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Descriptive Analytics is your hometown. It’s what you know and love, and where you feel the most comfortable.



Simply looking at your data and asking what happened—as in, what trends, patterns, and relationships does this data describe?—is a very valuable skill and an important background to have. But, as you look around and see what other people can do with data, you’re itching to enhance those familiar reports and processes. As you get more comfortable with Descriptive Analytics and working with data, you also see tools that are just barely out of reach and in the realm of the data science team.



What if you could predict future outcomes? Prescribe solutions? Not only could you ask and answer new and interesting questions that just might bump you up the career ladder, but you could end up working more closely with a data science team to churn out high-impact analysis.



If that’s how you feel, you’re not alone. You’re ready to explore beyond your hometown. What’s around the corner? Advanced Analytics. And, with self-service analytics platforms, Advanced Analytics is much closer than you might realize.


The Road to Advanced Analytics

Before you can fully embrace Advanced Analytics, let’s define what we’re talking about. The term ‘Advanced Analytics’ is used a lot, but does anyone have a clear definition of what it means? We do. When we talk about unlocking Advanced Analytics or up-leveling your career with the help of a self-service analytics platform, we envision analysts becoming citizen data scientists and being able to perform functions like these:


Predictive Analytics

Predictive analytics describes what’s likely to happen in the future. In other words, it allows you to go beyond today and answer questions about tomorrow. This capability is valuable in business, as decisions can be made based on a firm analytic foundation rather than on gut feel or rough extrapolation of past trends.



In 2016, just 10% of organizations had some form of predictive analytics. However, Gartner expects this number to grow to 35% by 2020. Platforms like Alteryx allow analysts to perform predictive techniques like classification via logistic regression or decision trees with drag-and-drop tools as well as with coding languages like R and Python.



The growth of predictive analytics could be a career-changing opportunity for you. Machine learning techniques can outperform statistical approaches to forecasting by over 20%, and analysts may even command higher salaries when they add that predictive fuel to their engine.


Prescriptive Analytics

Let’s say you’re interested in not just estimating what could happen in the future, but also what should happen to achieve a desired result. That’s where prescriptive analytics comes in. Rather than simply reporting back your findings, prescriptive analytics allows you to identify an optimal course of action. Prescriptive analytics does this essentially by gaming out different outcomes given certain business constraints and even simulating outcomes based on uncertain conditions.



Like predictive analytics, Gartner expects the use of prescriptive analytics to grow. As of 2016, only 10% of organizations had some form of prescriptive analytics. Gartner expects the number to grow to 35% by 2020, meaning you still have time to get ahead of the trends and be a trail blazer.


Add in Spatial Analytics

In a nutshell, spatial analytics is a bird’s eye view of what’s going on. How do these data line up with the physical world? By adding spatial analytics to predictive and prescriptive, you can do things like predict expected travel times, identify the fastest travel routes, recommend paths to build a pipeline, or pinpoint the best location for your next store or hospital.



If you think spatial analytics doesn’t apply to you, think again. 70-80% of data has a spatial component to it. Today, at this moment, you’re sitting on underutilized spatial data. Let your imagination run wild with the possibilities.


Let’s Hit the Advanced Analytics Highway

As you take on projects that incorporate Advanced Analytics, you’ll still visit your hometown, Descriptive Analytics. Just because you’ve added to new capabilities to your skillset doesn’t mean you’ll forget your roots.



But it’s a big world out there and it’s time to explore what else it has to offer. Your career is yours to own and, as you speed down the highway, you may find interesting pits stops along the way, like optimization, A/B testing, or even time series forecasting. Who knows what you’ll see! Follow your interests and know that, with each new skill, you’re continuing to add even more value to your career and your organization.