2 Common Misconceptions About Predictive Analytics


LeadofC2There’s a lot of talk about predictive analytics and big data, but people often confuse the two.

Big data refers to the mounting repository of information, whereas predictive analytics is the process that turns that data into something useful.

In other words, predictive unleashes data’s potential through algorithms that can actually learn from themselves. This can have far-reaching benefits in anything from sales acceleration to crime fighting and health care.

However, it’s not a simple process. In speaking with many different organizations and customers, I’ve found two common misunderstandings around data. Before any organization can make progress on the predictive front, it must understand:

  • Proper data hygiene
  • Implementation timing

1. Proper data hygiene

Complications in creating predictive models often come from dirty data. Far too many organizations think they can simply upload whatever data they have into the system and receive the perfect outcome.

That’s not how it works.

Organizations need to make sure their data is clean. That means it must be relatively error-free. If data is duplicated, inaccurate or outdated, it’s not going to give you accurate results.

In other words, garbage in means garbage out.

To help improve your data and ensure it’s ready to fuel a predictive engine, you must scrub it. That means amending or removing data in a database that is incorrect, incomplete, improperly formatted or duplicated.

Typically, this process involves updating records to create a single view of the data, even if it is stored in disparate systems.

2. Time to implement

In their eagerness to adopt predictive solutions, organizations often misjudge how quickly things will be up and running.

There are no microwaveable dinners when it comes to building predictive models. You can’t push a few buttons and expect a ready-to-eat meal in less than a few minutes.

I often have to help temper customer expectations when it comes to predictive solutions, helping them understand that this process takes time.

Organizations should plan for an implementation that can range from several weeks to several months. They must also constantly build and retrain their models with additional information to refine them and ensure they are working properly.

Start with strategy

The key is to have a well-thought-out data strategy.

Start by asking yourself what are the areas of your business where you can gain the most meaningful insights.

The XANT Cloud has positioned itself to help organizations develop and build their data strategy.

The XANT Cloud represents the evolution of big data applications for businesses, allowing companies to realize the full potential of predictive technologies in their own applications, business processes and sales practices.

To learn how predictive analytics can increase your sales success, download this free ebook.

The Science of Lead Scoring, Prioritization & Sales Success

Free eBook: The Science of Lead Scoring, Prioritization & Sales Success

79% of marketing leads never convert to sales. That means inbound reps waste a lot of time chasing the wrong leads.

Brent Peters

The post 2 Common Misconceptions About Predictive Analytics appeared first on InsideSales.

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