We are hearing more and more customers ask us about Tableau and Einstein in light of Salesforce’s Tableau acquisition. This is understandable and very much expected. Tableau has cemented itself as the global leader in business intelligence (BI), and Salesforce has spent the last six years growing Einstein Analytics following its acquisition of EdgeSpring in 2013. Soon, Tableau will be integrated into the Salesforce ecosystem, and, on the surface, there will be two data visualization offerings by Salesforce.
So you are probably asking yourself:
- How should an enterprise look to reconcile this?
- Will it be duplicative (or even a waste of money… I know you might be thinking that) to own both products?
- What are the best practices and recommendations on how to leverage the strengths of each?
These are just some of the questions that we are hearing. The truth is that it is possible for these two to “play nice” with each other and through their complementary features.
As we discussed in our last blog about Salesforce’s Tableau Acquisition, Tableau excels at the data preparation layer and easy-to-build data visualizations for drill-down and exploration. Einstein Analytics excels at delivering an augmented analytics experience and integrating with Salesforce workflows. Together, these features create a powerful combination to drive digital transformation in the enterprise.
Tableau-Einstein Strategy Starts with the Data
Anyone who’s been around the world of reporting and analytics knows that it all starts with the data. If you are using both Tableau and Einstein Analytics today, then chances are that you’ve already had to think hard about your data strategy. Companies that have the budget and bandwidth to have large implementations of both of these products have no doubt also engaged in a larger scale data strategy.
We could go on and on about data strategy (and there’s a lot of other content on our website that you can review on that subject), but for the purposes of this blog there is only one thing to keep in mind: the single source of truth. Users rely on their reporting to have consistent and trustworthy data. Without it, adoption and any change management will fail. Organizations that are using both Tableau and Einstein Analytics should strongly consider using a cloud data store as the source of data to feed both analytics products. We like Google Cloud BigQuery, but are well experienced in a variety of technologies.
If you are not ready for a cloud data store, then we recommend that careful consideration is given to data preparation. Both Tableau and Einstein have data connectors, with Tableau boasting solid capabilities with on-premise data, through neither are true data integration platforms. That said, any metrics or use cases that are replicated in both systems need to have data prepared with an eye for consistency.
Too often, governance is an afterthought. Not to say that this is not top of mind for some executives and IT professionals, but we have found that analytics governance is not as strong or consistently applied when compared to governance for other business applications or processes. Given that typical reporting and analytics functions are not considered business-critical nor part of production processes, this is understandable. However, governance is one of the most critical questions and can be a difference-maker in the success of your analytics program.
The way in which the enterprise tackles governance is a longstanding debate in the world of analytics. This rests in the constant push-and-pull on the spectrum of self-service analytics. Technology has allowed for reporting to emerge out of the backroom, dot-matrix reports of systems of old. The proliferation of Microsoft Excel alone played a huge role in putting reporting in the hands of business users. As more sophisticated reporting and analytics tools evolved and matured, the ability to put the powers that were traditionally reserved for IT professionals in the hands of everyone became a reality. But, with that came issues of governance. Before long, the same metrics and KPIs were reported by many different people all arriving at different results; and, countless versions of the same information were replicated many times over.
The governance question plays right into a dual Tableau-Einstein strategy. Analytics governance can range from the extremes of a proverbial wild west where every end-user has power user capabilities to tightly controlled environments where every request for data must go through approvals. While both of these products can be used regardless of the approach to governance, there are nuances to these two products that allow you to take advantage of their features as it relates to different users and governance models within that spectrum.
Tableau has gained market share and cemented itself as the market leader in data visualization due to the way that they have “democratized” data for all types of users. It is a great tool for power users to explore, re-package, and interrogate data in order to derive insights. It’s flexible, scalable, and easy-to-use; and, it is built with an eye towards end-users creating their own content and sharing with others.
On the other hand, Einstein Analytics excels at delivering great content to end-users when the use case is fairly well-defined. A user can explore a dataset to find insights, but the power of this tool rests in situations where the data and the ways in which one should explore it are pre-built. If you want to guide your end-users to look for particular insights and have tighter controls on governance, then Einstein Analytics shines well. Einstein also provides a range of capabilities starting with Einstein Discovery that allows AI features to be easily incorporated into the user experience. These are capabilities which are more advanced and easier to use than what is offered in Tableau.
Meeting Your Users Where They Are
Perhaps the most obvious way that you can take advantage of both Einstein Analytics and Tableau is by delivering insights to users directly in the environment in which they work. With Einstein Analytics, you have a native Salesforce experience which allows powerful insights to be embedded directly to the end-user workflow. Whether you are simply creating an account-specific dashboard for an Account record page or stitching together visualizations with the advanced analytics of Einstein Discovery, the ability to guide users to take the right action natively in Salesforce is a great way to drive real business value with analytics.
However, if your users are not in Salesforce or they require a platform to conduct more self-directed data analysis, then Tableau may be a better fit. Mature organizations are going to have many different users who will have different needs and traverse multiple applications. The key is to understand the use case and persona and determine the best fit. It can become difficult to traverse all the various combinations of data sources, users, and use cases for analytics and determine the best fit. In future blogs, we will dig into this detail even further.
In summary, it is totally reasonable for an organization to use both Einstein Analytics and Tableau. It will come down to the individual user and purpose in order to determine the strategy for having these two great products work in a complementary fashion. Data is your critical foundation, and while a cloud data store is recommended, Tableau can provide a great foundation in its own right with its wide range of connectors which include on-premise data sources. Users who need “traditional” dashboards, the ability to perform multi-step data analysis, and are likely to publish content are going to gain the most value out of Tableau. Users who utilize Salesforce for their day-to-day jobs and can benefit from insights integrated into their workflows are going to get more value from Einstein Analytics.
Here are a few questions we are using to help our customers determine their path forward:
- Do you have business users with the right analytical skills to dive into data for advanced analysis, regression models, or developing machine learning algorithms?
- Are you more concerned with what has happened and is happening? Or is your use case more about what can happen in the future?
- How would you describe your approach for data governance? Do you want to define and then feed the right information to your users or let them build their own tools?
- What integration toolset are you using? Where is your data located?
We are excited to see how this will unfold as Salesforce and Tableau unite. We can help you make sense of your analytics journey with Salesforce. In the coming weeks and months, we’ll add more detail on our view on how Einstein and Tableau can complement each other.
In upcoming blogs, we will share our recommendations for customers that have:
- Only Salesforce Einstein deployed and are considering Tableau
- Only Tableau deployed and are considering Salesforce Einstein
In the meantime, if you’d like to earn about our solution accelerators or jump-start services to successfully deploy Einstein Analytics and Einstein Discovery contact us today!