If these are the main reasons that customers turn to platforms, how can organisations ensure the platform gets used? In the first context, there are clearly issues about looking into the longer term, and being confident that the platform has wider applicability for the organisation.

But perhaps there are also questions about scalability: making sure that the platform has options for multiple teams to use it, with small incremental costs charged to each new team. It also means continuing to encourage teams to use the platform to address problems, and showing them how where necessary.

The scalability and multiuser nature of platforms does, however, bring its own challenges, notably security. A multitenant environment, where data is handled, managed and manipulated, is something of a security concern. It is, however, worth getting over this. Some organisations, for example, have found it possible to restrict access to certain subgroup users.

Encouraging use is also a matter of having the right tools available. The platform must be open to a range of users with quite different skills – for example, in Python, Java, Lua, SAS® or R – and also different levels of skills, from experienced programmers to new business users. For widest use, the platform must be open and accessible, as well as easy to use.

It also needs to have the right data, which means that this must already be available to the organisation. It is important to ask whether there are enough useful data sets available, because without data, the platform will not get used. Finally, the platform must perform. The algorithm must respond within the required time, and it must also deliver useful and reliable results. It is no use encouraging people to use a platform that delivers unreliable answers.

Looking to the future

There has unmistakably been something of a step change in thinking about platforms. This may be a result of the amount of data now available. It may also be because of the rise in accessible tools that means business users can get involved in analysis.

It may, however, not least be a result of the increased use of machine learning and artificial intelligence. All of which means that an analytics platform is now moving into the realm of “essential” rather than “nice to have.”

This article was republished with permission from SAS.