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The evolution of technology has increased to a new level in the last five years and Customer Relationship Management (CRM) is an integral part of it. CRM has helped companies manage and analyze customer interactions and data throughout the customer life cycle, with the goal of improving business relationships with customers. But the analysis of data, managing it, and making business decisions based on that customer data is becoming more and more challenging because of the social media, internet, and technology. With a huge amount of customer data growing exponentially, extracting patterns, business trends, and actionable information is becoming a tedious and time-consuming task for organizations worldwide. Data generation, processing, and consumption are simple but converting that data to spot opportunities, trends, and correlation is a real challenge. In order to effectively tackle this situation, Salesforce has launched its most advanced platform called Einstein Analytics.
Pretty catchy name, right?
The Einstein Analytics platform has really become a buzz word in the tech town. So what is this Einstein Analytics? Why is there such a buzz about it? Why is everybody talking about it since its launch?
In this chapter, we will cover the following topics:
- The Einstein Analytics platform
- An introduction to Einstein Analytics
Einstein Analytics is a comprehensive business intelligence tool powered by artificial intelligence, and used to analyze all your business data in quick succession for precise predictive insights and prescriptive recommendations.
Salesforce users will get recommendations and suggestions automatically. The Einstein Analytics platform learns from the customer data already in Salesforce and makes predictions accordingly. Insights, predictions, and recommendations are served up seamlessly in Salesforce.
Consider this scenario, Anutosh Infotech is a multinational organization that launched a new product called All Smart. All Smart is a tracking device that connects your belongings to the All Smart application on your phone and helps you find them. Now, to create awareness of this product, Anutosh Infotech started publicizing it via ads on Facebook, YouTube, and so on and they got the xyz number of leads through it. Now Einstein will automatically give a recommendation about which campaign is more effective. So for the next campaign, the company will spend more money on effective campaigning. You can also share multiple insights regarding this campaign's Lead Scoring, Einstein Recommendations, Einstein Social Insights, and so on.
The Salesforce document clearly states the following:
"Einstein is like having your own data scientist to guide you through your day. It learns from all your data, and delivers predictions and recommendations based on your unique business processes."
In Einstein Analytics, we can gather data from different locations, such as Excel, Salesforce, Informatica, and so on and merge them together to build the insights.
Here are the main reasons why Einstein is the next big thing:
- Social media is a platform where customers give their feedback about products and services. So the data on social media will play an important role and this data is increasing exponentially each day.
- Einstein Analytics uses new technologies such as advanced machine learning, deeper understanding, predictive analytics, natural language processing, and smart data discovery. So, with every interaction and every additional piece of data, it will learn and self-tune to get smarter.
- Einstein Analytics uses predictive analysis and gives recommendations based on the data history.
- User can connect to data on other platforms and built dashboards on it. The user can also import that data to Einstein Analytics without any formulas or coding.
- Einstein is secure, trusted, scalable, dependable, and, of course, mobile! It enables everyone in your organization to get instant access to powerful statistics and figures through its intuitive point and click visual interface.
- It promises to help sales, marketing, and service professionals make better decisions, up to 38% faster by leveraging artificial intelligence. It proposes to achieve this via contextually relevant, self-service Analytics applications that can tell you what is happening, why it is happening, what is likely to happen, and what action you should take.
From the sales, marketing, or services standpoint, this smart, artificially intelligent tool is helping businesses understand the results of their different activities better.
You may ask, what is so great about Einstein Analytics or how it is different from the already present Analytics tool.
The answer to these questions is simple. Einstein Analytics allows users to note performance data to get an insight into different activities and their results. This is quite like the other tools, but here is how it is different:
- Its artificial intelligence analyzes productivity and automatically gives insights and recommendations for informed decision making
- It includes the following entire pool of Analytics applications to expose more in-depth and futuristic information:
- Service Analytics
- Sales Analytics
- B2B Marketing Analytics
With Einstein Analytics, every CRM user can now easily analyze what happened in the business, why it happened, and what steps to take, without a team of expert data scientists.
Salesforce is partnering with Trailhead, an interactive, guided, and gamified learning platform that offers 12 online learning modules. Trounce your competitors and get a head start by learning Einstein Analytics with Trailhead.
To start learning, go through the following tutorials:
- Analytics/Wave Basics
- Mobile Analytics / Wave Exploration
- Desktop Analytics / Wave Exploration
If you are working with Salesforce as a partner, the following are specific training programs for you:
- White belt
- Green belt
- Brown belt
- Black belt
Einstein Analytics is a cloud-based data platform as well as a data-analysis frontend, and it's designed to analyze not just Salesforce sales, service, and marketing data, but also any third-party application data, desktop data, or public data you care to bring into the mix.
