Data is unquestionably the primary intelligence tool of global business; it offers a method by which companies can analyze the market and their position within it to develop informed strategies that will help them compete profitably. Companies have always collected data, and executives have always used that data to help them make decisions. But today managers are frequently overwhelmed with data. There is simply too much of it available to adequately process without advanced tools and methodologies. Organizations are struggling with how they can leverage this ever-increasing dataflow to their advantage.
Indeed, the sheer volume of data available today enables corporate leaders to combine and analyze it in ways that produce new insights into markets, customers, and business strategies. Global corporations are pinning their hopes on the transformational opportunities that this Big Data can purportedly unlock.
Few would doubt that the collection and analysis of the vast quantities of data now available to companies are essential to effectively and competitively run many areas of business operations. However, producing value from such expansive and amorphous data sets poses a serious challenge. Technology alone won’t solve the problem. In fact, most failed data analysis efforts derive from one or more of these three strategic errors:
1. The wrong question is asked.
2. The wrong data is used.
3. The data is treated as part of a discrete project rather than as part of an ongoing process.
Executives must recognize—and avoid—these potential pitfalls if they hope to harness the full power of the data now available to them to help realize their business objectives.
Of course, data analysis involves more than just data. It requires the appropriate industry expertise, analytical models, and enabling tools that must be integrated into a solution that surfaces specific actions and informs key decisions. Here’s one definition that captures the role of Big Data in the analysis process:
True Big Data analysis gives context to a complex set of information; applies sophisticated analytics that transform the information in ways that answer important questions on demand; and highlights new insights yielding critical information that informs big decisions and strategy.
The “complex set of information” typically relies on a large amount of data but it can also include small bits of data, discrete information sets, high-value differentiated data, and industry knowledge drawn from a number of sources. Companies are no longer limited to just curating information in their structured databases.
Data collection is just the first step in the process of utilizing data to help inform business strategy. Effectively analyzing that data involves guidance and knowledge from the right people (industry experts and analytical specialists) armed with the appropriate tools (platforms and analytical models) that can “connect the dots” between seemingly unrelated phenomena.
In our highly interconnected world, competitive advantage comes not only from the speed with which data can be analyzed, but also from how effectively the barriers between different types of information can be broken down to help establish the big picture and provide big insights.
Best practices for Big Data
Big Data analysis requires adherence to a disciplined approach to ensure the process results in clear and actionable insights. This approach can be captured with three core best practices.
1. Ask the right question, clearly.
This practice may seem obvious. Nevertheless, failure can often be traced back to a bad or unclear question. The data may be correct and the analysis flawless but, if the issue under analysis is poorly defined and the query off base, the answer suggested by the analysis may not be what the company needs to know or act on. To ask the right question, managers are well advised to employ experts with a deep understanding of the company’s industry and markets—and often related industries as well. Also needed are analysts who understand the data and analytical tools in the context of specific industries and markets and who ultimately can translate data into clear insights for industry executives.
A case in point is the work IHS did on behalf of the Panama Canal Authority. The Canal Authority initially wanted to know how the expansion of the Canal would impact its revenues. Ultimately, a series of interdependent questions emerged that broadened the analysis. If the Canal had built a model to forecast revenue based on historical trends and relationships with canal expansion, it would have missed important nuances about dynamic shifts in global trade like the impact on global trade of natural gas and oil resulting from the unconventional energy revolution unfolding in the U.S. These turning points in global trade could significantly impact the Canal’s competitive position in the market.
2. Look beyond your own horizon.
We live in an increasingly interconnected world. Markets, technologies, and industries across the globe are converging. Aerospace suppliers now compete for critical parts and equipment with suppliers to the automotive and maritime industries. Consumer preferences in the mobile media market are now shaping the development of technologies in the automotive sector. The greatest power of Big Data comes from its ability to integrate information from a multitude of sources, allowing organizations to see the big picture and form insights never before discernible.
But to tap this power, companies need to look beyond themselves and their immediate market. That means identifying and using data from sources outside the company and perhaps outside the industry. In a dynamic, global economy, businesses cannot rely on extrapolations of the past to predict the future.
Industry experts must be relied upon who understand emerging trends and can help adjust a mathematical model to ensure greater forecast accuracy.
In the case of the Panama Canal Authority, IHS brought together more than 30 experts from across many industries and disciplines, including trade and transportation, maritime, energy, chemical, automotive, and economics to construct detailed models of trade flows by type of goods and commodities, type of vessel, and origin and destination of shipment. The models combined the Canal’s own data with other industries in ways they had never done before. Among other things, they demonstrated that the development of capacity for 6.5 billion cubic feet/day of liquefied natural gas exports from the Gulf Coast of Mexico to Asia will likely create shipping transits that they never anticipated for the Panama Canal.
3. Big Data analysis is a voyage, not a destination.
In an increasingly volatile world, data changes quickly. Accordingly, data analytics needs to be an ongoing, iterative process. Economic and political environments change rapidly. Commodity prices rise and fall. Models need to be updated not only with the latest data but also the latest expert insights. Technology improvements not only may change a company’s markets, requiring the updating of data, but may also impact the ability to analyze larger amounts of data. Significant ongoing investments in infrastructure may be required to guard the return on investment. Processes for regularly collecting and using the latest data should be in place.
“Effectively analyzing that data involves guidance and knowledge from the right people armed with the appropriate tools that can “connect the dots” between seemingly unrelated phenomena”
Perhaps most important, data analysis should be a process of continual improvement and fine-tuning. It is imperative to learn from the past as well as to try to project into the future. Failures often offer critical insights that help make future efforts more successful. Comparing what the model predicted would happen to what actually happened provides the ability to adjust models as needed. Continual refreshing of the data, models, and industry insight are critical to producing the best, most accurate projections, which in turn provide the basis to make the best decisions.
Big Data is important but it’s no panacea. It’s just one type of data, albeit an important one, that is required to help companies understand the world and make informed decisions. However, to realize the greatest potential from this Big Data revolution necessitates years of accumulated wisdom from industry experts who ask the right questions, build the models, analyze the data, and interpret the answers to deliver the big insights.