Analytics already plays a major role in business success, and that role is only growing over time.
The ability to leverage massive quantities of data to adapt quickly, make smarter decisions, and be confident those decisions are backed by math and science—this is a dividing factor between those companies that have a business edge and those that are left behind.
For all its ubiquity, though, companies need to clear some rather large hurdles before effectively putting predictive analytics to work. These hurdles include:
- Managing data in a way that makes predictive analytics models successful
- Developing the capabilities and practices for predictive analytics
- Finding—and affording—the right talent to make predictive analytics projects effective
While none of these common hurdles are insurmountable, they underscore the importance of being deliberate when building out your predictive analytics capabilities.
In other words, you need a plan, and that plan should involve these three areas of focus: proper data management, utilizing the right tools, and finding the right talent. Let’s dive into each of these.1. Proper data management
Like all modern analytics, predictive analytics requires a large pool of data to sift through in order to return results.
With the vast majority of this data being unstructured (meaning, it doesn’t serve a specific purpose already), a great deal of time and energy needs to be put into properly categorizing, tagging, and governing information.
More importantly, all the rules and governance that you apply to data needs to happen as it comes in. Otherwise, you risk a number of unforced errors, including data siloing and unreliable data—two main culprits behind predictive analytics projects grinding to a halt.
2. Utilizing the right tools
Predictive analytics may be a relatively new arrival in the grand scheme of things, but there are already some robust out-of-the-box tools available to help you get your initiatives up and running.
These tools are additions to ERP systems. Some, like JobBOSS, are tailored for midsize manufacturers, but most can be utilized by organizations regardless of industry. Among the more popular ones are SAP Business One and Oracle’s NetSuite.
Keep in mind, though, that the more customized your predictive analytics workloads are, the more your organization is going to need specialized talent and skills to get the most out of any solution you find. Which brings us to …
3. Finding talent
In many ways, predictive analytics is still an emerging technology. Because of this, high-skilled talent is relatively rare and always in demand.
While this is obviously a bonus for those with the necessary skills, it often makes it hard for the non-Tesla, non-Netflix businesses of the world to find the talent needed to internally jumpstart predictive analytics initiatives.
That’s where partnering with outside experts can help level the playing field.
Here at Redapt, for example, our advanced analytics teams assist with every component of adopting predictive analytics from designing and building the right architecture for resource-intensive workloads, to identifying the right reporting tools and helping organizations construct their first predictive analytics models.
Regardless of whether your organization tackles the adoption process in-house or with a partner, the time to get started with predictive analytics is now. The faster you can start making smarter decisions, creating efficiencies, and gaining insights on where your industry is going, the more likely you are to get ahead of your competitors.
To learn more about how you can implement predictive analytics in your business, check out our exhaustive guide on advancing your advanced analytics capabilities.
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