Recognizing Baseline Data is Not Enough
An Excel document—flat numbers on a page—won’t cut it in 2020, no matter how great your Excel skills are. Because of the vast amounts of data, and the ability to make complex connections from it, marketing teams need to learn to augment their data with machine learning and AI. The flat data—baseline data—is a starting point. But the value comes in making active observations, finding patterns, and developing new queries based on the connections found. Oh, and those connections need to be made quickly. Like—right now.
If you’re looking at your budget and freaking out, don’t worry. While demand for real-time analytics has increased, the cost of in-memory processing has also gone down, making it more accessible for more companies. This year, don’t just focus on gathering more data. Focus on gathering the tech that will help you crunch it most meaningfully.
Know the Great Value Comes in Prediction, Not Description
This isn’t exactly a new trend, but moving forward, predictive analytics will be a basic requirement of any successful marketing team—not a nice-to-have for the rich and famous. More and more, it’s essential that teams focus not just on “where we are” but “where are we going?” What do consumers want to buy? When? What do they need that no one has yet given them? Finding those niche opportunities are the way to take the lead in your market sector. And with advancements in AI and machine learning, noted above, those predictions will only become more accurate and more powerful.
Invest in Graph Analytics
Not everyone is a numbers person. With data paving the way for much of the decision-making happening across the enterprise, it’s essential that employees find quick, easy ways to make meaningful connections with the data they’re receiving. One of the ways to do that: graph analytic. Graph analytics will better help your team understand complex connections between people, customers, places, times, and things—without overwhelming them with numbers. Graph analytics can also be especially helpful with things like scenario planning and risk management—big issues with lots of moving parts and lots to lose or gain.
Use Analytics for Lifecycle Management
Lifecycle management is key in terms of product development, and the smartest marketing teams will be using AI and machine learning to optimize their processes at scale, from app development and testing, to launch, support, and recovery.
On the flip side, no algorithm was meant to last forever, no matter how well it worked for your marketing team. As we head into the next decade, we’ll see better rules of engagement in terms of lifecycle management of analytics from development to testing to back up to recovery. Which works? When do they need to be reworked? How do we make sure that algorithms and coding don’t go the way of the data swamp? Dirty data costs the United States $3.1 trillion a year! How much does bad AI cost?
Natural Language Processing
Again, not all of us are not numbers people. Luckily, the tech powers that be understand that. In the future we’ll see an increasing ability to run queries by voice command, which will make things especially easy for those marketing teams who know what they want to find out—but don’t know which metrics to use to get there.
Remember: this is no magic bullet in marketing. Analytics are a great tool, but even the best numbers are meaningless if you don’t have a plan for executing your discoveries or keeping your data up to date. Moving ahead, the amount of data flying at you from all angles is only going to increase as the Internet of Things gains steam. The main point to remember is that being a data-driven marketing team is no longer an option—it’s a necessity.
The original version of this article was first published on Forbes.