathering the metrics that truly matter rather than cluttering your dashboard with unnecessary figures.</p>
<h2><strong>BI Dashboard Best Practices</strong></h2>
<h3><strong>1. Know Your &ldquo;Why&rdquo;</strong></h3>
<p>Start by identifying your main objectives or challenges. Which data sources are crucial for finding the answers you need? Which metrics do you need to monitor closely to address your current business questions? By defining these requirements early, you can keep the dashboard focused, intuitive, and free of clutter.</p>
<h3><strong>2. Know Your Audience</strong></h3>
<p>Determine who will be using the dashboard and what their goals are. Ask:</p>
<ul>
<li>Who will interact with this dashboard?</li>
<li>What objectives or metrics do they care about?</li>
<li>How frequently will they consult the dashboard (daily, weekly, monthly)?</li>
</ul>
<p>Tailor the design, data, and structure to your audience&rsquo;s specific needs and cadence.</p>
<h3><strong>3. Determine Your Data Sources</strong></h3>
<p>Gather data from internal and external systems, but only the sources relevant to your dashboard&rsquo;s goals. Adding unrelated data can create visual clutter and confusion. Also ensure that your sources provide accurate, high-quality data. Utilize data connectors or integrators to automate updates, ensuring real-time accuracy.</p>
<h3><strong>4. Work from the Top Down</strong></h3>
<p>To help new users quickly find essential information, place the most important metrics at the top&mdash;ideally in the upper-left corner. This &ldquo;inverted pyramid&rdquo; structure ensures critical data is immediately visible. Middle sections can offer supporting details, while the bottom or right side can handle background information or less critical data.</p>
<h3><strong>5. Make Your Charts Easy to Understand</strong></h3>
<p>Use consistent formatting, clear labels, and simple colors. Some key tips:</p>
<ul>
<li>Clearly label each section so users know what they&rsquo;re viewing.</li>
<li>Use size, color, and spacing to organize dashboard elements.</li>
<li>Avoid visual clutter&mdash;less is often more.</li>
<li>Keep the most important information in the upper left.</li>
<li>Maintain consistent alignment and leverage white space to keep the interface balanced.</li>
<li>Present an overview first, and allow users to drill down for deeper insights.</li>
</ul>
<h3><strong>6. Consider Screen Sizes</strong></h3>
<p>Be mindful of how users will access the dashboard&mdash;on desktops, tablets, or smartphones. Limited screen space on mobile devices means you&rsquo;ll need to prioritize which information appears first. Ensure buttons, filters, and interactive elements are easy to tap or click, and that everything scales for different screen sizes.</p>
<h3><strong>7. Allow Users to Interact</strong></h3>
<p>Offer training sessions so users can learn how to navigate the dashboard. Provide interactive filters, such as:</p>
<ul>
<li><strong>Drill down</strong>: Display more granular data in a hierarchical view as needed.</li>
<li><strong>Drill through</strong>: Show pop-up windows with extra context or details.</li>
<li><strong>Click-to-filter</strong>: Filter the entire dashboard by clicking on a specific chart or metric.</li>
</ul>
<p>These features keep the dashboard tidy while allowing users to explore information at their own pace.</p>
<h3><strong>8. Include Historical Data</strong></h3>
<p>Incorporating <strong>historical data</strong> helps users track trends over time. Make sure to label axes, columns, rows, and legends to provide context&mdash;this helps viewers understand whether metrics are moving in the right direction.</p>
<h3><strong>9. Enable White Labeling</strong></h3>
<p>Customization is key for many users. Let teams adapt the dashboard to match their brand or department, including fonts, colors, and logos. A consistent theme can make the dashboard feel more familiar and aligned with organizational branding.</p>
<h2><strong>Conclusion</strong></h2>
<p>A BI dashboard can be a powerful tool for collecting and communicating critical information. By leveraging best practices&mdash;focusing on your goals, understanding your audience, integrating the right data sources, and keeping the interface organized&mdash;you&rsquo;ll create a dashboard that is both effective and easy to use.</p>
<p>Multiple tools are available for building such dashboards, from simple spreadsheets to advanced BI platforms. With a well-designed BI dashboard, your organization can make more informed decisions, improve collaboration, and respond to data in real time&mdash;ultimately driving better business outcomes.</p>

dashboard design.avif

Business Intelligence (BI) dashboards enable companies to gain deeper insights into their data by presenting statistics in chart- and graph-based formats that are easy to understand. While data visualization is a core element, these dashboards also streamline reporting and communication

