Einstein Analytics – Salesforce Business Intelligence

In Analytics Cloud / Einstein, Best Practices by MondayCall Admin

The beginning of a new year is an exciting time. Re-living the glory of past accomplishments, learning from previous mistakes and applying fresh knowledge to create a strong plan for the coming year. One of the many lessons we’ve learned in our careers as sales and marketing leaders is the importance of goal setting early in the year. Clearly articulated goals coupled with insightful data can quickly place you ahead of the competition.

For Salesforce customers, there are numerous tools available (both native and available on the AppExchange) which provide transparent and insightful data to end users. Although many of these tools are strong, one of the most effective tools we’ve come across in the previous months is newly improved Wave Analytics.

What is Wave Analytics?

Wave Analytics (formerly Salesforce Analytics Cloud) is a business intelligence tool, which has taken the complexities of data analysis and turned them into simple, mobile-friendly solutions that any Salesforce user can understand.

Previously, implementing the Analytics Cloud platform required extensive development hours because it was meant to be built from scratch. Today, the Wave Analytics platform comes pre-configured and is auto-populated with your current Salesforce data, drastically lightening the burden of implementation. Additionally, there are numerous, mobile-friendly applications that come pre-configured and are department-focused. Meaning, applications that provide intelligent KPIs related to Sales, Service, Marketing and other miscellaneous departments.

Although quality and transparency cannot be stressed enough in data-driven organizations, it’s simplicity that drives value. When comparing Standard Salesforce reporting to Wave Analytics, we discovered some key differences worth noting.

Wave Analytics vs. Standard Salesforce Reporting

One of the most noticeable differences between the two is the user interface. Wave Analytics has a clean, modern look that tends to encourage the end-user to explore data much more thoroughly. Pre-defined filters that you don’t have to recreate with each report, dynamic dashboards with customizable widgets, heat maps, forecasting and strong data security are just a few differences between the two.

Wave Analytics is also designed to analyze data from external sources, whereas standard Salesforce reporting would require you to import all data before analyzing. This means there is no need to litter your Salesforce instance with unnecessary customizations and complex development tasks. Open API access for Wave Analytics allows you to quickly import external data source into a single, data analysis platform.

Another major differentiator between Wave Analytics and standard Salesforce is the mobile capabilities. Wave Analytics was built “mobile-first” and is optimized for tablets, phones and even smart watches. Whereas data analysis and report building is limited on the Salesforce1 mobile platform. Wave Analytics allows your team to efficiently slice and dice reports from the comfort of their mobile device without losing crucial functionality.

A few smaller, but notable differences include the security model, licensing, and the learning curve. Security in Wave Analytics is separate from standard Salesforce, so a new security model must be scoped and defined. It’s not required to license all your current Salesforce users on Wave Analytics, so you can select a handful of users to begin utilizing the platform, helping protect your wallet. There’s also a shallow learning curve for newly exposed users, meaning there’s not an extensive need for professional services after the initial configuration.

Best Practices / Questions to Ask

Wave Analytics is a powerful business intelligence tool, which can provide strong value for your management staff. Although the tool is known for it’s simplicity of use, being successful on the Wave Analytics platform requires thorough planning and complete transparency of data sources.

Understanding your data sources is the first and most important step in the planning process. Designing a dataset is a complex process that requires a thorough understanding of incoming and outgoing data. Matching and data transformation needs to be completed in order to get the data into Salesforce. Wireframes, flowcharts and other transparent visualizations are extremely important in the planning phase.

Once you have a complete understanding of where the data is coming from and how it’s structured, it’s now time to thoroughly define your requirements. What do you want to get out of Wave Analytics? What are your current weaknesses in reporting? What are you looking for in reports, dashboards, filters, etc.? Who’s going to be using the tool? What security measures must be taken?

The most important takeaway from the implementation of Wave is learning and training your users. The power of the platform comes from the simple UI that encourages the users to explore the data within Wave Analytics. Understanding how the platform works, both inside and out, can show drastic improvements in how your team utilizes and analyzes data.