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Realizing the value of analytics and big data is as much about culture as it is about the technology used to capture and analyze information.
By Howard Alexander, Director, Business Systems Data Warehousing, Boeing
Here at Boeing we’re confronting the same challenge that countless other companies in a variety of industries are facing: how to take all the data we’re capturing and turn it into usable information. We’re collecting more data than ever before, and we’ve had to figure out how to change our employees from simple data gatherers and reporters into information users who create business value.
We’ve been on this journey for several years. For a large corporation like ours, this level of change takes time. The first step was to take a hard look at the information that we’re gathering to figure out the business value it might deliver.
It’s sexy today to focus on the Internet of Things and how to use the new data we’re getting from sensors embedded in machines to come up with new products and processes. After all, our Boeing 787s create half a terabyte of data per flight. But there’s much more immediate value we can get from the information locked inside the data we’ve been gathering for years. For us, that meant rethinking how we use the data generated by our Finance, Supply Chain, and Program Management functions.
Starting at the End
Our goals had been to evolve from reacting to events to using business information to act before something happens — or even to anticipate a particular outcome in order to improve results. And that is as much, or more, about culture than it is about the technology you use to capture, analyze, and use that information. We want to increase the number of decisions throughout the business where the analysis of information is the primary driver of our actions.
The first step in being able to do that is to make sure you understand the problem you are trying to solve. By starting at the end of the process, everything becomes less difficult. You can’t take a “build it and they will come” approach. You have to work backwards and know what success will look like in order to select the tools and platforms that will enable it. If you don’t, you end up throwing a lot of different technologies at problems in an attempt to solve them.
That has to start with the business owners. They have to be the ones to articulate what success truly is and the value they want to derive from the data. They have to tell you how they want to measure success. They have to be the ones who buy in to the technologies and processes and then market them to the larger organization.
The goal here is to embed the analytics as close to the user of the data as possible. Why? So that it’s actionable.
Our functional areas were in different states of maturity in terms of being able to capitalize on the business value of data and analytics. It’s been beneficial to us to start with the one or two areas that had the clearest understanding of what success would look like for them. That has made it much easier to deliver something that not only reaps rewards for that area but also serves as an example to the rest of the organization of what we can achieve with fact-based decision making. If you can achieve that business value with a fairly quick turnaround, you have a model that you can use in areas where it might take a little more effort.
Self-Service and Sharing
Another primary goal of this long-term transformation is to create an environment that enables as much self-service analytics and reporting as possible. We want everyone to become a value integrator.
That means we want to push the analytics further out — as close to the end consumer of the information as possible via whatever platform is required, whether that’s a laptop, PC, or mobile device. We want to be able to deliver insights that trigger action. We don’t want our employees waiting for someone else to present them with a report, the way it happens with a traditional decision support system. We want people to be able to see real-time data streaming in and respond to it.
It has been an iterative process: You experiment. You find things that work in the culture, and you find things that don’t work. You have to be willing to be a learning organization, and as you see patterns that work, you adopt them. That’s especially true in the area of analytics, which is a rapidly evolving space. The analytics and big data tools change every year, if not more often. You have to be flexible and create an architecture that enables you to get the best out of what’s out there and what’s to come.
One thing that has worked is to have people share the tools and reports they’ve created with peers and colleagues. That is helping us create an environment where folks are accustomed to developing their own analytics and sharing their efforts. Part of the way we do that is to employ some analytics ourselves. We’re always looking at the types of information our business users are asking questions about. We have a group of folks who then meet with user groups to share reports and analytics that have already been developed in those areas. That way, we’re using systems that have been vetted for both accuracy and performance. If you’re looking for x, you’re interested in this analytics and report.
Managing Data Demands
We’re a very large organization, so creating an environment that can handle the increasing demand for analytics has been critical. We can’t be overwhelmed. From a human resources standpoint, we have to make sure we have the right type of training and support systems to help our employees harness the power of analytics on their own. We are enabling people to embrace new tools and new ways of thinking. We’re creating communities of practice that help our culture become a fact-based one.
We’re also looking at strategies for managing the IT infrastructure, which serves as the backbone for all the data and analysis. When we’re talking about real-time and predictive analytics, performance is critical. We’re currently exploring cloud strategies that will give us additional capacity when we need it.
Certainly, we’re getting increased efficiency in terms of traditional business intelligence reporting of what has already happened. Our primary goal, however, is to enable our employees to take action while they still have the chance to influence outcomes. Better outcomes are the measure of our success. We’re also exploring how we can predict probable outcomes based on patterns we’ve observed in the past. And we continue to work with those goals in mind.
The key is to start with the end in mind. I cannot say that enough.
Originally published in CIO Straight Talk, No. 6 (February 2015)