This post originally appeared on the Umbel blog. The Umbel Marketing team helped edit and refine the original content.
Salesforce isn’t satisfied with its $32B market capitalization. It wants a slice of the $42B business intelligence industry, currently dominated by major companies like Tableau, Domo, Microsoft, Oracle and SAP. How so? Well, Salesforce recently introduced the Wave Analytics Cloud, a product that will let companies connect data from different systems, visualize that data, and allow users to make actionable decisions from insights found in that data. This is themain business proposition of many big data BI companies.
Of course, these visualizations are not meant to be incredibly sophisticated but to rather help standardize business operations. According to the New York Times, “the application was able to look at a national sales force, quickly sort it by people and regions, then figure out where there were big disconnects between budget targets and actual results”
Despite its splashy entrance and enormous brand presence, the Wave Analytics Cloud will face many of the same pitfalls that its competitors face – limited data access, lack of a dedicated staff to use its robust functionality, and limited actionability.
Low Accessibility, Stale Data
First, visualization software has gotten very good over the past decade. Data quality has, too. Data access, on the other hand, has not.
Data warehouses are painfully slow, take years to build and have a limited number of people in a large organization that know how they work – or, worse, how to even use them. Pulling raw data to visualize in Wave might take weeks, at which point the data will be stale and no longer help the Sales team meet its goals or support the digital marketers in placing better social ads. Data warehouses by their very nature are warehouse-like. They are difficult to navigate, hard to improve and painfully slow to access. Wave won’t address any of these concerns.
Business Analysts, or Lack Thereof
Second, visualizing data takes time. The Wave Analytics Cloud will present users with a number of ways to aggregate, slice and manipulate raw reports to create any number of charts. Despite the size of the business intelligence industry, there are not that many individuals at smaller to mid-size organizations that have dedicated business analysts on payroll. This means that Wave won’t be accessible to a substantial part of the market.
Salesforce’s perspective on BI is also very human-oriented and limits the amount of knowledge that can be gained from its product. Humans have to find the data, ask the questions and wait for an answer. The real power in an analytics product is not just receiving answers to the questions asked but uncovering something about the data that you would have never thought to ask. Very few, if any at all, business intelligence products combine the two approaches.
Third, actionability is not a core part of Wave as much as Salesforce’s PR department would like you to believe. For the data to be actionable, the Business Analyst has to find something – an interesting piece of information, say – that is large enough to warrant action that goes beyond emailing his manager. If a digital marketing analyst learns that the BlueKai targeted ads aren’t performing well but they can be improved by adding a number of segments, he would have to find a way to combine the data that originally came from the data warehouse with the cookie data out of BlueKai and the tagging set up in Tealium. Wave lacks the ability to access these systems in a secure and compliant manner that respects user privacy while improving the productivity of said analyst.
The Wave Analytics Cloud is a massive product offering as well as a strategic shift for Salesforce. But they’ll have an uphill battle along with their competitors if they don’t address the underlying problems around data accessibility, on-call business analysts and real actionability. It has the potential to do well for Salesforce’s stock price and force competitors to improve their offerings, but it might not solve the underlying problems around understandable and actionable data.