Are you responsible for ensuring your company or group is profitable and growing? 

If yes, then I assume you already understand the value and importance of using analytics.  However, what you may not know is the difference between advanced analytics and non-advanced or simple analytics.  

Clarifying the distinction between these two analytic approaches is the goal of this article.   

At Lityx, we are fond of talking about “Advanced Analytics,” but realize that for many, it doesn’t matter.  What matters is if you can improve a business challenge—not the technical aspects of how. It is worth understanding though at least a bit about what it means when you hear someone say “Advanced Analytics” because it implies additional benefits not available with less advanced analytics.  So let’s talk about some of the benefits of advanced analytics that you cannot get as quickly, as accurately, or in some cases at all, out of more simple analytics.  

A definition…

I define analytics in general as the examination of information/data to gain insight to assist with improved decision making and strategy.  Advanced analytics is then all that plus, using more intensive and/or granular techniques and methodologies that require greater skill, experience, tools, and understanding that (hopefully) result in even better decision making and strategy. I say “hopefully” because there is no guarantee that just because you take an advanced analytic approach to your situation, you will necessarily come away with better insight.

This is due to several reasons such as the:

  • depth, breadth, and quality of the data,
  • the ability to apply the approach correctly,
  • whether the method is the right one. 

The two most commonly recognized examples of advanced analytics today are machine learning and artificial intelligence—both of which I will touch on later.  

As you might imagine, there are tons of analytic approaches that qualify as advanced.  If we are dividing analytics up into only two types, advanced and non-advanced, it is probably easier to talk about what is non-advanced and leave everything else as “advanced” given the explosion of advanced capabilities that have come to the analytics space.  

Non-advanced Analytics

Non-advanced analytics is what you likely have experience using.  It is almost always backward-looking, telling us what happened or why did it happen and most commonly presented as reports or decks with summary statements, findings, tables, and charts.  

Nowadays, a whole host of tools are available for the execution of non-advanced analytics, and the value is and will always be high. The level of effort is relatively low, and the insight is high in comparison to not having it at all.  You need this type of analytics to tell you what is happening, to help you manage your business, to share performance, and communicate with key stakeholders.  

This is never going away and will never diminish in value. What has happened though, is where it once was considered advanced to have some of this insight, it is now table stakes and expected for every business. The non-advanced analytic approach is also more accessible than ever, less expensive, and faster to produce.  

An important aspect and downside to this type of analytics is that it requires significant interpretation.  The “so-what” gap as I like to call it otherwise known as the “insights-to-action gap.” What are the results saying that you should do differently, and why? 

That question can be hard to answer as you sort through a pile of figures and charts.   

Advanced Analytics

I like Figure 1.0 from Gartner, below, as one way to think about the increasing levels of analytics. It offers us a dividing line for advanced analytics.  

Figure 1.0

I have placed the dotted line between Diagnostic and Predictive analytics as to the location where most practitioners of advanced analytics see the most significant jump.  

Descriptive and Diagnostic analytics is reporting and use of business intelligence tools to examine what has occurred to explain what happened and why.  

Predictive Analytics, as you would have expected, is looking ahead and predicting what will happen using techniques such as Machine Learning.  

This type of analytics has become pervasive with the tools available. And, the experience required is becoming less and less all the time.  

Prescriptive Analytics informs you what is required to achieve a specific outcome. You can think of this as a type of Artificial Intelligence (AI). As an example, at Lityx, we use a technique called mixed-integer programming that performs constrained optimization and provides this type of insight.  The field of AI is pervasive and exciting, beyond the scope of this article, but the fantastic ideas offered continue to grow.  

Much of advanced analytics is still more hype than reality, but that is always the case with new innovations.  One key difference with advanced analytics is that it dramatically closes the so-what or insights-to-action gap. The answers you find in this type of analysis inform you or in some cases specifically, tell you what to do.  

Non-advanced analytics can then be helpful digging deeper into why the solution recommended is best.

An Excellent Example of Advanced Analytics At Work

A client was using a combination of counts, averages, ratios, etc. along with a mapping tool to identify the best zip code locations to hold events—a very laborious process with no clear answer. The only insight they could derive was best candidates for location to choose from depending on the prioritization of factors. They were getting good insight and were able to make better decisions than they could have otherwise, but it was taking a long time, and they were missing out on the benefits of advanced analytic techniques.  

The high-level benefits of advanced analytics, in this case, include decreased turn-around time, freeing up staff to work on other or more strategic matters, the ability to quickly examine different scenarios and most importantly immediate, specific and better recommendations through examining of a much wider variety of information simultaneously.  

These benefits are all possible because of advances in computer power and tools to support a wider variety of advanced techniques. 

In this case, we can build predictive models from historic results and look ahead into existing or new markets and make accurate predictions on a variety of performance or cost measures.  Then we can combine all of the predictions, required business rules, and desired outcomes into a prescriptive analytic decision engine to identify all qualifying candidates and rank them from best to worst.  

This rank can then be duplicated using updated business rules or desired outcomes and compared over and over.  In this way, the business manager can quickly receive and understand recommendations and tradeoffs to make the best decision.  

The detailed level and accuracy of the insight available through advanced analytics is far greater than with non-advanced analytics. 

The challenge, of course, is access to the resources and people who understand how to perform advanced analytics.  Thankfully resources are more abundant than ever, but in high demand and so typically pricey. The tools that can perform advanced analytics are not very expensive though (free in some cases) and the approach is becoming more accessible every day.

Why Should You Know About and Care About Advanced Analytics? 

The short answer is its ability to provide a competitive advantage both for the individual and the business.  

You don’t need to understand all of the underpinnings of advanced techniques fully, but you should know the gist of what is going on and be in a position to leverage these techniques.  Using advanced analytics, you can access very detailed and complicated insights such as the most optimal allocation of your marketing dollars across each of your audience, offer, and channel segments layered with numerous constraints (business requirements) that you must meet.  

We live in a time when you can answer almost any marketing question. The sophistication of technology, software, and people who understand how to leverage them abounds.  The challenge is to narrow down the questions, so they are answerable and then obtain the necessary data to find the answer.  

As someone who works closely with clients in this area it never ceases to amaze me how many seemingly unique challenges are typically quite common with readily available solutions using advanced analytics.

I encourage you to look into this further, talk with a trusted source that knows about this area, educate yourself, and begin using advanced analytics.  

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