Businesses have a never-seen-before opportunity to learn more about their operations, markets, and customers by leveraging the humongous amounts of data aggregated from a variety of sources – apps, software, websites, and social media. The need to dive deeper into and derive insights from this data has never been greater. Legacy business intelligence and analytics products use structured, relational databases as their underlying technology. Relational databases lack the agility, speed, and deep insights required to turn data into value. Salesforce has transformed business intelligence technology by taking a novel approach to analytics, combining a non-relational approach to diverse data forms and types with advanced search capability, an engaging interface, and an intuitive mobile-friendly experience.
Salesforce's Einstein Analytics Platform enables businesses to explore their data quickly without relying on data scientists, complex data warehouse schemas, or monolithic resource-intensive IT infrastructures.
Legacy Business Intelligence (BI) tools restrict an organization's agility, and their application is limited to IT and analysts. Interestingly, while Business Intelligence tools have become more sophisticated over time, the core architectural approach to BI and analytics has largely remained unchanged. When an organization sets out to investigate an issue or question, the BI team responds by creating a relational database or data warehouse. Data warehouses comprise relational databases that add and store data in rows and columns, with each piece of information stored as a value in the table. Relationships across tables develop into schemas.
Every fresh infusion of data expands the schema by adding new rows and dimensions. Once the structure is established, it is sacrosanct and cannot accommodate new data; adding new data necessitates the creation of a new schema from the ground up. The relational database paradigm remains effective for a wide range of applications, particularly transactional activities involving highly organized data. However, during the last decade, developments in technology, data volume and diversity, and dynamic markets have created a chasm between historical business intelligence and analytics capabilities based on classic relational database design and today's business requirements.
The relational database model poses a number of issues in today's corporate landscape:
User Challenges
The model limits agility.
The waterfall nature of traditional Business Intelligence acts as a deterrent for discovering new ways of doing business, restricts team members' ability to challenge existing processes, and prevents teams with the most access to customers and the market from invoking their curiosity and asking their own questions for exploring innovative modeling techniques to improve the business.
It is not representative of the way in which users explore information.
Traditional Business Intelligence projects do not have the flexibility to refine the user query or add new data for context. Users ask a question and then wait weeks or even months for an answer; if they learn that the initial question was incorrect, the schema build-out must begin all over again. Another limitation of traditional BI is that it pre-aggregates the data which limits insights.
It forces compromise.
A typical BI setup balances expected queries and performance. Compromise leads to discontent. For instance, data is rolled up to a higher granularity to improve query efficiency, but this precludes users from answering second or third-order queries. They must then return to IT to figure out the solution or utilize an alternative tool to solve their questions.
Business Challenges
The model slows down the business.
Creating a BI schema can take weeks or even months depending on its size and complexity. On top of that, this does not include the time internal users must wait in line for BI or IT resources to become available. This delay indicates a poor time to value for BI investments; and imposes severe constraints on the business, which frequently relies on BI insights to move forward proactively which can hamper its ability to act quickly.
It is resource-intensive.
The current setup of designing BI solutions necessitates an army of professionals from architects and business analysts to data scientists and project managers to manage an organization's BI requirements. Because businesses rely heavily on BI, these teams are frequently well rewarded and in high demand.
Pivot business intelligence on its head for agile, end-user discovery.
In recent years, a number of new solutions have attempted to address the issues raised above. Many of them, however, have continued to rely, at least partially, on the same design and technological approaches that created the problems in the first place. One example of an emerging innovation is the usage of columnar or in-memory databases, which BI companies have implemented during the last decade. While they made progress, the relational model and its limitations remained a hindrance.
Salesforce, on the other hand, has created and launched an analytics platform that challenges traditional business intelligence. The Einstein Analytics Platform rejects most of the preconceived concepts of data warehousing and database design, instead adopting a "Google-inspired" approach to business analytics. It includes a proprietary, non-relational data store, a search-based query engine, powerful compression methods, columnar in-memory computation, and a fast visualization engine.
The Einstein Analytics Platform combines the complexity of heterogeneous data, the fluidity of questions and problems users are trying to solve, and the end user’s need for exploring data with agility, all without any restrictions on time and information. Einstein Analytics was architected from the ground up to allow enterprises to quickly find value in data. The platform was built first for a native mobile app, allowing users to rapidly find answers and take action using their smartphones.
