Data analysis is the present and the future when it comes to innovation of technology. Never before have organizations been so easily able to see the progress of a project in real time. But the way that data is collected and analyzed is crucial for the success of your innovation. To misuse your data analysis platform will lead to a misunderstanding of your project and your team, and could lead to mistrust from stakeholders.

Having the right team, of course, is a big part of it. Properly implementing the right learning platform is even more important. But how do you go about that? It requires care, finesse, and the right team. Here are a few tips to successfully implement a learning platform.

Develop a Strategy

Before you make any decisions, make a plan. What are your organization’s LMS needs and why are you seeking a learning platform and data analysis. The project manager should create the plan, the case, and the pitch. From there, they can find the right team to implement the learning platform. With a strategy in mind and a team formed, the decisions for the rest of the process will go much more smoothly.

Choose the Right Tool

Naturally, the next step to successfully implementing a learning platform for your data analysis needs is to, well, choose the platform. There are three main tools that organizations can use for machine learning: Python, SAS and R.

• Python. Python is an older program than R and SAS and for some time was too limited to stand up to either of them. However, it’s quickly gained in popularity lately. It’s an open source scripting language with libraries, statistical functions, and opportunities for simple and complex model building. In terms of structured data, Python is often preferable, but it’s still a bit more limited than R in terms of visualization.
• Statistical Analysis Software (SAS). SAS was created for commercial data analysis and has been the market leader essentially from its conception. It’s full of statistical functions, it’s user friendly and it has great technical support, making it a dream for companies who need a reliable learning platform. Unfortunately, it’s often a step behind when it comes to cutting edge solutions, and it’s definitely the most expensive option.
• R. Much like SAS, but open source. R was originally popular in the academic field, but eventually spread to the commercial field. It’s usually up-to-date with the latest solutions. In some areas, it’s not as fast as Python, but it does have more options for data visualization. It’s also definitely the most cost effective option.

Currently, most organizations seem to find R the most beneficial learning program, but all three programs have their benefits. Your choice depends on your organization’s needs. Another more recent option is Practera. It’s a Sydney based learning platform that’s special in its collaborative options. Team members can give feedback on the things that are going well and less well for them as they work, and algorithms alert the project manager of weak links in the project that might need their attention.

Create a Timeline

So you have the strategy, the team, and the tools. Now it’s time to get to work. But since stakeholders will want to know when to expect results, you want to set a realistic timeline, leaving plenty of room for inevitable problem solving. Think of all the steps involved: collection, migration, analysis, etc. How long would each step take for a certain pool of data? Then consider how long it would take if one or more of those steps were hindered. You should always leave yourself sufficient room for error. If the work is complete before deadline, you’ll only look that much better to stakeholders, and your team will be able to relax.

When setting a timeline, honesty and communication is vital. A lack of communication will slow down the work. Sometimes team members, in an effort to impress a supervisor or under perceived pressure from that supervisor, might put themselves on a tighter deadline than they can handle. Encourage openness and be honest yourself to find the best timeframe for the work.

Migrate Your Old Data

If your switching from one learning platform to another, you’ll need to migrate your data from the old system to the new. This will involve both your IT and the IT of the new learning platform, so in this case, it might be worth it to pay the extra cost for SAS and their exemplary tech support. You don’t have to move all your data, and in fact, this might be a good opportunity to declutter. Simply move relevant data as well as user profiles. For the rest, you can start fresh with your new learning platform.

It’s a combination of having the right people and the right platform to accommodate them. When these things are in place, the implementation and execution of your LMS needs will follow.

Bio 

Christine is an assistant for Practera and enjoys fine dining during her spare time among other things.

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