<h1>How to build a holistic AI/ML strategy for analytics</h1>
<h2>What is AI and machine learning? And what does it mean for analytics?</h2>
<p>AI is now a boardroom priority, and it’s never been more critical for analytics leaders to get ahead of the curve by building a comprehensive AI/ML strategy for analytics.  In this post, we’ll dive into your opportunities and delve into Artificial Intelligence (AI) and Machine Learning (ML) examples around analytics so you can lead your organization in delivering new insights and experiences to your users.</p>
<p>Let’s start by cutting through the hype and clarifying definitions. AI is the field of computing that enables algorithms to perform tasks that would typically require human intelligence, including speech recognition, decision-making, problem-solving, language translation, and more. AI systems aim to simulate human intelligence and exhibit traits like learning, reasoning, and self-correction. Generative AI (or GenAI), popularized by OpenAI, is a type of AI capable of producing or generating new content such as text, images, audio, or video.</p>
<p>Machine Learning is often used interchangeably, but it’s a subset of AI that focuses on using algorithms and statistical models to enable computer systems to learn and improve from experience without being explicitly programmed automatically. Machine Learning algorithms can learn from data and make predictions or actions based on the patterns and insights they discover.</p>
<h3>AI/ML analytics defined</h3>
<p>Advances in AI and ML have significant implications for analytics leaders. And the advent of GenAI dramatically increases the stakes. By clearly understanding AI/ML use cases, you can revolutionize the way your users conduct analytics and how they derive insights. You can infuse your product with predictive, prescriptive, and conversational experiences to engage your users and elevate adoption.</p>
<p>AI and Machine Learning algorithms can rapidly analyze vast volumes of data and uncover complex patterns, trends, and insights that business users may miss. They can enable teams to gain a deeper and more nuanced understanding of their data so users get more actionable insights.  For example, teams can use predictive and prescriptive analytics to make forecasts and recommendations by leveraging historical data.</p>
<p>Standard methods include regression and time-series analysis, classification and decision trees, and neural networks. With the right technology, product and software engineering teams can directly provide predictive and prescriptive insights within their applications to enable users to anticipate future trends, identify potential risks, and optimize decision-making processes. Infusing your product with AI/ML can provide more powerful insights than descriptive analytics alone.</p>
<p>Another opportunity is to automate and improve <a href="/glossary/data-preparation/">data preparation</a> by streamlining repetitive and labor-intensive tasks involved in flowing data in for analysis using AI and ML. Algorithms can categorize, clean, enrich, and process large datasets, saving time and resources for analytics delivery teams. This allows analytics teams to focus on higher-level tasks, such as focusing on metrics, and <a href="/glossary/interactive-visualization/">visualizations</a> for analysis.</p>
<h3>Incorporating GenAI into your AI/ML strategy for analytics</h3>
<p>GenAI opens up a new avenue of opportunity: conversational analytics. Because Large Language Models (LLMs) excel at understanding and contextualizing user inquiries, they can provide a more intuitive and user-friendly interface for interactions with analytics. Instead of navigating through complex dashboards or writing specific queries, users can have an ongoing conversation with the AI to access the desired information, from asking questions to explaining answers.</p>
<h2>Artificial intelligence and machine learning examples</h2>
<p>Discussions around AI and ML can often get abstract fast, so let’s delve into three critical AI/ML use cases around data preparation, predictive analytics, and conversational analytics.  An important consideration is that as you evaluate analytics platforms to enable your AI/ML strategy, they provide the power to allow you to adopt and benefit from them rapidly.</p>
<h3>Data preparation</h3>
<p>Let&#8217;s say you’re delivering a marketing product and want to incorporate data for analysis. Your data comes from multiple sources, including spreadsheets, databases, and social media platforms, but it’s unstructured, inconsistent, and requires manual effort and scripting to clean and integrate. You need a way to fix and enrich the data automatically.</p>
<p>AI for data preparation can be utilized to automate this task. For example, Natural Language Processing (NLP) algorithms can be applied to parse textual data and extract relevant information, such as company names. They can identify and correct inconsistencies or errors in the data, such as spelling mistakes or missing information.  It can assist in data transformation by identifying patterns and trends to fill in missing values, standardize data formats, and perform data type conversions.</p>