Technology principles underlying the Einstein Analytics Platform.
Agility
Einstein Analytics does not differentiate between data types. It onboards data by embracing any data structure, kind, or source and making it available quickly, eliminating the need for a lengthy ETL procedure.
Speed
Heavy compression, optimization methods, multi-threading, and other techniques enable extremely fast and highly efficient queries on massive datasets.
Search-based exploration
It uses an inverted index to search data similar to Google search which provides query results in seconds.
Actionability
When a user gains insight or makes a key decision, they may immediately take the next best action straight from within Einstein Analytics.
Columnar, in-memory aggregation
In Einstein Analytics, quantitative data is stacked up in a columnar store in RAM in the Salesforce Cloud rather than the row structure of a relational database on disk.
Interactivity
Fast, intuitive visualization encourages user adoption and contextual understanding, offering genuine self-service analytics to all business users.
Open, scalable cloud platform
Einstein Analytics is an extensible platform with easy-to-use APIs and its scalable architecture compliments existing BI systems and allows businesses to have deep relationships with third-party tools and systems. It is also deeply integrated with Salesforce so you can see your Sales Cloud and Service Cloud data like never before, collaborate, and take action from within Salesforce.
Mobile-first design
Einstein Analytics is an open, scalable, and extendable platform. Einstein Analytics' architecture, which includes simple APIs, allows for extensive integration with third-party applications and complements existing BI systems. It is also deeply linked with Salesforce, allowing you to see your Sales Cloud and Service Cloud data like never before, collaborate, and take action directly from Salesforce.
Security
The Einstein Analytics Platform is built on Salesforce's tried-and-true, multilayered approach to data availability, privacy, and security, with the added benefit that data on the Salesforce platform does not need to leave Salesforce servers to be available for analytics.
A unique approach to Business Intelligence that offers faster time to value.
In order to provide an open, agile, self-service solution for enterprise business intelligence, Salesforce has brought together a number of unique approaches, including a non-relational inverted index data store, a quick and potent query engine, an intuitive and compelling visualization, mobile-first technology, and the trusted, scalable, high-performance power of the cloud. Given that numerous companies have made significant investments in business intelligence technology, Salesforce developed Einstein Analytics to enhance current offerings, facilitate seamless integration with external data tools, and allow businesses to easily tailor their analytics programs. The goal of enterprises using BI solutions to accelerate time to value is supported by this new BI analytics platform.
Additionally, Einstein Analytics facilitates enterprise-wide adoption, supports a unified data governance strategy, and frees IT teams from labor-intensive and low-value data retrieval and preparation tasks so they can concentrate on more strategic endeavors. The open Einstein Analytics Platform positions Salesforce and its partners to continuously innovate and add layers of intelligence to help business users gain insights even faster, through automated analytics, as the world enters the third phase of computing — from today's systems of engagement to tomorrow's systems of intelligence. The basis for true business intelligence in the future is Einstein Analytics, which is quick, flexible, perceptive, and capable of not just capturing past customer and business behavior but also anticipating future trends.
If you want to harness the true power of business intelligence for sales, marketing, and customer service, connect with a trusted Salesforce Consulting partner. Our certified Salesforce consultants can empower you with the tools and insights aligned with your business needs and help you get started.
To find out more, schedule a free Salesforce Einstein Analytics demo today.
In June 2023, the world’s foremost Customer Relationship Management (CRM) product company announced the launch of AI Cloud, a path-breaking enterprise AI solution. This dependable, open, and business-ready platform is intended to boost organizational productivity by embedding generative AI experiences into all Salesforce apps. This significant achievement demonstrates Salesforce's continued commitment to trusted AI, as well as its ambition to enable businesses regardless of size and industry to digitally transform and provide a 360-degree view of their customers.
AI Cloud includes purpose-built tools and functionality to deliver enterprise-grade AI and is Salesforce's latest multidisciplinary endeavor to add AI capabilities to its product line. In many respects, it is a continuation of the company's generative AI program, which was introduced in March 2023 and endeavors to integrate generative AI throughout the Salesforce technology stack.
