Predictive Intelligence, Machine Learning, Artificial Intelligence, Advanced Analytics.
All these terms are gaining attention at major marketing, business, and analytics conferences. If you’re like most people, you probably walk away from these conferences feeling like your company is behind in its analytical capabilities. However, don’t let the chatter fool you. The reality is that many companies aren’t ready to digest the most advanced machine learning algorithms just yet – but that shouldn’t stop you from building a roadmap for how to get there:
Step 1: Invest in People
Data scientists, representing that rare mix of analytical expertise and business acumen that many companies are looking for today, have never been in higher demand. Hiring one of these sought-after talents to join your company is the first step to building out your analytics capabilities. However, getting the right person “on the bus,” as leadership expert Jim Collins advises in his book “Good to Great,” is difficult in itself.
The skillset required of a data scientist has evolved; not only are data scientists asked to be the creators and translators of data-driven analytics solutions for the business, they are also increasingly the conduit to technology teams. You want to look beyond purely technical skills on a resume and seek out a talent for communicating complex topics in a way that a business user can understand, as well as some understanding of the technology environments from which the data originates and in which the solutions will be deployed.
ACTION: Look for data scientists with a diverse background.
Step 2: Get Back to the Basics
In the business world, the answer isn’t always found in the newest, coolest advanced algorithms or sophisticated machine learning applications – opportunity almost always reveals itself in the basics. In analytics, the “basics” typically refers to descriptive, predictive, and prescriptive statistics. What’s the difference? TechTarget defines them this way:
- Descriptive analytics aims to provide insight into what has happened.
- Predictive analytics helps model and forecast what might happen.
- Prescriptive analytics seeks to determine the best solution or outcome among various choices, given the known parameters.
Think of descriptive, predictive, and prescriptive modeling as related tools in your data science toolbox; building off of these initial analytics techniques creates a solid foundation of knowledge to move to the next level in data science. That next level may very well involve more complex machine learning – but start with the basics.
ACTION: Frame a problem in terms of descriptive analytics, which may very well answer 95 percent of a business stakeholder’s questions right away.
Step 3: Organize Data in an Actionable Format
Data science is much more than cleaning and transforming data, running queries, and writing code, although there is a lot of that too. It’s about providing results back to your business stakeholders in a way that’s easily consumable. Spreadsheets full of numbers force business users to work through minute details of how a problem was solved when most of the time what they’re really looking for are answers and a recommendation to act. Even when the data science behind a solution is complex, you should consistently communicate the results in ways that simplify and summarize the solution in clear language. Even better, look for opportunities to deploy dashboarding and reporting software technologies to help automate and respond to some of the simpler, more commonly asked questions (e.g. what is our year-over- sales by product category?). This empowers your business stakeholders and frees up the data scientists to work on the hardest problems.
ACTION: Enable key business stakeholders to answer their own questions with data via dashboards and reporting.
Step 4: Make Data Meaningful for the Business
We know it’s important for a data scientist to be an effective translator, finding the secrets hidden within the data and translating them into language that business users can understand and act upon. This deep dive into an understanding of customer behaviors lets marketers tailor their communications, interactions, and messaging in meaningful and relevant ways. Similarly, the data scientist should work hand in hand with the business to help it understand how industry trends and consumer preferences might change in the future, and to predict which customers may be affected most by a change.
ACTION: Integrate your data scientists tightly with the business so that they can learn to identify more relevant stories arising from the analytics, and anticipate key questions from stakeholders.
The industry is excited about the possibilities that predictive intelligence can unlock. With the rise of machine learning, everyone has an eye towards harnessing the power of advanced analytics to take the guesswork out of predicting the future. While there is no doubt that these complex capabilities are change agents for how marketers and business strategists will generate insights with data in the long term, don’t overlook the value of deploying your data scientists to solve problems with more straightforward analytics – the basics. As an acquaintance of mine puts it, “Don’t be the science fair in the back room – provide actionable insights for the business.” This can only be done by getting your analytics straight.