Underpinnings Of Intelligent Search, Discovery, and Insights In Salesforce
No matter how big your database is, if you’re unable to utilize its potential, it makes no difference to your business. And this is where most of the companies lack due to the inability to explore their data. A large chunk of data stored by the firms is in unstructured data format.
Consequently, it takes up enormous time, effort, and resources to bring unstructured data into a standardized form.
So, unorganized data urges the right method and analytics to derive meaningful insights and reap data benefits. But fortunately, Salesforce users are far from such inconvenience due to Einstein Analytics that enables intelligent search, discovery, and analytics.
What is Salesforce Einstein Analytics?
Salesforce Einstein Analytics is a cloud-based analytical tool that helps with predictive analysis through reports and dashboards. Not only does the tool help you to gain quick insights into data, but it also keeps you posted about activities taking place in the organization. This analytic app empowers CRM users to make the most complex decisions, unveiling the latent potential of data.
Further, enhancing the capabilities of Einstein Analytics, AI-powered Einstein Discovery lets business users detect and understand relevant patterns based on data available. Most importantly, it delivers smart explanations for the identified patterns in easy-to-understand languages.
So there’s no additional need for building data models to analyze patterns.
Wave Analytics Acquisition
Big enterprise databases usually create problems when it comes to extracting valuable information, but not with Wave Analytics. It was a cloud-based tool that helped users to build reports and dashboards regardless of the size of the database.
How Wave Analytics Incorporated Into Salesforce Einstein Analytics
Edgespring- an analytics cloud company was acquired by Salesforce in 2013. A year after at Dreamforce 2014, people came to know Edgespring as Wave Analytics, and later the name changed to Salesforce Einstein Analytics, which we have now.
For a better understanding of Einstein Analytics, let’s check out its various components.
Components Of Einstein Analytics & Their Scopes
Analytics collects and organizes your data into four components which are as follows:
You can treat an app as a folder, just like you’d use the Private Reports folder in Salesforce, you can use My Private App in Einstein Analytics to store dashboards, lens, etc. Each user has their own app, and it can be shared with different levels of access based on roles, groups, etc.
The dashboard lets users analyze and explore widgets. Users can simply monitor the key metrics of a business. Also, the dashboard permits the addition of interactive charts to squeeze-out the information in an easy-to-read format.
A lens is quite similar to the Salesforce report and provides a view of data in datasets. Using a lens, users can view data in graphical form. Without any restriction, a lens can be saved and shared independently with users.
A dataflow is a set of instructions to transform the data as per your dashboard needs. It helps you to specify what data to extract from Salesforce objects or datasets, how to transform the datasets, and which datasets to make available for querying. In simple words, dataflow helps you with datasets for the dashboard.
The Face Of Analytics Going Forward
Data and analytics have become fundamental components for delivering enterprise value. Thus, to enhance a company’s ability to grow faster, an analytical approach is pre-requisite to make any informed decisions. By 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency.
Further, by the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.
Source: Gartner projections
Without a doubt, data and analytics have been accelerants to digitization and transformation. The emergence of Salesforce Einstein data analytics has not driven enterprise decision-making but also aided users with predictive insights and prescriptive recommendations. The use of analytics has unraveled new opportunities within their data at every turn.