AI Cloud hosts and serves text-generating AI models from a variety of partners, including Amazon Web Services (AWS), Cohere, Anthropic, and OpenAI, on Salesforce's cloud platform. Salesforce's AI research group offers first-party models, which support services such as code creation and business process automation. Customers can also introduce a custom-trained model to the platform, storing data on their own infrastructure.
Generative AI Across Salesforce Products
Salesforce-built models in AI Cloud enable new capabilities in Salesforce's marquee products – Salesforce Data Cloud, Mulesoft, Tableau, and Salesforce Flow.
Einstein GPT in CRM
Einstein GPT is the next generation of Einstein, Salesforce's AI engine, which now makes over 210 billion AI-powered predictions per day. By merging proprietary Einstein AI models with ChatGPT or other leading large language models, customers may use natural-language prompts on CRM data to trigger powerful, real-time, tailored, AI-generated content. Here’s a look at how Einstein GPT helps teams to boost productivity.
Einstein GPT for Sales: Automate routine sales tasks such as drafting emails, scheduling meetings, and preparing for follow-ups.
Einstein GPT for Service: Automatically generate knowledge articles from past case notes. Auto-generate tailored agent chat responses to boost customer satisfaction through personalized and faster service engagements.
Einstein GPT for Marketing: Generate tailored and targeted content in real-time to engage customers and prospects via email, mobile, social media, and advertising.
Einstein GPT for Slack: Get AI-powered customer insights such as smart sales summaries via Slack and reveal user behaviors such as knowledge article updates.
Einstein GPT for Developers: Leverage Salesforce’s proprietary LLM to boost developer productivity by using an AI-powered chat assistant to generate code for languages such as Apex.
Einstein Trust layer
What is the key differentiator of AI Cloud? Salesforce is promoting Einstein Trust Layer, a cutting-edge moderation solution that prevents text-generating algorithms from storing sensitive data such as consumer orders and contact information.
The Einstein Trust Layer is a powerful set of features and safeguards that protect your data's privacy and security, increase the safety and accuracy of your AI output, and encourage responsible AI use throughout the Salesforce ecosystem. The Einstein Trust Layer, which includes capabilities such as dynamic grounding, zero data retention, and toxicity detection, is intended to let you harness the power of generative AI while maintaining your safety and security standards.
A rising number of global corporations have prohibited or restricted the usage of generative AI, such as ChatGPT, citing privacy concerns. Einstein Trust Layer is tailor-made for such enterprises that have stringent compliance and governance constraints that prevent them from adopting generative AI tools. The first question that arises in everyone's mind is how much can we trust generative AI. The Einstein Trust Layer is purpose-built around trust and security and designed to enable these enterprises to approach these new technologies safely and securely.
The Einstein Trust Layer acts as a bridge between an app or service and a text-generating model. It detects when a prompt may include sensitive information and automatically deletes it before it reaches the model. This layer can also screen for toxicity (eg. racism or other types of discrimination), whether in a prompt or the model's response.
Users who link third-party models to AI Cloud such as Google's Vertex AI can use Einstein Trust Layer. Salesforce's partnership with OpenAI ensures cooperative content moderation by leveraging OpenAI's safety tools and the Einstein Trust Layer.
Salesforce is providing a set of prompt templates and prompt template building tools to set AI Cloud apart from other managed AI service offerings available today. The Einstein Trust Layer’s optimized AI prompt templates leverage harmonized data to contextualize outputs generated by the models in alignment with the organization’s needs, improving the quality and relevance of the created content.
The Einstein Trust Layer reduces the time and cost to adapt a generative AI model for a particular use case. For instance, a customer could design a template that instructs a model to draft emails in accordance with a particular style, or one that retrieves specific information from a Salesforce record. AI Cloud marks a fundamental shift in the automatic creation of email content – one that is grounded in CRM data.
AI Cloud represents the powerful combination of data, customer relationship management, and AI. As prompts become smarter and better, AI Cloud is poised to become an invaluable tool for businesses delivering greater value to customers across the Salesforce technology stack.
Trusted AI begins with secure prompts.
