Top 10 List on Big Data
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Top 10 List on Big Data

The amount of data in our world has been exploding, and analyzing large data sets-so-called big data-will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey's Business Technology Office. Leaders in every sector will have to grapple with the implications of big data, not just a few data-oriented managers. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data for the foreseeable future.


MGI studied big data in five domains-healthcare in the United States, the public sector in Europe, retail in the United States, and manufacturing and personal-location data globally. Big data can generate value in each. For example, a retailer using big data to the full could increase its operating margin by more than 60 percent. Harnessing big data in the public sector has enormous potential, too. If US healthcare were to use big data creatively and effectively to drive efficiency and quality, the sector could create more than $300 billion in value every year. Two-thirds of that would be in the form of reducing US healthcare expenditure by about 8 percent. In the developed economies of Europe, government administrators could save more than €100 billion ($149 billion) in operational efficiency improvements alone by using big data, not including using big data to reduce fraud and errors and boost the collection of tax revenues. And users of services enabled by personal-location data could capture $600 billion in consumer surplus. The research offers seven key insights.

1. Data have swept into every industry and business function and are now an important factor of production, alongside labor and capital. We estimate that, by 2009, nearly all sectors in the US economy had at least an average of 200 terabytes of stored data (twice the size of US retailer Wal-Mart's data warehouse in 1999) per company with more than 1,000 employees.

2. There are five broad ways in which using big data can create value. First, big data can unlock significant value by making information transparent and usable at much higher frequency. Second, as organizations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days, and therefore expose variability and boost performance. Leading companies are using data collection and analysis to conduct controlled experiments to make better management decisions; others are using data for basic low-frequency forecasting to high-frequency nowcasting to adjust their business levers just in time. Third, big data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services. Fourth, sophisticated analytics can substantially improve decision-making. Finally, big data can be used to improve the development of the next generation of products and services. For instance, manufacturers are using data obtained from sensors embedded in products to create innovative after-sales service offerings such as proactive maintenance (preventive measures that take place before a failure occurs or is even noticed).


Distilling value and driving productivity from mountains of data

3. The use of big data will become a key basis of competition and growth for individual firms. From the standpoint of competitiveness and the potential capture of value, all companies need to take big data seriously. In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, compete, and capture value from deep and up-to-real-time information. Indeed, we found early examples of such use of data in every sector we examined.

4. The use of big data will underpin new waves of productivity growth and consumer surplus. For example, we estimate that a retailer using big data to the full has the potential to increase its operating margin by more than 60 percent. Big data offers considerable benefits to consumers as well as to companies and organizations. For instance, services enabled by personal-location data can allow consumers to capture $600 billion in economic surplus.

5. While the use of big data will matter across sectors, some sectors are set for greater gains. We compared the historical productivity of sectors in the United States with the potential of these sectors to capture value from big data (using an index that combines several quantitative metrics), and found that the opportunities and challenges vary from sector to sector. The computer and electronic products and information sectors, as well as finance and insurance, and government are poised to gain substantially from the use of big data.

6. There will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.

7. Several issues will have to be addressed to capture the full potential of big data. Policies related to privacy, security, intellectual property, and even liability will need to be addressed in a big data world. Organizations need not only to put the right talent and technology in place but also structure workflows and incentives to optimize the use of big data. Access to data is critical-companies will increasingly need to integrate information from multiple data sources, often from third parties, and the incentives have to be in place to enable this.

The top marketing executive at a sizable US retailer recently found herself perplexed by the sales reports she was getting. A major competitor was steadily gaining market share across a range of profitable segments. Despite a counterpunch that combined online promotions with merchandizing improvements, her company kept losing ground.

When the executive convened a group of senior leaders to dig into the competitor's practices, they found that the challenge ran deeper than they had imagined. The competitor had made massive investments in its ability to collect, integrate, and analyze data from each store and every sales unit and had used this ability to run myriad real-world experiments. At the same time, it had linked this information to suppliers' databases, making it possible to adjust prices in real time, to reorder hot-selling items automatically, and to shift items from store to store easily. By constantly testing, bundling, synthesizing, and making information instantly available across the organization-from the store floor to the CFO's office-the rival company had become a different, far nimbler type of business.

What this executive team had witnessed first hand was the game-changing effects of big data. Of course, data characterized the information age from the start. It underpins processes that manage employees; it helps to track purchases and sales; and it offers clues about how customers will behave.

But over the last few years, the volume of data has exploded. In 15 of the US economy's 17 sectors, companies with more than 1,000 employees store, on average, over 235 terabytes of data-more data than is contained in the US Library of Congress. Reams of data still flow from financial transactions and customer interactions but also cascade in at unparalleled rates from new devices and multiple points along the value chain. Just think about what could be happening at your own company right now: sensors embedded in process machinery may be collecting operations data, while marketers scan social media or use location data from smartphones to understand teens' buying quirks. Data exchanges may be networking your supply chain partners, and employees could be swapping best practices on corporate wikis.

All of this new information is laden with implications for leaders and their enterprises. Emerging academic research suggests that companies that use data and business analytics to guide decision making are more productive and experience higher returns on equity than competitors that don't. That's consistent with research we've conducted showing that "networked organizations" can gain an edge by opening information conduits internally and by engaging customers and suppliers strategically through Web-based exchanges of information.

