Most people in the tech world know the hype cycle all too well. A new technology enters the marketplace amid great expectations. Inevitably, disappointment sets in and a retrenchment period begins, practice and process catch up to expectations and new value is unleashed.
Right now, there is probably no area more hyped than big data and there’s already no shortage of self-proclaimed experts. Yet most big data efforts fail and there is a growing divide between enterprises that are benefiting from its use and those who are not.
There are a variety of reasons for this—a lack of qualified data scientists, poor integration across departments and a failure to manage expectations all play a part. Yet for those who have built a big data culture, the investment is already paying off. They key lies not with fancy algorithms or buzzwords, but by focusing on real world problems.
Replacing Routine Maintenance With Predictive Analytics
UPS runs one of the largest logistics operations in the world, delivering millions of packages every day. If one of their trucks has even a minor breakdown, it can be a big deal, resulting in driver downtime, late packages and angry customers.
So UPS used to replace important parts every few years to ensure that its vehicles stayed in good working order. Now, however, they collect data from hundreds of sensors in each vehicle. Then algorithms analyze that data from thousands of trucks to predict when a part is likely to break down, allowing UPS to save millions in maintenance costs.
IBM used similar algorithms to help the city of Boston to reduce municipal costs by predicting when repairs are likely to be needed. They even went a step further in Rio, where big data systems not only anticipate when deadly landslides are likely to happen, but are integrated with a next-generation operations center that helps coordinate response.
This is all possible because, with enough data, patterns begin to emerge that can detect anomalies. By combining smart algorithms with the Web of Things, we can both cut costs and increase operational effectiveness.
The New Consumer Conversation
Marketers have long known how important it is to have your finger on the pulse of the consumer. However, in the past, it’s been a fairly crude process. You either performed basic surveys with pre-canned answers that customers would check off or had to read genuine responses one by one.
Now big data is enabling a new consumer conversation that uses natural language processing to read and evaluate consumer responses. For example, Semantria worked with Schwan’s frozen foods to evaluate thousands of responses and understand what their customers really thought about them.
The USTA took an altogether different approach to speak to their audience at the recent US Open. They used big data analytics to analyze 41 million data points over 8 years of Grand Slam play and created a Slam Tracker that would instantly generate “keys to the match” so that fans could see what their favorite player had to do to win.
And the NBA recently launched a similar effort, opening up its statistical database and corresponding video library to all of their fans for free. We often think about big data as a purely analytical effort, but sometimes it can be most useful improving communication and transparency.
Improving Public Safety
We like to see police out on the street. They make us feel safe by keeping watch, responding quickly and intervening when trouble comes along. Yet they often get stuck doing grunt work and shuffling papers. Although important, these things don’t make us feel all that much safer. Big data is making an impact here as well.
One of the earliest systems was the Memphis Police’s Blue CRUSH system, which reduced crime by more than 30% and was so effective that when funding was cut, it created a controversy. More recently, IBM started a research partnership in Ft. Lauderdale, FL that will help analyze data across city departments.
This opens up a whole new world of possibilities. Do new building permits—and the construction that follows—increase theft? How will changing demographics in different areas of the city affect crime rates? Does an increase in requests for public assistance correlate with an increase in crime?
And we’re only starting to scratch the surface. Semantria, recently tuned it’s language processing algorithms to recognize street slang in Detroit. By combining chatter gleaned from social media with predictive crime analytics, police departments can become even more effective.
Making Big Data Work
While all of the success stories are impressive, what we don’t hear much about are the big data failures—the big initiatives with big budgets that amount to little or nothing at all. Big data is no panacea, its a tool and much like any other, it needs to be used intelligently. Here’s how you do that:
Start With A Problem: Probably the worst thing you can do is to create a highly publicised “big data initiative.” Much like any other technology, big data works best when it’s invisible to the end users, but helps them do their jobs nonetheless.
One thing that all of the use cases described above have in common is that they started with a problem to solve. UPS and Boston wanted to lower maintenance costs, Rio and the other cities wanted to improve public safety, Schwan and the USTA wanted to talk to consumers. Every good project starts with a problem, not a solution.
Focus On The Core: Humans are very bad information processors. Nevertheless, we spend a lot of our time performing informational tasks. Poring over documents and shuffling papers is simply not a good use of people’s time. Big data can free up those tasks so that front line personnel can focus on their core mission.
As Mark Cleverley, Global Director of Public Safety at IBM puts it, “We are moving into an era where we can be roughly right more frequently and precisely wrong less. Narrowing down possibilities helps us to make the data to be actionable.”
In other words, big data is most effective when it is aimed at improving people’s ability to do their jobs.
Increase Visibility Across The Enterprise: Another advantage to big data is that it can help the entire enterprise work as one functional unit. There is no longer any need for data silos for different functions such as marketing, finance, logistics, etc. Big data techniques allow us to all work from the same data set and pull out what we need.
Build Collaboration: In most organizations, there are subject matter experts who are able to generate insights and front line personnel who are responsible for delivering a product or service. One of the great management challenges is helping one group communicate effectively with the other.
That’s why IBM’s Cleverley sees a big part of his job as “bringing analytical capabilities to much broader class of people who are not specialists.” By letting giving people at the point of service access to expert level insights, the entire organization benefits.
And that’s the secret to success. The best way to approach big data is not to try to build a better system, but to build a better enterprise.