A prompt is a series of instructions that guides a large language model (LLM) to produce relevant results. The more contextual the prompt, the better will be the outcome. The Einstein Trust Layer allows you to safely enter AI prompts with context about your business while its data masking and zero data retention capabilities ensure the data's privacy and security when delivered to a third-party LLM.
Seamless privacy and data controls.
Utilize the scale and cost-effectiveness of third-party LLMs while ensuring your data's privacy and security at every stage of the generating process.
Data Masking
Before providing AI prompts to third-party LLMs, automatically mask sensitive data such as personally identifiable information and payment information and customize the masking settings as per your company's requirements. The availability of the Data masking capabilities of EinsteinGPT varies by feature, language, and geography.
Dynamic Grounding
Generate AI prompts with business context securely from structured or unstructured data by taking advantage of multiple grounding methodologies and prompt templates that can be scaled across your organization.
Secure Data Retrieval
Allow secure data access and contextualize every generative AI prompt while retaining permissions and data access limits.
Your data is the real product.
Salesforce allows customers complete control over the use of their data for AI. Whether you use Salesforce-hosted models or third-party models such as OpenAI, AI Cloud does not retain any context. Once the output is generated, the LLM forgets both the prompt as well as the output.
Eliminate toxic and harmful outputs.
Scan and evaluate each prompt and output for toxicity and empower employees to share only suitable content. Ensure that no output is shared unless a moderator or designated content approver accepts or rejects it, and save every step as metadata to leave an audit trail to promote compliance at scale.
Securely Unlock Enterprise-Grade Generative AI with AI Cloud
Einstein, Data Cloud, Flow, Tableau, and Mulesoft all benefit from AI Cloud's capabilities. Salesforce AI Cloud empowers organizations to unlock the future of their AI journey with a solution that is trustworthy, open, and intelligent.
Developing trust and embracing openness
The Einstein GPT Trust Layer enables businesses to employ generative AI with confidence by facilitating the deployment of relevant models for a range of tasks. This trust layer allows enterprises to use a variety of large-language models (LLMs) while adhering to their trust and openness standards, which prioritize data privacy, security, and compliance.
Leveraging capabilities of Third-party Large Language Models (LLMs)
Salesforce's AI Cloud promotes open development by integrating third-party LLMs such as Amazon Web Services (AWS), Cohere, and others. These LLMs are hosted within Salesforce's secure infrastructure, so user prompts and responses stay within the Salesforce environment. Salesforce has also formed a trusted partnership with OpenAI, utilizing their Enterprise API and security capabilities, as well as the Einstein GPT Trust Layer, to secure data retention within Salesforce.
Salesforce's own large language models such as CodeTF, CodeGen, and CodeT5+, assist companies in reducing talent gaps, lowering implementation costs, improving team efficiency, and detecting incidents that require immediate attention.
Bring Your Own Model
With AI Cloud, companies that have trained their unique models elsewhere to integrate seamlessly with their desired infrastructure. These custom models, whether built with Google's Vertex AI, Amazon's SageMaker, or any other platform, can connect directly to AI Cloud over the secure Einstein GPT Trust Layer. Organizations can maintain control and privacy over their information by storing it within their trusted perimeter.
Enterprise Ready Solution
Salesforce predicts that by the end of 2030, Generative AI will drive $15 trillion in global economic growth and increase GDP by over 25%. These are remarkable numbers and Salesforce believes that AI Cloud will propel businesses to new heights, with efficiency and productivity being the key differentiators.
Prompt Template and Builders
With AI Cloud, Salesforce has created a user-friendly solution that generates AI prompts that rationalize data and ensure that the content provided is in complete alignment with an organization's unique context.
As a young company (we are only a decade old!), we are driven by the immense potential of emerging technologies such as Generative AI to deliver value to our clients and help them bring their ideas to fruition. To fully explore the potential of AI Cloud, connect with a trusted and certified Salesforce implementation partner. Our Salesforce AI services help marketing, sales, service, commerce, engineering, and IT teams work seamlessly with generative AI.
To know more about how we can tailor unique scalable solutions for you by leveraging the power of generative AI to enhance the customer experience, connect with an
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