Data Dashboard Design Top Considerations

<div class="row mt-4">
            <div class="col-md-12 col-xl-9 col-lg-9">
              <p class="insight-detail-para">
                <strong>
                  Your digital platform should be working for you, not against
                  you. Outdated technology can slow down your team, stifle
                  innovation and hinder your growth. This article explores the 5
                  signs that┬áyour organization is ready for digital platform
                  modernization.
                </strong>
              </p>
              <p class="insight-detail-para">
                By modernizing your digital platform, you create more engaging
                customer experiences, improve operational efficiency, enhance
                security, and gain a competitive advantage in the marketplace.
                You also start to create cohesive technologies ÔÇö instead of more
                overhead ÔÇö ensuring improvements truly amplify business
                performance.
                <br /> <br />
                Before diving into when itÔÇÖs time to modernize, letÔÇÖs first ask
                a fundamental question: What is a digital platform, and how does
                it impact modern business operations?
              </p>

              <h2 class="insight-detail-heading">
                What is a digital platform?
              </h2>
              <p class="insight-detail-para">
                A digital platform is a central hub that integrates your
                businessÔÇÖ software and technology into a single, unified system
                ÔÇö allowing all the pieces of your business to work together
                seamlessly.
              </p>
              <p class="insight-detail-para">
                <strong>This includes: </strong>
              </p>
              <ol>
                <li>
                  
                  <strong>Customer interactions: </strong> Your website, mobile
                  app, online store, social media channels are all part of your
                  digital platform, allowing customers to engage with your
                  brand.
                </li>
                <li>
                  
                  <strong>Business operations:</strong> Behind the scenes, your
                  platform can manage things like order processing, inventory,
                  customer data, and internal communications.
                </li>
                <li>
                  
                  <strong>Data and analytics:</strong>A modern digital platform
                  provides valuable insights into customer behavior, market
                  trends, and overall performance.
                </li>
              </ol>
              <p class="insight-detail-para">
                Modernizing your digital platforms isnÔÇÖt just about keeping up ÔÇô
                itÔÇÖs about getting ahead. By building a strong foundation
                through infrastructure modernization, you open the door to
                exciting new technologies like Generative AI, Machine Learning,
                and Advanced Analytics. But where do you begin? The first step
                is recognizing the need for change.
              </p>
              <div class="orange-bg-detail mb-4">
                <p class="insight-detail-bgpara text-white">
                  For a platform to be truly effective, it must evolve to
                  provide robust, scalable solutions for essential tasks,
                  thereby empowering teams to concentrate on higher-level,
                  strategic jobs.
                </p>
              </div>
              <h2 class="insight-detail-heading">
                5 signs it's time for platform modernization
              </h2>
              <p class="insight-detail-para mb-4">
                A digital platform is a central hub that integrates your
                businessÔÇÖ software and technology into a single, unified system
                ÔÇö allowing all the pieces of your business to work together
                seamlessly.
              </p>
              <img
                src="../assets/images/detail-end-image.png"
                class="img-fluid detail-scnd-image mb-4"
              />
              <h2 class="insight-detail-heading mb-4">
                1. User expectations outpace your platform
              </h2>
              <p class="insight-detail-para mb-4">
                Adapting rapidly and adopting new approaches to meet
                organizational and product goals is central to digital platform
                engineering. If your teams canÔÇÖt implement new technologies and
                approaches fast enough, they wonÔÇÖt be able to keep pace with
                user expectations or competitor innovation. This lag can result
                in outdated features, slower performance, and a lack of
                innovation, leading to user dissatisfaction and attrition.
                <br />
                <br />
                To stay competitive, your digital platforms must empower teams
                to experiment, iterate, and deploy new solutions quickly. This
                adaptability does more than meet current user needs; it also
                anticipates future demands, ensuring sustained user engagement
                and loyalty.
              </p>

