Analytics Across the Enterprise: Analytics transforms a ‘dinosaur’
The story of how IBM not only survived but thrived by realizing business value from big data.
By (l-r) Brenda Dietrich, Emily Plachy and Maureen Norton
This is the story of how an iconic company founded more than a century ago, and once deemed a “dinosaur” that would not be able to survive the 1990s, has learned lesson after lesson about survival and transformation. The use of analytics to bring more science into the business decision process is a key underpinning of this survival and transformation. Now for the first time, the inside story of how analytics is being used across the IBM enterprise is being told. According to Ginni Rometty, chairman, president and chief executive officer, IBM Corporation, “Analytics is forming the silver thread through the future of everything we do.”
What is analytics? In simple terms, analytics is any mathematical or scientific method that augments data with the intent of providing new insight. With the nearly 1 trillion connected objects and devices generating an estimated 2.5 billion gigabytes of new data each day, analytics can help discover insights in the data. That insight creates competitive advantage when used to inform actions and decisions.
Data is becoming the world’s new natural resource, and learning how to use that resource is a game changer.
Analytics is not just a technology; it is a way of doing business. Through the use of analytics, insights from data can be created to augment the gut feelings and intuition that many decisions are based on today. Analytics does not replace human judgment or diminish the creative, innovative spirit but rather informs it with new insights to be weighed in the decision process.
Analytics for the sake of analytics will not get you far. To drive the most value, analytics should be applied to solving your most important business challenges and deployed widely. Analytics is a means, not an end. It is a way of thinking that leads to fact-based decision-making.
|This article is adapted from the book, “Analytics Across the Enterprise: How IBM Realizes Business Value from Big Data and Analytics.”|
Big Data and Analytics Demystified
If analytics is any mathematical or scientific method that augments data with the intent of providing new insight, aren’t all data queries analytics? No. Analytics is often thought of as answering questions using data, but it involves more than simple data
(or database) queries. Analytics involves the use of mathematical or scientific methods to generate insight from the data.
Analytics should be thought of as a progression of capabilities, starting with the well-known methods of business intelligence, and extending through more complex methods involving significant amounts of both mathematical modeling and computation.
Reporting is the most widely used analytic capability. Reporting gathers data from multiple sources, such as business automation, and creates standard summarizations of the data. Visualizations are created to bring the data to life and make it easy to interpret.
As a generic example, consider store sales data from a retail chain. The data is generated through the point of sale system by reading the product bar codes at checkout. Daily reports might include total store revenue for each store, revenue by department for each region, and national revenue for each stock-keeping unit (SKU). Weekly reports might include the same metrics, as well as comparisons to the previous week and comparisons to the same week in the previous calendar year. Many reporting systems also allow for expanding the summarized data into its component parts. This is particularly useful in understanding changes in the sums.
For example, a regional store manager might want to examine the store-level detail that resulted in an increase in revenue from the home entertainment department. She would be interested in knowing whether sales increased at most of the stores in the region, or whether the increase in total sales resulted from a significant sales jump in just a few stores. She might also look at whether the increase could be traced back to just a few SKUs, such as an unusually popular movie or video game. If a likely cause of the sales increase can be identified, she might alert the store managers to monitor inventory of the popular products, reposition the products within a store, or even reallocate inventory of the products across stores in her region.
Why Analytics Matter
Quite simply, analytics matters because it works. You can be overwhelmed with data and the value of it may be unattainable until you apply analytics to create the insights. Human brains were not built to process the amounts of data that are today being generated through social media, sensors, and more. While gut instinct is often the basis for decisions, analytically informed intuition is what wins going forward.
Several studies have highlighted the value of analytics. Companies that use predictive analytics are outperforming those that do not by a factor of five. In a 2012 joint survey by the IBM Institute of Business Value and the Said Business School at the University of Oxford of more than 1,000 professionals around the world, 63 percent of respondents reported that the use of information (including big data and analytics) is creating a competitive advantage for their organizations. IBM depends on analytics to meet its business objectives and provide shareholder value. The bottom line is that analytics helps the bottom line. Your competition will not be waiting to take advantage of the new insights from big data. Should you?
IBM has approached the use of analytics with a spirit of innovation and a belief that analytics will illuminate insights in data that can help improve outcomes. The company hasn’t been afraid to make mistakes or redesign programs that haven’t worked as planned. Unlike traditional IT projects, most analytics projects are exploratory. For example, the Development Expense Baseline Project explored innovative ways to determine development expense at a detailed level, thereby addressing a problem that many thought was impossible to solve. IBM analytic teams haven’t waited for perfect data to get started; rather, they have refined and improved their data along the way.
The key is to put a stake in the ground with a commitment that analytics will be woven into your strategy. That’s how IBM does it. This approach is also effective with big data. Rather than postpone the leveraging of big data, you should embrace it, establish a link between your business priorities and your information agenda, and apply analytics to become a smarter enterprise.
Staying focused on solving business problems was the pragmatic start, and the other crucial element was having very high-level executive support from the beginning. From a governance perspective, those are two key levers to drive value: focus on actions and decisions that will generate value and have high-level executive sponsorship.
