Advanced Analytics – I Can Do That, Right?
Blog - Advanced Analytics, Analytics
Let’s set the stage for Mega Corporation (sounds important, very fictious). It seems that Mega leadership has realized that existing dashboards, KPIs, and scorecards are useful but not quite making the grade. The C-suite is all jazzed about this concept of Digital Transformation and want some of that for Mega. Putting aside what that term means, we now have the makings of a new project (we’ll call Titan) that needs definition, scope, talent, and resources. Of course, you are the person for the job as you have run numerous successful analytics projects in the past. Easy pickings, right?
First, you get the development A-team all set, ready to design, code, and deliver this critical project. Then, you gather stakeholder agreement on the problem statement and desired outcomes and agree on budget and resourcing. Finally, you document everything in the business case, functional specifications, design architecture, and countless other artifacts that your thorough Software Development Lifecycle call out. You are ready to pull the trigger on another successful project.
Maybe. Modern analytics projects – often preceded by the adjective advanced – are not going to succeed using the same formula you’ve always used. Certainly, the probability of meeting desired outcomes might not be as high unless proper consideration to emergent capabilities is given. Initially, you might think that the relationship between analytics / advanced analytics is akin to the difference between algebra / honors algebra (sorry if this dredges up awful high school memories). You know, the same material just done faster with a higher degree of difficulty. Nope…think like that and you might be on a path to ruining your perfect project delivery record (side note – if you are perfect so far, you are either running your first project or have only worked in academia where “the college try” counts as a win…but I digress).
Let’s dive into why these projects require additional consideration.
Advanced analytics require high-calorie data diets. Data is the oxygen for any analytics project, we all know that. We also know that for years we’ve collected all sorts of data and analyzed much of it – what’s different now? A lot.
To satisfy the demands of advanced analytics, you need real-time data feeds – not (gasp!) daily. You also need data that often does not reside within the firewall of your enterprise. Think social data, web data, public data, syndicated data, and even sensor data. This assortment must be brought to bare in a sophisticated manner or risk diminishing the impact your project will achieve.
Not all data will require real-time processing, agreed. But in this new world of advanced, the data that lends that spice to your “secret sauce” will most assuredly not age well. Consider your data architecture carefully as it will demand modern approaches to ETL/ELT/acquisition. Indeed, focusing on your data needs and integration strategy will be time well spent.
Don’t argue over BI. Lord, the argument over which tool is best to present the data is way past its prime. Put a pin in it, we’re done with this. Truly, all of the modern leaders in data user interfaces (about a dozen) have pros and cons, depending on what you value most. Despite that fact, all of them can serve you well in delivering compelling visuals, statistics, and actionable information to your customers. None of them, by themselves, will derail the mission.
What happens frequently is the customer (department, division, etc.) has their favorite horse in this race and you (IT) have the “company standard”. If you are lucky, they are one and the same – great, move on. If not, then quickly come to the understanding that picking the “best” BI tool is like picking the best paint color for your car – it’s personal, not performance, related. As long as you go with one of the market leaders, you will be fine (a vocado is not a car color, sorry).
Algos rule the day. Dashboards are nice and scorecards are cool, but we are well beyond that when we talk advanced. Think scoring algorithms, predictive models, and automated learning. If you are really looking to transform Mega Corporation and impress customers, deploy these bad boys to the edge so that they have a real financial impact.
Think about it. Much of what delights (frightens) us about Google and Amazon is their ability to know what we mean or want at any given moment. They’ve long understood that having the best data (see point #1 above) and combining it with the smartest algorithms (models) can often lead to competitive advantage and customer wonderment. Imagine when you deliver algorithms that increase sales margins, improve employee retention, and discover new markets? Yeah, take a bow.
So, don’t burn your old analytics playbook – there’s a lot of good stuff there that still works today. Just modify the recipe a bit to ensure that you are bringing your advanced game.
This blog was originally published on LinkedIn