              <h2 class="insight-detail-heading mb-4">
                2. Basic problems aren't being solved
              </h2>
              <p class="insight-detail-para mb-4">
                Expecting a digital platform to solve every unique challenge
                your teams face is unrealistic, but it should effectively
                address common issues. If your teams frequently resort to
                individual solutions for basic problems like caching, load
                balancing, or scaling, your platformÔÇÖs existing solutions may be
                lacking.
                <br />
                <br />
                This gap can lead to inefficiencies, increased costs, and slowed
                progress as teams spend valuable time and resources on
                workarounds instead of focusing on innovation. For a platform to
                be truly effective, it must evolve to provide robust, scalable
                solutions for essential tasks, thereby empowering teams to
                concentrate on higher-level, strategic jobs.
              </p>

              <h2 class="insight-detail-heading mb-4">
                3. Development cycles are slowing down
              </h2>
              <p class="insight-detail-para mb-4">
                Slower development cycles indicate a lack of support for
                downstream teams. Long dependency chains and unoptimized
                toolchains create pipeline bottlenecks, delaying progress and
                frustrating development teams. These inefficiencies extend cycle
                times and hinder swift response to market demands and user
                feedback.
                <br />
                <br />
                The first step to counter rising development times is to analyze
                each stage in your development workflow. By examining your
                development stream, you can identify bottlenecks and take the
                appropriate steps to ensure your teams can improve your Devops
                to deliver high-quality products faster and more effectively.
              </p>
            </div>
            <div class=" col-md-12 col-xl-3 col-lg-3">
              <div class="side-detail-box">
                <div class="upper-main-box">
                  <h2 class="text-white side-box-heading mb-0">Related Articles </h2>
                </div>
                <div class="lower-main-box">
                  <h2 class="side-que mb-0">What Is a Digital Product?</h2>
                  <a href="#" class="btn-detail">Learn More</a>
                  <h2 class="side-que mb-0">Platform Modernization </h2>
                  <a href="#" class="btn-detail">Learn More</a>
                  <h2 class="side-que mb-0">How Manufacturers Create Value with Data? </h2>
                  <a href="#" class="btn-detail">Learn More</a>
                </div></div>

              <div class="side-detail-box mt-3">
                <div class="upper-main-box">
                  <h2 class="text-white side-box-heading mb-0">Tags </h2>
                </div>
                <div class="lower-main-box">
                  <h2 class="side-tag ">Digital Platform</h2>