The ideal team to do analytics is a collaboration between an experienced data scientist, a person steeped in the area of the business where the challenge needs to be solved, and an IT person with expertise in the data in that particular area of the business.
A joint study by MIT Sloan and the IBM Institute for Business Value developed several recommendations. The first is that you start with your biggest and highest-value business challenge. The next recommendation is to ask a lot of questions about that challenge in order to understand what’s going on or what could be going on. Then you go out and look for what data you might have that’s relevant to that challenge. Finally, you determine which analytic technique can be used to analyze the data and solve the problem.
Because most companies have constraints on the amount of money and skills available for projects, estimating the ROI can provide a better differentiator for selecting the project with the highest potential impact than relying on instincts. Estimating an analytics project’s ROI involves both capturing the project costs and measuring the value.
Relationships inferred from data today may not be present in data collected tomorrow. The relationships that you infer from data about the past do not necessarily hold in data that you collect tomorrow. You cannot analyze data once and then make decisions forever based on old analysis. It’s important to continually analyze data to verify that previously detected relationships are still valid and to discover new ones. Fortunately, major discontinuities with data do not happen very often, so change generally happens gradually. Social media sentiment, however, has a much shorter half-life than most data.
Using relationships derived from past data has been repeatedly demonstrated to work better than assuming that no relationships exist. The relationships that have been detected are likely correlation rather than causality. However, these relationships, if detected and acted upon quickly, may provide at least a temporary business advantage.
You don’t have to understand analytics technology to derive value from it. For a long time, many business leaders expressed the opinion that mathematics should be used by only those who understood the details of the computations. However, in recent years this view has been changing, and analytics is being treated like other technologies. You must learn how to use it effectively, but it is not necessary to understand the inner workings in order to apply analytics to business decisions. You have to apply analytics methods in the context of the problem that is being solved and make the results accessible to the end user. But just as the user of a car navigation system does not need to understand the details of the routing algorithm, the end user of analytics does not have to understand the details of the math.
Typically, making the results accessible to the end user involves wrapping the math in the language and the process of the end user. Also, the analytics can be embedded deep inside things so that the user does not see it, like in supply chain operations. Analytics should be usable by anyone, not just those with Ph.D.s in statistics or operations research. Some users will want to understand the algorithms and inner workings of an analytics model in order to trust the results prior to adoption, but they are the exception.
Fast, cheap processors and cheap storage make analysis on big data possible. Moore’s law has resulted in vast increases in computing power and vast decreases in the cost of storing and accessing data. With readily available and inexpensive computing, we can do what-if calculations often and test a number of variables in big data for correlation.
Doing things fast is almost always better than doing things perfectly. Often inexact but fast approaches produce enormous gains because they result in better choices than humans would have made without the use of analytics. Over time, the approximate analytics methods can be refined and improved to achieve additional gains. However, for many business processes, there is eventually a point of diminishing returns: The calculations may become more detailed and precise, but the end results are no more accurate or valuable.
Using analytics leads to better auditability and accountability. With the use of analytics, the decision-making process becomes more structured and repeatable, and a decision becomes less dependent on the individual making the decision. When you change which people are in various positions, things still happen in the same way. You can often go back and find out what analysis was used and why a decision was made.
Dr. Brenda L. Dietrich is an IBM Fellow and vice president. She joined IBM in 1984, and during her career she has worked with almost every IBM business unit and applied analytics to numerous IBM decision processes. She currently leads the emerging technologies team in the IBM Watson group. For more than a decade, she led the Mathematical Sciences function in the IBM Research division, where she was responsible for both basic research on computational mathematics and for the development of novel applications of mathematics for both IBM and its clients.
In addition to her work within IBM, she has been the president of INFORMS, the world’s largest professional society for operations research and management sciences. An INFORMS Fellow, she has received multiple service awards from INFORMS.
Dr. Emily C. Plachy is a distinguished engineer in Business Analytics Transformation at IBM, where she is responsible for leading an increased use of analytics across IBM. Since joining IBM in 1982, she has integrated data analysis into her work and has held a number of technical leadership roles including CTO, process, methods, and tools in IBM Global Business Services.
In 1992, Emily was elected to the IBM Academy of Technology, a body of approximately 1,000 of IBM’s top technical leaders, and she served as its president from 2009 to 2011. She is a member of INFORMS.
Maureen Fitzgerald Norton, MBA, JD, is a distinguished market intelligence professional and executive program manager in Business Analytics Transformation, responsible for driving the widespread use of analytics across IBM. In her previous role, she led project teams applying analytics to IBM Smarter Planet initiatives in public safety, global social services, commerce and merchandising.
Norton became the first woman in IBM to earn the designation of Distinguished Market Intelligence Professional for developing innovative approaches to solving business issues and knowledge gaps through analysis.
Note: This article is adapted from the book, “Analytics Across the Enterprise: How IBM Realizes Business Value from Big Data and Analytics,” authored by Brenda L. Dietrich, Emily C. Plachy andÂ Maureen F. Norton, published by Pearson/IBM Press, May 2014, ISBN 978-0-13-383303-4, ©2014 by International Business Machines Corporation. For more information, visit: ibmpressbooks.com.
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