By using website you agree to our use of cookies as described in our cookie policy. Learn More


Q&A: Lessons Learned from Enterprise Analytics

Three IBM analytics experts examine how IBM has leveraged the technology in enterprises around the world. In this interview, they explore the lessons learned and challenges BI professionals face in their analytics journey.

[Editor's note: Brenda L. Dietrich, Emily C. Plachy, and Maureen F. Norton are the co-authors of Analytics Across the Enterprise: How IBM Realizes Business Value from Big Data and Analytics. They spoke to TDWI about their book, what mistakes organizations make (and what lessons they learned) in leveraging big data.]

BI This Week: In a nutshell, what is your book about?

Emily C. Plachy and Maureen F. Norton: Our book tells the compelling inside story of how IBM transformed into a smarter enterprise using analytics as a way of doing business. Getting value from data and analytics enterprise-wide is a journey. To help readers find IBM stories they can use in their journeys, we organized the book by business function, such as human resources, marketing, sales, and supply chain. The stories are from IBM, but they apply to many businesses. The first chapter provides an overview of big data and analytics, explains the importance of using data and analytics to solve business problems and improve business performance, and discusses proven approaches and how to get started.

What's the biggest mystery enterprises face as they begin their analytics journey?

Emily: One mystery that enterprises face is how to get started with analytics. The answer is to start with your most important problem. Next, ask questions to help refine what you want to learn, then look for data than can help answer the questions. Form a team to prepare the data and create the analytics model. The team should be composed of an experienced data scientist, someone experienced in the business area, and an IT person skilled in analyzing the data. Work incrementally on the data and iterate on the model.

Maureen: Another mystery enterprises face is how to cultivate an analytics culture within an enterprise to use the analytics insights to improve an outcome. If analytics are done for analytics sake, it is "just fun math" and won't drive outcomes. You have to use the insights from the analytics to do something different than you otherwise would have done -- for example, changing a strategy, decision, or behavior.

Brenda L. Dietrich: A third mystery involves measuring the impact of analytics, especially when the deployment of analytics is coupled with changes in the business process or flow of information. Applying the analytics to historical data, and comparing the impact of the recommended decisions to the outcome of the actual decisions can be useful in building a business case for deploying analytics. The use of a carefully controlled pilot deployment, for example, for a single line of business or geography, can enable measurement of impact, as some organizations will be taking action based on analytics while others will not.

What are the three biggest mistakes organizations make in their transformation to leverage data and analytics?

Brenda and Emily:

1. Failing to prepare for deployment by preparing the analytic methods to serve the end user in his/her job. To realize value, people must use analytics results to drive decisions and actions. Early in the life cycle, target user should buy in to the solution and be ready to use it.

2. Spending time to understand and prepare data without being driven by a business problem; teams assuming that "If we build it, they will come." Instead, let the business needs drive the order in which data is understood and prepared.

3. Working to create a perfect solution in one step -- analytics teams debating amongst themselves rather than focusing on the end users' needs. Delivering analytics solutions incrementally has several advantages, including helping target users buy in to the solution and allowing people to use increments that provide insight they did not have before, allowing them to make better decisions.

Why did you write this book at this time? In other words, what's so special about analytics today?

Brenda: IBMers are often interviewed for books or articles on analytics. After all, we provide many of the tools in this space. Additionally IBMers have written articles and given conference presentations about specific analytics projects, but there was no single place where an interested reader could see the range and variety of the work. We had developed a customer-focused presentation about IBM's use of analytics to support its transformation. In the presentation we saw the makings of a book, so we decided to tell the story, or at least the story so far.

What "Aha!"s or lessons learned came from the case studies in your book?

Maureen: The case studies demonstrate that analytics is a way of doing business and not just a technology. Insights from the analytics have to be embedded into existing business processes to have an impact and transformation is itself a process, not a project. One finance person we interviewed summed it up best. When asked about the transformation, the person said, "We will never be done."

Brenda: Another "Aha!" was that creating a learning, adapting organization requires analytics. The organization has to keep track of what is known (data), how it is interpreted and acted upon (analytics), and both the expected outcome (from the analytics) and the actual outcome (from new data). This new data describing how the organization works, then becomes a part of what is known.

Emily: A lesson from the book is that it is not necessary to understand how an analytics technology works to get value from it. You do need to learn how to use an analytics solution effectively, but it is not necessary to understand the inner workings.

What did you find most compelling in writing this book?

Maureen: What I found most compelling is the breadth of challenges that analytics can help solve. For example, reliable studies indicate that between 50 and 70 percent of merger and acquisition deals fail. M&A decisions are a pure example of a decision process because they are usually in the hands of a few smart leaders who make high-stake decisions with a limited amount of input because the deals have to be kept close to the vest.

IBM developed a Mergers and Acquisition solution to help identify potential acquisitions and the supporting integration needed to support IBM's growth strategy. IBM invested $39 billion in acquisitions from 2000 to 2013, with $17B from 2005 to 2013 on business analytics and optimization acquisitions. The analytic solution identified 18 key attributes that are used to assess potential acquisitions. The model develops the information and the expert user adds the subject matter expertise and provides advice for the business. IBM's acquisition portfolio performance is ahead of the industry which contributes directly to the growth strategy.

Brenda: In all of the cases presented in the book, communication played a vital role. The team providing the analytics communicated with data providers, executive sponsors, end users, and other stakeholders from the inception of the project through its completion. Both end users and executive sponsors communicated with peers in other organizations, which often led to the spread of analytics. Analytics providers shared methods and data experiences with one another, leading to the sharing of methods, code and data. Through this book IBMers will share their analytics stories with each other and with our clients.

Emily: I found the importance of cognitive computing in analyzing vast amounts of data compelling. Cognitive computing is a new form of computing characterized by computing systems which sense, learn, reason, and interact with people. Jeff Jonas, one of our IBM Fellows, has a powerful quote: "The most competitive organizations are going to make sense of what they are observing fast enough to do something about it while they are still observing it." Cognitive computing is coming just in time to allow us to act in time by providing visualization of big data insights based on our questions, by helping us explore data and uncover insights, and by helping us detect anomalies – and this is only the beginning.

Who should read your book?

Maureen: This book should be read by current and future business leaders and students who want to boost their careers and stay on the leading edge. For current business leaders, the recommendations in the book inspire a can-do approach to getting started or accelerating their journey. Every business has challenges and the approach outlined shines a light on new problem-solving approaches that are proven -- the book contains real-world examples, lessons learned, and a way to think about adding more science to decision processes to drive outcomes.

For students, this book will give them a competitive advantage in their studies and career. Understanding how a business can leverage big data and analytics along with a view into the future of cognitive computing will help them differentiate themselves in the job market. For example, one lesson in the book from the finance chapter is that the people who are best at unlocking the real potential in applying analytics are not the pure-play finance person or the pure-play analytics person but the individuals who have an understanding of each. The recommendation is to take the time to learn how to build a hypothesis, mine data, visualize the data, and apply creative thinking to solve the business problem as it will be worth the investment.

Brenda: Data scientists and others who work in the field of analytics may also enjoy the book. Although it won't divulge any new algorithms or models, it may give them ideas on new areas to which analytics can be fruitfully applied.

TDWI Membership

Get immediate access to training discounts, video library, research, and more.

Find the right level of Membership for you.