Visier recent funding rewards a novel approach to building an analytics platform
- Summary:
- Visier has taken a modular approach to building analytic applications that allows it to be both nimble and highly productive.
Funding announcements aren’t particularly interesting unless there is something else going on behind the scenes. On the dark side, analysts are always on the lookout for down rounds and funding that is tied to the replacement of the CEO. On the positive side, analysts get more excited when the company receiving the new capital doesn’t really need it or has some novel usage for the money being planned. Visier’s recent $45 million series D financing round qualifies as one of those more positive and interesting events.
Visier sprang onto the scene a few years ago when its founder, John Schwarz, left SAP to found the company. Schwarz previously was founder and CEO of Business Objects. Imagine having Schwarz’ Rolodex and being able to call dozens or hundreds of leading business executives to ask them how they would solve certain but chronic HR problems like unplanned attrition. Based on the insights they got from these executives, Visier built a new kind of HR analytics and bring them to the market years before most HRMS vendors could. That's an interesting approach to developing analytics - putting customer requirements first. .
To put a sharper point on things, let’s contrast Visier with a major ERP vendor. A top ERP executive at one firm recently intimated to me that they had a dozen-plus page Microsoft Excel spreadsheet that was full of potential analytic applications they would like to bring to market. This company found that the development, market testing and rollout of these HR and finance analytics to be time-consuming. Their average analytic application takes over 18 months to become market-ready. At the rate they are progressing, we will all be retired before those initial solutions hit the market.
Make no mistake, there are problems with the commercial production of analytic applications today. Some vendors failed to understand how to create applications that scale. In other words, how can they take the learnings acquired in the development of one analytic solution and reapply those quickly to other kinds of similar business problems? It is this inability to scale in the development of analytic apps that decides the market success or failure of analytics firms going forward.
The current Visier customer is the Chief HR Officer. The Visier applications are designed around helping companies solve critical people management issues. But that HR only focus is going to change with this new funding.
This latest financing round of $45 million almost doubles Visier’s funding to date. These monies will be used to expand the solution set into other areas such as finance. Broader geographic expansion will also occur.
This financing round brings in an additional investor (Sorenson Capital). Sorenson comes in at a time when the company has tripled the number of enterprise customers and grown recurring revenue by 500+% . All this occurred since the last financing round approximately three years ago.
Will Visier succeed? Probably. The company’s initial HR analytic applications were designed to be sold to and used by HR executives. HR departments are notoriously hard pressed to get any IT resources to help them with technology initiatives.
For Visier to succeed in that space they had to create solutions that delivered real-time analytic answers while requiring only a miniscule amount of (one-time and continuous) assistance from the customer’s technology team.
Visier did not require customers to build on-premises data warehouses and/or manage the contents of same. Everything Visier delivered had to be easy to set up, non-opaque in its rules and logic and capable of becoming smarter and smarter over time.
The result of this novel approach is that the company’s headcount continues to grow much slower than the rate of new customer acquisitions. This is an interesting and important metric as too many analytic application builders cannot scale their efforts.
The most important reason for Visier's scaling ability is that the learnings from one problem or customer are easily transferred to another. This is because Visier creates mechanisms for logic reuse.
Likewise, analytics that bounce around from many different functional areas or vertical industries may find that each customer and business problem requires dozens or hundreds of new or unique interfaces or integrations. By creating an analytic platform, libraries of common integrations and a master data management capability, Visier achieves higher productivity and can create more applications, more rapidly for ever larger numbers of customers.
In my recent conversation with Schwarz, we talked about how many end-user companies struggle to build their analytic capability in house. Companies might, for competitive advantage reasons, be tempted to build their own tools. However they often fall into some common traps.
The first trap is that they will try to build a data warehouse as the key source of insight for the analytic applications. Unfortunately, data warehouses are time sinks, technically challenging and hard to maintain. And, that’s assuming that the customer correctly anticipated which data elements need to be incorporated into the data warehouse in the first place.
It gets worse. Many firms try to build a custom analytic tool that uses mostly ERP transaction data. While calling this a proof of concept, it makes the subsequent utilization of unstructured data like satellite images and/or photos, massive data sets like IoT sensor data and streaming data almost impossible to incorporate.
Finally, custom solutions often require the use of multiple but highly technical and complex tools that are beyond the capabilities or skill sets of the intended users.
In the end, company executives want insights, answers and suggested courses of action. Pre-built analytics win if they can be implemented fast and with little impact on IT and operations. These same executives don’t want expensive, time-consuming systems that are rigid and from which value is difficult to achieve.
The market for great analytic applications is clearly there. Unfortunately, few vendors have looked at this space correctly. Analytics may not be a simple bolt-on to the existing ERP suite. That perspective will likely generate incremental solutions of minor usefulness. Worse, until these vendors learn how to build analytic apps at scale, they will not create viable solutions at a market acceptable pace.
The winners in this space will be the vendors who approach analytics with a different viewpoint. This is directionally where Schwarz is taking Visier. It's a good space to occupy.