Friction can be a big problem when engineers validate components for cars or planes. But we have found it can be just as much of an issue when you are trying to use data to improve your business. Instead of high temperatures and grinding gears, friction in data analytics can lead to failed projects, wasted money, and unnecessary work. In the three decades we have spent implementing data analytics solutions, we have found there can be organizational friction between departments and people, technical friction between tools and vendors, or financial friction between aspiration and reality. Often, it is all three. As much as 87% of the time, the result is the same – the data analytics project goes nowhere.
What is friction in analytics? It can mean:
- Communication gaps between data experts and domain experts
- Lack of knowledge or red tape around who can access data
- Incomplete, messy, or imperfectly formatted data
- Confusion regarding where pipelines or machine learning (ML) models should run and how to deploy them
- Skill disconnects between today's data experts and established data analytics toolsets
- Uncertainty or project redirections caused by constantly changing tools and infrastructure
These are big challenges that require careful thinking and holistic solutions; equal parts organizational education, technology acceleration, and all-around flexibility.
This is why the combination of Altair and RapidMiner is so powerful. RapidMiner brings a world-class, advanced data analytics platform and industry-leading Center of Excellence (CoE) program for organizational data analytics transformation. Altair brings scale, an established data analytics business, unique technology, and the revolutionary, patented Altair Units software licensing model. Together, we are uniquely able to provide the technical accelerators and organizational streamlining that our customers need to succeed in their data analytics initiatives.
The power of bringing together Altair and RapidMiner includes three components that can eliminate the friction points that hinder data analytics project execution:
1. The Center of Excellence (CoE) Program
It all starts with the CoE program. RapidMiner pioneered a process for data-immature organizations to transform themselves into data-savvy ones. It identifies the path that tackles the highest priority use cases first, upskills the business line teams so they can address data analytics problems themselves, and trains everyone – from business analysts to data scientists – to use the right tools for the problem.
On completing the engagement with the CoE, organizations will have succeeded in their first couple data analytics projects and will be prepared to succeed in the future. Since the acquisition, we have adopted the CoE for the entire Altair data analytics customer success organization. This has increased the scope of what we can offer and the team’s capacity by more than fivefold.
The CoE addresses several of the biggest points of friction in the beginning of data analytics projects. Chief among these is the gap between goals and the skills needed to reach them. There simply are too few data scientists, data engineers, and data analysts in organizations – and the world in general – to accomplish every project an organization wants to implement. In addition, these folks usually are not deeply involved in the business problem, so when they take on a project, the assessment and problem-solving stages can drag on or sometimes falter entirely.
The CoE addresses this by training business line personnel to tackle data analytics problems themselves. This means organizations don’t have to hire dozens more data scientists and data engineers, instead the people who experience problems can solve them on their own. It also means the data scientists and data engineers already in place can focus on the toughest challenges, above and beyond the basics. It brings out the best in everyone.
2. The RapidMiner Platform
The second piece to successfully overcoming friction is the RapidMiner platform. Organizational education and transformation is key, but the right tools accelerate the process and solidify future success.
The RapidMiner platform is a cloud-native, end-to-end, multi-persona analytics platform that accelerates data analytics projects on an enterprise scale. Put simply, it can help anyone in your organization connect to data, transform it, automate it, build machine learning models with it, deploy those models in production, and build data analytics applications the business line uses every day.
The RapidMiner platform smooths out several key friction points. At the most basic level, RapidMiner bridges the gap between users and data, providing dozens of connectors for big data and small data alike. It empowers single-person teams as much as large, multifaceted teams because each stage of the data analytics process can be executed step-by-step within the platform. Moreover, it puts the power of data analytics into the hands of those who know the problem best, so there is no friction between data experts and domain experts. This is because it has toolsets for users of all skill levels, no matter their level of coding expertise. It supports no-code functionalities via Auto ML for novice users, code-optional functionalities via the workflow builder for intermediate users, and full-code functionalities for experts. Moreover, users can move seamlessly between these modes as needed.
Critically, the RapidMiner platform also accommodates the shifting infrastructure landscape and complexities that come from trying to establish a data analytics ecosystem designed to last into the future. Altair has a long history of offering tools on-prem, in the cloud, in hybrid setups, and even as packaged appliances – the RapidMiner platform is no exception.
Finally, RapidMiner will uniquely be able to overcome the friction between established data analytics ecosystems and more modern, open tooling through integration with Altair SLC, an SAS language compiler and runtime that connects the SAS language with open toolsets like Python and R.
3. The Altair Units Business Model
The last piece of the package is Altair’s patented, revolutionary Altair Units software licensing model. The Altair Units model is a non-consumable, token-based model where tokens can be transferred from one person to another, and from one Altair product to another. Which means instead of buying seats for each of 73 different products, Altair customers buy a level of concurrent usage or “bandwidth,” and get access to the entire suite. This includes access to more than 60 third-party software applications, which are part of the Altair Partner Alliance.
This means different teams in different departments within an enterprise can use the same Altair Units pool. Additionally, it allows one user to use several Altair products in sequence without needing to purchase separate licenses.
The Altair Units system eliminates the friction of managing dozens of licenses from dozens of vendors – a seldom-addressed data analytics headache. And, because it is based on concurrent use, it makes the overall enterprise software ecosystem much more cost-effective.
With the CoE, the RapidMiner platform, and Altair Units data analytics becomes an easy, natural part of business. Every person can easily make sense from data, even when it is confusing, complicated, or messy. Every enterprise can cut through the organizational and technical resistance that keeps them from finishing data analytics projects and draw a clear path from their first data analytics idea to real-world use and business impact. Every data team can keep moving forward even when they switch cloud vendors, data warehouses, analytics tools, and programming languages. Our expertise, platform, and business model are the catalysts for data success, providing clarity in uncertainty, consistency through change, and collaboration despite silos.
Put another way, Altair plus RapidMiner means Frictionless AI. It means no technical friction between you and your data; no organizational friction between data experts and domain experts; no workflow friction between data application design and production deployment for effective decision making; and no migration friction when infrastructure or tools change. Just quick, repeatable, and successful data analytics projects.