<h3>Predictive analytics</h3>
<p>Another one of our AI/ML use cases is for predictive analytics. You might work in corporate retail or provide software to retailers. You want to use historical data to forecast future demand for a particular product. You have a whole set of factors to consider, such as past sales data, seasonality patterns, customer behavior, and external factors, such as economic indicators, weather, and marketing campaigns, that can all be used to build a predictive model that can forecast sales.</p>
<p>The predictive analytics you provide might produce a report that predicts that the demand for a particular product will rise by 20% over the next three months. So, your users might take proactive measures such as increasing inventory, adjusting pricing, or launching a targeted marketing campaign.</p>
<h3>Conversational analytics</h3>
<p>The following example is around using GenAI and Natural Language Query to provide a conversational experience. In this example, you might be the provider of an application for sales teams to plan staffing and territory coverage. You’re looking to differentiate your product by providing a conversational experience and making it easier for users to engage with analytics.</p>
<p>Incorporating conversational analytics might enable a user to interact with a chatbot within your product and ask, &#8220;Can you show me the sales trends for the past year in the United States?&#8221;</p>
<p>The analytics chatbot would analyze the user&#8217;s query, understand the intent behind it, and retrieve the relevant data. The user might follow up and ask to explain the results, and the system then generates a narrative response in natural language, such as &#8220;The sales trends in the United States for the past year have shown a steady increase. There was a significant spike in sales during the holiday season, followed by a gradual decline in the first quarter of this year.&#8221;</p>
<h2>Supercharge your AI/ML strategy with knowledge graphs</h2>
<p>Simply put, knowledge graphs act as a memory system for organizations by storing relevant information about queries and how users engage with analytics.  They can underpin AI and ML by storing and ‘studying’ the associations between all the relevant entities and interactions, such as models, metrics, clicks, filters, and more, to store the organization&#8217;s collective wisdom. It’s valuable data that can provide more “human” and context-aware insights from AI.</p>
<p>For instance, as people transition within an organization and new individuals interact with data, knowledge graphs can provide recommendations based on past queries made by predecessors. This ensures that valuable knowledge from previous searches is not lost and can be used to inform future recommendations.</p>
<p>Imagine a team of analysts working on a project to improve customer retention. They have been using a variety of data sources to identify customer behaviors and preferences that indicate a likelihood of churn. One of the analysts is promoted to a new position, and a new hire takes their place. Without a knowledge graph, the new analyst would need to start from scratch, combing through various data sets and trying to make sense of the findings. However, if the previous team had been using an analytics platform underpinned by a knowledge graph, the new analyst would get recommendations and guidance based on previous interactions to uncover insights without starting from a clean sheet of paper.</p>
<h2>Download the guide to set your AI/ML analytics plans into motion</h2>
<p>Ensuring analytics experiences are infused with AI/ML and providing predictive and prescriptive insights is already the key to fostering user engagement and driving actionable analytics. The emergence of Generative AI has further elevated the potential for AI to unlock the next level of value from analytics by delivering conversational analytics experiences.</p>
<p>Development teams are taking a composable development approach to deliver AI/ML analytics infused into their applications, rapidly building applications using flexible modular components that can be combined and arranged to meet application and user needs. </p>
<p>It’s time for analytics leaders to define their strategy, use composable development to flow AI/ML into the analytics user experience and infuse AI/ML into every step in the analytics process, from data to decisions. </p>
<p>Ready to get started? Get the complete guide: <a href="/whitepapers/sisense-ai-throughout/">Unlocking end-to-end AI for analytics: From ML to GenAI</a>. In it, you’ll learn:</p>
<ul>
<li>How to build a powerful end-to-end AI/ML strategy, from predictive to generative.</li>
<li>Next-level detail into core concepts from trends and forecasting to GenAI</li>
<li>Why critical success considerations include composability, LLM flexibility, and trust.</li>
</ul>


How to build a holistic AI/ML strategy for analytics

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