                  <h2 class="side-tag ">Digital Platform Engineering</h2>

                  <h2 class="side-tag ">Modernization </h2>

                </div></div>

            </div>
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<div>
<h1>Data Lake vs. Data Warehouse vs. Data Lakehouse: Understanding the Differences</h1>
<p>Data lakes, data warehouses, and data lakehouses are three of the most common data storage options. Educate yourself to make the best choice for you and your business.</p>
</div>
<div>
<div>
<div>
<div>
<div>
<div>Browse by category
<div>
<ul>
<li><a href="#what-is-a-data-lake">What is a data lake?</a></li>
<li><a href="#what-is-a-data-warehouse">What is a data warehouse?</a></li>
<li><a href="#what-is-a-data-lakehouse">What is a data lakehouse?</a></li>
<li><a href="#comparing-data-lakes-vs-data-warehouses-vs-data-lakehouses">Comparing data lakes vs. data warehouses vs. data lakehouses</a></li>
<li><a href="#which-data-storage-option-is-best-for-you">Which data storage option is best for you?</a></li>
<li><a href="#incorporate-scriptstory-into-your-data-stack">Incorporate ScriptStory into your data stack</a></li>
</ul>
</div>
</div>
</div>
<p>Think about the myriad of tools people in your company use to do their jobs.</p>
<p>You and your colleagues depend on product management software marketing tools and countless other software solutions daily. Most of those tools generate valuable data. Your customers also generate data when they use your product, visit your website, or perform other trackable actions</p>
<p>What should you do with all that data? You can store the information to extract insights, and there are a couple of options for how and where to do it. Data lakes, data warehouses, and data lakehouses are three of the most common data storage options.</p>
<p>Each has advantages and disadvantages, and the best choice depends on data volumes, the level of scalability you need, and other factors.</p>
<div>
<div>Key takeaways
<div>
<ul>
<li>Many companies choose a data lake, data warehouse, or data lakehouse to store data they want to analyze and use to inform business decisions.</li>
<li>Data lakes store large volumes of structured, semi-structured, and unstructured data. Data warehouses are more organized and designed to store structured data. Data lakehouses offer a hybrid approach.</li>
<li>The best data storage solution for your company depends on various factors, including data type and format, performance requirements, and data volume.</li>
</ul>
</div>
</div>
</div>
<h2>What is a data lake?</h2>
<p>Data lakes store large volumes of data in its native format, whether structured, semi-structured, or unstructured. They deliver the most value when they&rsquo;re implemented alongside other infrastructure that supports machine learning , predictive analytics and other big initiatives.</p>
<p>The three main benefits of data lakes are scalability, cost-effectiveness, and flexibility. They reliably handle petabytes of data, which is a massive volume&mdash;one petabyte equals one million gigabytes. This enables you to scale storage up and down as needed affordably. Since data lakes store all kinds of data, they can also adapt to ever-changing business requirements.</p>
<p>Two potential downsides to using a data lake are data governance issues and performance.</p>
<p>Data lakes store many different types of data, which can lead to data integrity issues. When using a data lake, it is important to follow data governance best practices to ensure data accuracy, consistency, and overall data quality.</p>
<p>The sheer volume of data in a lake can also be problematic&mdash;if data is poorly organized, redundant, or siloed, querying speeds and overall performance also suffer.</p>
<p><strong>When to use a data lake:&nbsp;</strong>Data lakes are most frequently used in streaming, machine learning, and data science scenarios. For example, a company could use a data lake to store and analyze user interaction data, including viewing habits, preferences, and engagement metrics.</p>
<h2>What is a data warehouse?</h2>
<p>Think of a data warehouse as a well-organized storage room.Data warehouses store structured data from a variety of different sources. Data is stored in a relational structure, meaning that data inside the warehouse is neatly organized into rows, columns, and tables.</p>
<p>Data undergoes a process called data ingestion before getting stored in a data warehouse. Data ingestion involves collecting, processing, and preparing data for storage. Here&rsquo;s how it works:</p>
<ol>
<li>Extract data from various sources.</li>
<li>Transform the data by cleaning, processing, and converting it into the desired format.</li>
<li>Load the newly transformed data into your data warehouse.</li>
</ol>
<p>The data ingestion process enhances data quality and consistency. The benefits of data warehouses include streamlined data processing practices, improved data analysis and reporting abilities. Many of the most popular data warehouses also integrate with various business intelligence tools, making it easier to use data to drive decision-making.</p>
<p>Data warehouses aren&rsquo;t without their downsides. Implementing and maintaining a data warehouse can be costly. Ingesting and integrating data from various sources is sometimes complex and time consuming, especially when you&rsquo;re dealing with different data formats and structures. Additionally, handling unstructured data sometimes requires additional preprocessing, leading to potential delays in data processing.</p>
<p><strong>When to use a data warehouse:&nbsp;</strong>Data warehouses are well suited for analyzing structured data for business intelligence and reporting purposes. For example, ecommerce company could use a data warehouse to store sales and marketing-related data about acquisition channels, purchases, and campaign performance.</p>
<h2>What is a data lakehouse?</h2>
