ENTRIES TAGGED "Data Science for Business"
Focusing attention on the present lets organizations pursue existing opportunities as opposed to projected ones
Slow and Unaware
It was 2005. The war in Iraq was raging. Many of us in the national security R&D community were developing responses to the deadliest threat facing U.S. soldiers: the improvised explosive device (IED). From the perspective of the U.S. military, the unthinkable was happening each and every day. The world’s most technologically advanced military was being dealt significant blows by insurgents making crude weapons from limited resources. How was this even possible?
The war exposed the limits of our unwavering faith in technology. We depended heavily on technology to provide us the advantage in an environment we did not understand. When that failed, we were slow to learn. Meanwhile the losses continued. We were being disrupted by a patient, persistent organization that rapidly experimented and adapted to conditions on the ground.
To regain the advantage, we needed to start by asking different questions. We needed to shift our focus from the devices that were destroying U.S. armored vehicles to the people responsible for building and deploying the weapons. This motivated new approaches to collect data that could expose elements of the insurgent network.
New organizations and modes of operation were also required to act swiftly when discoveries were made. By integrating intelligence and special operations capabilities into a single organization with crisp objectives and responsive leadership, the U.S. dramatically accelerated its ability to disrupt insurgent operations. Rapid orientation and action were key in this dynamic environment where opportunities persisted for an often unknown and very limited period of time.
This story holds important and under appreciated lessons that apply to the challenges numerous organizations face today. The ability to collect, store, and process large volumes of data doesn’t confer advantage by default. It’s still common to fixate on the wrong questions and fail to recover quickly when mistakes are made. To accelerate organizational learning with data, we need to think carefully about our objectives and have realistic expectations about what insights we can derive from measurement and analysis.
Business users are starting to tackle problems that require machine-learning and statistics
I talk with many new companies who build tools for business analysts and other non-technical users. These new tools streamline and simplify important data tasks including interactive analysis (e.g., pivot tables and cohort analysis), interactive visual analysis (as popularized by Tableau and Qlikview), and more recently data preparation. Some of the newer tools scale to large data sets, while others explicitly target small to medium-sized data.
As I noted in a recent post, companies are beginning to build data analysis tools1 that target non-experts. Companies are betting that as business users start interacting with data, they will want to tackle some problems that require advanced analytics. With business analysts far outnumbering data scientists, it makes sense to offload some problems to non-experts2.
Moreover data seems to support the notion that business users are interested in more complex problems. I recently looked at data3 from 11 large Meetups (in NYC and the SF Bay Area) that target business analysts and business intelligence users. Altogether these Meetups had close to 5,000 active4 members. As you can see in the chart below, business users are interested in topics like machine learning (1 in 5), predictive analytics (1 in 4), and data mining (1 in 4):
What business leaders need to know about data and data analysis to drive their businesses forward.
A couple of years ago, Claudia Perlich introduced me to Foster Provost, her PhD adviser. Foster showed me the book he was writing with Tom Fawcett, and using in his teaching at NYU.
Foster and Tom have a long history of applying data to practical business problems. Their book, which evolved into Data Science for Business, was different from all the other data science books I’ve seen. It wasn’t about tools: Hadoop and R are scarcely mentioned, if at all. It wasn’t about coding: business students don’t need to learn how to implement machine learning algorithms in Python. It is about business: specifically, it’s about the data analytic thinking that business people need to work with data effectively.
Data analytic thinking means knowing what questions to ask, how to ask those questions, and whether the answers you get make sense. Business leaders don’t (and shouldn’t) do the data analysis themselves. But in this data-driven age, it’s critically important for business leaders to understand how to work with the data scientists on their teams. In today’s business world, it’s essential to understand which algorithms are used for different applications, how statistics are used to create models of human and economic behavior, overfitting and its symptoms, and much more. You might not need to know how to implement a machine learning algorithm, but you do need to understand the ideas the data scientists on your team are using.
The goal of data science is putting data to work. That’s what Data Science for Business is all about, and the reason I’m excited to see us publishing it. There are many books about data science, and an increasing number of undergraduate and graduate programs in data science. But I haven’t seen anything that teaches data science for the leaders who will be using data to drive their businesses forward.