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5 Reasons to Build A Predictive Model In-House

Reading time: 5 minutes

By Earl Sires

Many organizations face the daunting task of pulling together disparate data sources, cleansing the data, and learning how to leverage the data for predictive analytics-led decision-making. Organizations often feel they’re limited by budget and technical expertise, and they lack a clear understanding of what it takes to build a predictive model themselves.

If you’re planning to add predictive analytics to your organization’s toolkit, and are having a tough time deciding between a consultant, a proprietary model, or a self-built solution, consider these 5 reasons you should build a predictive model in-house!

5 Reasons to Build Your Own Predictive Model:

  • It’s Easier than it Sounds
  • It’s Scalable and Adaptable
  • You Can Defend Your Results
  • You’ll Be a Data Evangelist
  • It’s Affordable and has GREAT R.O.I.

1. It’s Easier than it Sounds

Unless you’re already working in the field, Predictive Analytics sounds complicated, doesn’t it? Fortunately, the recipe is straightforward:

You Need:

  • A Problem to Solve
  • Data Related to your Problem
  • A Predictive Model

That third item might sound daunting, but once you understand what a predictive model is, it’s not as complex as it seems. When it comes down to it, you’re taking information, running it through a formula, and using the results to inform your decisions.

There is a slight learning curve to understanding how a predictive modeling application works, as there is with any new software. However, today’s tools are becoming more user-friendly by the day, and support is often available if you need training or get stuck.

Once you’re comfortable with analytics, it becomes very intuitive, and you’ll find that it’s easy to teach to new users – especially if you built the model yourself, because you’ll be very familiar with it.

You’ll sometimes get pushback from the IT department when you seek to get access to the data you need to build a predictive model. Typically, though, it takes very little work from IT’s side to get a model up and running. Once you’re connected to shared data sources on the network, that’s all the configuration that’s typically required.

2. It’s Scalable and Adaptable

Perhaps your organization plans to grow in the next year, or you discover a new application that you’d like to use predictive analytics for. A self-built model as part of an analytics platform means you can scale up or down as much as you need to.

You can close integration gaps between your existing systems, such as databases and visualization tools, or implement it as part of an all-in-one platform to service all of your data needs, from prep to modeling to sharing.

A common scenario: you have several staff members who all have different needs:

  • Some need to work directly with the data to create historical reports
  • Some come to you with urgent requests for reports on a spontaneous basis
  • Others need monthly, more comprehensive reports.

With in-house analytics, you can put together processes that resolve these needs, from data visualization to ad hoc reports to regularly scheduled predictive reports.

On the subject of flexibility, we here at Rapid Insight are regularly surprised at the variety of uses our customers find for the software. A recent example: a University using the software to organize enrollment and financial aid let us know that they’re also using it to structure their commencement and graduation ceremony.

Once the software is in use across your organization, the creative uses people make of it will surprise you, too.

3. You can Defend Your Results

When you build a predictive model yourself, you’re intimately familiar with its structure. You know the source of your data and you know that it’s clean, organized, and optimized for modeling. You know why the model includes certain variables and excludes others.

If you notice something out of place in your model, you can quickly correct it and get your results back on track.

With a proprietary (sometimes described as “black box”) model, you must rely on the company to make updates to fix any issues you observe. A consultant may not alert you of issues they observe, and if you notice something amiss, there could be a significant time lag before the consultant addresses your issue.

Relying on a proprietary model or hiring a consultant means you won’t have a look beneath the hood. To a certain extent, you’ll be making a leap of faith with a third-party solution, and you’ll just have to trust that their model is doing what it should be.

When you’re dealing with a sensitive or important subject, you don’t want to be in that position. You want to be able to clearly and directly defend the results of your project. You want to know it has integrity and that every part is serving its intended purpose.

4. You’ll be a Data Evangelist

YOU understand how important data is to your operations, but do the other key stakeholders in your organization get it? Too often, the answer to that question is no.

One of the major benefits of an in-house system is that you will make believers out of the rest of the decision-makers in your organization. Once they see the benefits of data and have some hands-on experience with it, they’ll understand its critical importance in the decision-making process.

When you can provide accurate, digestible, up-to-date reports and dashboards, you’ll demystify data and gain credibility and support. A predictive model sometimes confirms hunches, but models also regularly show surprising trends that lead to a change in strategy. As a data evangelist, you can bring impactful information to light and put it in the right hands.

That doesn’t just apply to the people at the top of the organization; with an in-house platform, you can democratize data and make it accessible and useful to everyone in your organization. If a coworker regularly requests certain information from you, you can show them how to create and schedule a report to generate automatically for them as often as they want it.

5. It’s Affordable and has GREAT R.O.I.

Pricing for a self-built solution is remarkably low, especially when you factor in the return on investment. In a recent webinar, Matt Rehbein, the Director of Institutional Research at Lipscomb University, noted that Rapid Insight’s data analytics platform reduced the time to produce a report from a full month down to just two days.

Transpose that kind of time-savings to your organization. What are your data-related pain points? Do you pull data from multiple, disorganized sources? Are there errors or omissions in your data that make it less useful to you? Do you manually enter data to compile reports?

These are all examples of areas where in-house tools can streamline the process and save you time. What could you get done with 28 extra days in your month?

Consultants and proprietary analytics can’t compete on pricing. With a consultant, you’ll pay a high rate regardless of how long the work takes, and the same goes for proprietary analytics. When you build a predictive model in-house, creativity is the only limit to your savings.

In Summary…

Consultants and proprietary models might beat in-house solutions if the only question is “how easy will it be”. There’s nothing easier than sending off your data and waiting for the (hopefully accurate) results. But neither of those options offer the verifiability, flexibility, affordability, and institutional impact you get when you build a predictive model in-house.

At Rapid Insight, our goal is to empower people to take control of their data and put it to work. That’s why we offer free, unlimited support when you sign on as a customer. We train you on the software, assist you in getting up and running, and are always just a phone call away when you need help.


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