<p>Data lakehouses combine features of data lakes and data warehouses. Like a data lake, they offer a unified storage platform for diverse data types. However, they also offer powerful data processing and analytics capabilities like a data warehouse.</p>
<p>Essentially, data lakehouses use the data structure and principles found in data warehousing and combine them with a data lake's flexibility and low-cost storage.</p>
<p>Lakehouses address some common challenges associated with data lakes, including data governance and the potential for slower retrieval speeds.</p>
<p>On the governance side, data lakehouses include elements of data warehousing, like consistent data structures and validation controls. These tactics help maintain data integrity. Since the data has some structure and organization, data retrieval and querying are also faster.</p>
<p>The main disadvantage of a data lakehouse is the complexity. Implementing and managing a data lakehouse involves a high degree of technical expertise, even more than implementing and managing a data lake or warehouse.</p>
<p><strong>When to use a data lakehouse:&nbsp;</strong>Many companies use a data lakehouse to simultaneously execute multiple data processing operations, such as retrieval, storage, and analysis. For example, a healthcare company could use a data lakehouse to store patient records, real-time sensor data, and clinical trial data.</p>
<h2>Comparing data lakes vs. data warehouses vs. data lakehouses</h2>
<p>Now that you understand the basics of each data storage solution, it&rsquo;s time to look at how each one handles two key areas:</p>
<ul>
<li><strong>Data structure and schema</strong>&nbsp;refer to how data is organized and stored within the system. Schema defines the organization and structure of the data tself, including its format and any rules and limits applied to the data fields.</li>
<li><strong>Querying and performance</strong>&nbsp;encompass how the storage system processes and retrieves data. This includes how quickly and efficiently the system executes queries, generates reports, and performs various analytical tasks.</li>
</ul>
<p>Data lakes use a schema-on-read approach for data structure. That means the structure and requirements for organizing data are applied when it&rsquo;s being read, not when it&rsquo;s initially stored. A schema-on-read approach gives you more flexibility when handling diverse and unstructured data.</p>
<p>Data warehouses use a schema-on-write approach, meaning data must adhere to a predefined structure when it&rsquo;s loaded into the warehouse. This provides data consistency and enables for more efficient data processing and analysis.</p>
<p>Data lakehouses strike a balance between structured and unstructured data needs. They enable you to store unstructured data and then apply structure when it&rsquo;s retrieved. Lakehouses also support schema evolution by enabling changes to data structures as business needs evolve, like adding new data formats and modifying existing formats.</p>
<p>Not surprisingly, how a data storage platform handles data structure and schema directly impacts speed and performance.</p>
<p>While data lakes excel at handling massive volumes of diverse data, the system interprets the structure of your data every time it's accessed. This can lead to processing delays, a problem for companies needing real-time analytics capabilities.</p>
<p>Data warehouses boast faster querying response speeds thanks to their data's well-organized and highly structured nature. But there&rsquo;s a cost to this upside&mdash;you&rsquo;ll spend more time preparing data, which limits how quickly you can load data into your warehouse.</p>
<p>Data lakehouses are the best of both worlds. Like a data lake, they support various ingestion types, which results in quick loading speeds. They also incorporate structured data organization similar to a warehouse without the same degree of rigidity. It&rsquo;s a combination that delivers fast query response times plus efficient processing and analysis.</p>
<h2>Which data storage option is best for you?</h2>
<p>Here are some general guidelines when selecting between a data lake, data warehouse, and data lakehouse:</p>
<ul>
<li><strong>Use a data lake&nbsp;</strong>for raw, unstructured data, like server logs and other system-generated data.</li>
<li><strong>Use a data warehouse</strong>&nbsp;to analyze structured, historical data, including customer information, transaction records, and inventory data.</li>
<li><strong>Use a data lakehouse</strong>&nbsp;to collect and analyze various data types and formats.</li>
</ul>
<p>Data storage is a complicated topic. It&rsquo;s not uncommon for a company to use several types of data storage for different purposes. Ultimately, the best choice for your company will depend on scalability, performance, and other technical considerations.</p>
<h2>Incorporate ScriptStory into your data stack</h2>
<p>Data storage and management is a considerable part of your overall data infrastructure. Even if you aren&rsquo;t responsible for managing that infrastructure, understanding the basics will increase your data literacy and help you make better data-driven decisions.</p>
<p>Whether you choose a data lake, data warehouse, or data lakehouse, storage is just one part of the modern data stack that supports data analytics at your company. Different analytics tools can enable data collection, analysis, and reporting.</p>
<p>ScriptStory digital analytics platform fits into any existing data environment and gives you insights into the full customer journey. Use it to optimize customer acquisition, improve retention, and increase your bottom line.</p>
</div>
</div>
</div>
</div>

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Data lakes, data warehouses, and data lakehouses are three of the most common data storage options. In this article, we will explore the details around the nuances in all three ways to store data

Data Lake vs Data Warehouses vs Data Lakehouse

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