One of them is Opera Solutions, which I was not familiar with despite having worked in predictive analytics for more than 10 years. I was surprised to learn the company claims to be generating $100 million in revenue annually. Founded in 2004, Opera provides predictive analytics as a service, employing a staff of approximately 150 data scientists along with hundreds of other employees. It claims this is the largest private group of scientists outside IBM. (I’ve written previously about IBM’s significant efforts in the predictive analytics market.)
I mention that for a reason. Opera Solutions is positioned at the intersection of several interesting industry trends: the rise of predictive analytics, the rise of big data, the rise in cloud computing – and a trend I will explore in our upcoming predictive analytics benchmark: the lack of skilled resources to work on the others. Academic institutions are recognizing the shortage of analytical skills also and are adding degree programs to address this need.
Software as a service (SaaS) implies some level of staff to develop, maintain and operate systems that companies run for clients, but the Opera Solutions model goes beyond that. Its data scientists provide domain expertise lacking in many organizations to create and apply predictive models to specific business activities.
Once the models are created, Opera continues to operate them and refine them on an ongoing basis. This services component caused me to question whether Opera is really just a consulting service, but it appears to have developed a reusable library of software integral to its cloud-based offering.
Opera focuses on developing applications in conjunction with a partner or customer, then generalizing those applications for broader use. It focuses primarily on six markets: global investments, credit and risk, marketing, supply chain, custom applications and data transformation. It currently has a portfolio of nine applications on the market and another six in development.
This concept of jointly developing applications has advantages and disadvantages. If you believe you have some unique way of doing business or analyzing data, you may not want to share that with others. On the other hand, if one of Opera’s existing products fits your needs, it may help you get into production more quickly and more cost-effectively than developing from scratch. If you do need to start from scratch and will allow Opera to allay some of the cost of developing your application by selling it to others with whom you do not compete, you should be able to reduce your costs.
The applications are a mix of Opera’s proprietary components and best-of-breed commercial or open source components. For instance, to manage large-scale data Opera works with standard relational databases, in-memory databases and Hadoop. Opera hosts the analytic applications in an Amazon cloud configuration, but they can be installed on-premises if the customer prefers.
Generally each product pulls data together from standard data sources and processes the data through Opera’s analytic stack to produce insights or actions. These insights or actions feed into decision-making processes via APIs and interfaces to existing applications. These applications may provide automated decision-making or present data to individuals to use in their daily activities. Opera Solutions then monitors what has happened as a result of these decisions and continuously repeats the process. Often this process can be labor-intensive, but Opera has found ways to automate many of the ongoing operations with its software components.
Much of the company’s intellectual property relates to its signal libraries, which help determine what is “noise” and what is a good predictor. In addition Opera has invested in visualization capabilities and machine learning. However, the company does not want to be in the software infrastructure market. It sees its value in providing the combination of services and software.
Given the underdeveloped state of the predictive analytics market and an apparent lack of expertise, that’s not necessarily a bad place to be. Opera can ensure the success of its customers by staying involved with their projects. It’s also realistic to recognize the role that skilled individuals play in predictive analytics projects today and use a cadre of such people to generate business.
Opera Solutions takes an interesting and unique approach. Given an apparent dearth of predictive analytics talent in enterprises and the potential value of those analytics, it could be a way to bring more forward-looking analysis into your organization. You will need to be comfortable with the model of sharing the resulting intellectual property with others, but if that isn’t an issue, this could be a viable option.
This blog originally appeared at davidmenninger.ventanaresearch.com.