Success starts with leadership who are willing to place an emphasis on data and analytics.
Crafting an Effective Analytics Strategy
We like to think that the pivotal point in the movie “Moneyball” – which may forever be the reference for data in baseball – is when Peter Brand (Jonah Hill) corners Billy Beane (Brad Pitt) in a parking garage to show what data can mean for the game. The hard numbers are a shining light in the darkness, but the whole thing would’ve ended if management (Beane/Pitt) had simply walked away.
Senior executives must make analytics a primary focus because that sets the entire foundation with the resources, time and talent required to run a top-notch analytics program. Leadership that is willing to back analytics helps anyone acquire the right people with diverse analytical specialties.
Support means acquiring talent in all areas of analytical modeling builds a team that challenges each other to get better – consider this them a key to your success.
Technology and the training to keep up with innovation are also a significant but important piece of the puzzle. Team members will need enough time to determine the right tools and skills for the job, and then the ability to hone talent in those tools. Having deep knowledge of the three best tools is more important than having fifteen tools without knowing how to use them or when to deploy them.
All of this comes full circle with the third most important ingredient in an effective analytics strategy: communication. Analytics teams need to create relationships with management and departments to learn about pain points, needs and expectations. Communication with management about processes and honest expectations ensures the long-term viability of an analytics program.
For communicating with your management, certain tactics will improve your odds of success. These don’t change the fundamental benefits of analytics, but shape the conversation into something management can understand and appreciate it. Use these three tools to turn a conversation about numbers into a discussion about business benefits:
- Be concise.
- Speak the language of your business, touching on success that is relevant and ensuring that proposals acknowledge the realities of your industry.
- Rely on existing partnerships. Every department head who can back up your capabilities with a success story helps clarify overall value. These small wins can create an opportunity for a groundbreaking undertaking.
Early communication is perhaps the most important because it guides future investment. It allows your analytics team to find low-hanging fruit for initial work and initial success. Nailing the first project is a way to tell leadership that the investment was a smart one.
Decide What to Measure for Early Success
Sometimes, the hardest part of an analytics effort is figuring out where to start. Analytics is a business decision so it must conform to business requirements to generate investment at most firms. That requires a clear definition of objectives and success.
No matter the size of your analytics program, from kickoff to long-term investment, there are four ways to decide what to measure and ensure you evaluate it properly:
- Define your objective, including what the baseline is and what success looks like to your business. You’re not picking the outcome – because that can lead scientists down a wrong path – but more of a way to decide whether or not something worked.
- Develop a theory of cause and effect to assess presumed drivers of the objective. This is where your talent really comes into play. They need to be able to measure what’s going on and, in the end, determine causation versus correlation.
- Identify specific activities that employees can do to help meet the objective. These changes and their outcomes are what you’re testing and measuring. This is how analysis drives internal development.
- Evaluate your statistics relative to the outcome and business models. Again, the strength of your analysis depends on your team. The broader the discipline base, the more startling patterns that may emerge.
Data works in the real world, too.
Amazon, for example, uses Big Data analytics to get customers to love the shopping experience. Every suggested item you see while shopping is based on thousands of other people who fit a similar profile and each customer service request comes with a mountain of data behind it.
They try to know you, your preferences and then predict what you’ll want or need next. And it works, as their volumes and quarterly statements continue to show.
Analytics can also grow to solve problems that look too complex to tackle. As we know in Texas, flooding hurts the economy. More than half of the Netherlands is at risk of flooding each year, so it pays roughly €1 billion each year to maintain 2,200 miles of dikes. The government wanted to increase the protection these dikes offered and considered a uniform 10x increase for every area – which comes with a significant price and would impact businesses and water systems at every single point.
A 2010 analytics program was able to look at decades of flood data and determine what areas were most at risk and what the proper response was for each risk zone. All-in-all, higher flood safety measures were met, and the 10x increase was only necessary in three points. The Dutch saved €8 billion thanks to analytics.
Adopting an analytics platform is an easy way for most brands to start seeing early, easy wins; like preseason games used to drive interest and get fans excited. Real wins come from longer exposure and reliance. When analytics is a core of your operations – when it is so ingrained it feels like your company pastime – then you open up doors to opportunities and stats we’ve yet to think up.