Algorithms Hold the Key to Your Analytics Success
Blog - Advanced Analytics, Analytics
Here is Jim’s 4th take on the Analytics Mindset Series. If you have missed his earlier musings, follow the links below:
If you’ve been following along my recent blog path, this topic should come as no surprise to you – skip to the next paragraph, oh faithful one. For those reading for the first time – welcome – allow me to quickly summarize those prior musings to make sure you are up-to-speed. In my prior blogs, my basic positions were that a) advanced analytics should be the eventual goal of your data project (https://tinyurl.com/yyssmke8); b) the BI tool itself is not the most important thing to focus on (https://tinyurl.com/y5onj8vb); c) take advantage of cloud analytics to achieve the greatest returns on your project (https://tinyurl.com/y5y8tk9h).
Given those premises, and keeping in mind the ultimate goal of maturing your analytics capability, let’s chat about the what, why, and how of algorithms.
What? a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer (dictionary.com). A generic start, but at least something from which we can build understanding. From this description, one could see that the programming code itself is algorithmic in nature, and that is quite true. Thus, at the most basic level, an algorithm in our context is most often expressed as a code that is written to perform specific functions, and executed within the context of your application to produce outputs that are useful in an analytical way. So, if an algorithm is “just” code, what makes it so important?
Why? As we know, computer code is an assemblage of logic statements, calculations, and actions that determine how the software reacts to various inputs (data from keyboards, clicks, or other systems). Algorithms are coding objects that have a specific purpose in a larger scheme; that is, they take specific inputs, manipulate them in a scientific or modeled approach, to produce outputs that have value in a larger context. Analogy – let’s take Nanna’s Irish soda bread recipe. The individual ingredients (code) have no special value in and of themselves. Yet, when they are assembled in a specific portion and sequence, it is then that the magic is revealed. One could say that the recipe is an algorithm that unlocks the wonder of that loaf. Of course, the algorithms you deploy might not be as tasty – but certainly more germane to your business objectives, and include additional elements that computers excel at.
How? When you get down to it, this is the crux of the matter. How does one create an algorithm? Where do the inspiration and raw materials come from? Once created, how do I use the algorithm within the context of my analytics framework? Let’s examine this one at a time…
Where is your pain? Let’s say you are working with the Sales team to improve their strike ($) and hit (win) rates. Currently, the Sales team accepts proposals from customers and treats them with equal importance. The VP of Sales is looking for a way to understand the nature of a customer proposal and derive a score such that it would help the team prioritize those opportunities that provide the greatest value. This is the making of an algorithm, wouldn’t you say? There are data elements to consider (customer history, market history, complexity, potential revenue, etc.) as you work with business experts, data scientists, and data engineers to test models that provide outputs that (hopefully) indicate the relative attractiveness of that proposal. The model could be used to inform where limited resources should be applied, among other things.
It’s alive! To bring your scoring model alive, the data science team can no doubt use any number of tools available in the marketplace. Open source has provided abundant resources – like R, Python, and Spark – through which your algorithm can come to life. However, many enterprises prefer to scale their solution using licensed products that provide integrated capabilities across development, visualization, and governance requirements. Either way, this is the step where you will use data to train and improve the model over time as you drive to production readiness.
Hey, look at me! We have this fantastic algorithm that Sales loves, now we need to surface it for greatest impact and accessibility. Certainly, you can embed the scoring metric within the Sales pipeline dashboard along with other key data about pending deals. Better still, create a specific “top deal targets” page on your intranet or mobile app that sales teams (and C-suite) can easily see on a daily basis – a good way to drive interest to these algorithmic endeavors and, along with it, additional budget support. Another option – integrate the scoring metric directly with your Sales/CRM tool so that busy teams don’t have to go into another screen to see this data, it is right there in the tool they use on a daily basis.
Analytics frameworks that utilize dashboards and tabular reports (I’m looking at you Finance) have been standard fare for over a decade and are still useful in the modern approach. Through the adroit use of algorithms, however, you have the ability to unlock insights that form the basis of your enterprise advantage. Unleash that advantage and you just might become the analytics hero of your organization.
This blog was originally published on LinkedIn