So, sign up for Salesforce Einstein Analytics and get started by visiting https://developer.salesforce.com/promotions/orgs/analytics-de. The link will take you to a site that resembles this:
Now that you have signed up for the special developer edition, let me walk you through the basic concepts and terminologies used in Einstein Analytics.
Before starting with Einstein Analytics, we need to understand the basic concepts and terminologies. This section will help you to understand, how the Einstein Analytics platform works and the significance of different terminologies. Understanding this concept is very important as it avoids confusion during the implementation and hands-on tutorials.
So without further ado, let's begin our journey of learning Einstein Analytics.
A dataset is a collection of related data that is stored in a denormalized, yet highly compressed form. You can create the dataset from different resources such as Excel, Salesforce, or other databases. In other words, you can say that it is a data resource, specially formatted to create analytics and reports on it. In Einstein Analytics, all the fields of dataset come under three categories such as date, dimension, and measure.
A measure is a quantitative value, for example, amount, price, profit, and loss. Measure can be used to make mathematical calculations such as sum, average, maximum, minimum, and so on.
A dimension is a qualitative value, for example, city, region, and status. Dimensions can be used to create grouping and filtering. As it is a qualitative value, you cannot do math in this field.
A date can be represented as a day, month, year, and, optionally, time. We can use the date field to group, filter, and perform math.
Dataset builder is a point and click UI feature provided by Salesforce to create datasets. You can create a single dataset for a Salesforce object. Data can be created based on one or more related Salesforce objects.
A lens is a particular view of a dataset's data. Just like reports in Salesforce, the lens provides insights into data. This helps you analyze and visualize your data.
A visualization is a pictorial representation of dashboards, application, and lenses. Commonly, it can be a line chart, bar chart, stack chart, tables, pivots, or compare tables. Every visualization has a query associated with it.
A dashboard is a collection of charts, metrics, and tables. We can have one or more lenses in one dashboard.
Dashboard JSON is the JSON file for your dashboard. This file includes the information related to your widgets, their location, settings, static steps, and how they are connected.
An explorer is an interface where you explore datasets and lenses. It is the easiest way to access your business data and get data insights. Using the explorer, users can add measures, grouping, filters, and so on. In the UI, users can switch between Chart Mode, Table Mode, and SAQL Mode.
An application is a curated set of one or more dashboards and lenses. For example, If you have created four dashboards for the sales team and two dashboards for the service team then you can create two separate applications (like the folder) one for the sales team and another for the service team and move the dashboards and lenses to the respective applications.
A transformation refers to the manipulation of data. You can add transformations to a dataflow to extract data from Salesforce objects or datasets, transform datasets that contain Salesforce or external data, and register datasets.
For example, you can use transformations to join data from two related datasets and then register the resulting dataset to make it available for queries.
Salesforce Analytics Query Language (SAQL) is used to access data from a dataset. It is a query language for Analytics platform. Just like all other query languages, SAQL retrieves data from the dataset. Lenses and dashboards also use SAQL behind the scenes. It gathers the meaningful data for visualizations. We can use SAQL to handle complex views such as working with multiple datasets to get a single view.
A predicate is a filter condition that defines row-level access for each record from the dataset. Define a predicate for each dataset on which you want to restrict access to records. In other words, row-level security is enforced by the predicate.
You can use a dataflow to create one or more datasets based on data from Salesforce objects or existing datasets. A dataflow is a set of instructions that specify what data to extract from Salesforce objects or datasets, how to transform the datasets, and which datasets to make available for querying.
In this chapter, you learned that Einstein Analytics is a cloud-based platform for connecting data from multiple sources, creating interactive views of that data, and sharing those views in applications. We saw that it is a better way to distribute insights to business users so that they can understand and take action on changing information. It is also powered by artificial intelligence, which means that you have your own data scientist. For a quick start and a fun way to start with Einstein Analytics, we went through Trailhead tutorials or badges. The regular developer organization does not have Einstein Analytics, so you signed up for a special developer edition athttps://developer.Salesforce.com/promotions/orgs/analytics-de, where you have a specific training program if you are working with Salesforce as a partner. Also, we covered the concepts and terminologies of Einstein Analytics.
In next chapter, we will cover the Einstein Analytics setup. We will also see how to create permissions. We will explore user types and user licenses here. Einstein Analytics also has limitations and these will be covered in the next chapter.