Over time, we believe big data may well become a new type of corporate asset that will cut across business units and function much as a powerful brand does, representing a key basis for competition. If that's right, companies need to start thinking in earnest about whether they are organized to exploit big data's potential and to manage the threats it can pose. Success will demand not only new skills but also new perspectives on how the era of big data could evolve-the widening circle of management practices it may affect and the foundation it represents for new, potentially disruptive business models.

Five big questions about big data

In the remainder of this article, we outline important ways big data could change competition: by transforming processes, altering corporate ecosystems, and facilitating innovation. We've organized the discussion around five questions we think all senior executives should be asking themselves today.

At the outset, we'll acknowledge that these are still early days for big data, which is evolving as a business concept in tandem with the underlying technologies. Nonetheless, we can identify big data's key elements. First, companies can now collect data across business units and, increasingly, even from partners and customers (some of this is truly big, some more granular and complex). Second, a flexible infrastructure can integrate information and scale up effectively to meet the surge. Finally, experiments, algorithms, and analytics can make sense of all this information. We also can identify organizations that are making data a core element of strategy. In the discussion that follows, we have assembled case studies of early movers in the big data realm (see "Seizing the potential of 'big data'" and the accompanying sidebar, "AstraZeneca's 'big data' partnership.")

In addition, we'd suggest that executives look to history for clues about what's coming next. Earlier waves of technology adoption, for example, show that productivity surges not only because companies adopt new technologies but also, more critically, because they can adapt their management practices and change their organizations to maximize the potential. We examined the possible impact of big data across a number of industries and found that while it will be important in every sector and function, some industries will realize benefits sooner because they are more ready to capitalize on data or have strong market incentives to do so (see sidebar, "Parsing the benefits: Not all industries are created equal").

The era of big data also could yield new management principles. In the early days of professionalized corporate management, leaders discovered that minimum efficient scale was a key determinant of competitive success. Likewise, future competitive benefits may accrue to companies that can not only capture more and better data but also use that data effectively at scale. We hope that by reflecting on such issues and the five questions that follow, executives will be better able to recognize how big data could upend assumptions behind their strategies, as well as the speed and scope of the change that's now under way.

1. What happens in a world of radical transparency, with data widely available?

As information becomes more readily accessible across sectors, it can threaten companies that have relied on proprietary data as a competitive asset. The real-estate industry, for example, trades on information asymmetries such as privileged access to transaction data and tightly held knowledge of the bid and ask behavior of buyers. Both require significant expense and effort to acquire. In recent years, however, online specialists in real-estate data and analytics have started to bypass agents, permitting buyers and sellers to exchange perspectives on the value of properties and creating parallel sources for real-estate data.

Beyond real estate, cost and pricing data are becoming more accessible across a spectrum of industries. Another swipe at proprietary information is the assembly by some companies of readily available satellite imagery that, when processed and analyzed, contains clues about competitors' physical facilities. These satellite sleuths glean insights into expansion plans or business constraints as revealed by facility capacity, shipping movements, and the like.

One big challenge is the fact that the mountains of data many companies are amassing often lurk in departmental "silos," such as R&D, engineering, manufacturing, or service operations-impeding timely exploitation. Information hoarding within business units also can be a problem: many financial institutions, for example, suffer from their own failure to share data among diverse lines of business, such as financial markets, money management, and lending. Often, that prevents these companies from forming a coherent view of individual customers or understanding links among financial markets.

Some manufacturers are attempting to pry open these departmental enclaves: they are integrating data from multiple systems, inviting collaboration among formerly walled-off functional units, and even seeking information from external suppliers and customers to cocreate products. In advanced-manufacturing sectors such as automotive, for example, suppliers from around the world make thousands of components. More integrated data platforms now allow companies and their supply chain partners to collaborate during the design phase-a crucial determinant of final manufacturing costs.

2. If you could test all of your decisions, how would that change the way you compete?

Big data ushers in the possibility of a fundamentally different type of decision making. Using controlled experiments, companies can test hypotheses and analyze results to guide investment decisions and operational changes. In effect, experimentation can help managers distinguish causation from mere correlation, thus reducing the variability of outcomes while improving financial and product performance.

Robust experimentation can take many forms. Leading online companies, for example, are continuous testers. In some cases, they allocate a set portion of their Web page views to conduct experiments that reveal what factors drive higher user engagement or promote sales. Companies selling physical goods also use experiments to aid decisions, but big data can push this approach to a new level. McDonald's, for example, has equipped some stores with devices that gather operational data as they track customer interactions, traffic in stores, and ordering patterns. Researchers can model the impact of variations in menus, restaurant designs, and training, among other things, on productivity and sales.

Where such controlled experiments aren't feasible, companies can use "natural" experiments to identify the sources of variability in performance. One government organization, for instance, collected data on multiple groups of employees doing similar work at different sites. Simply making the data available spurred lagging workers to improve their performance.

Leading retailers, meanwhile, are monitoring the in-store movements of customers, as well as how they interact with products. These retailers combine such rich data feeds with transaction records and conduct experiments to guide choices about which products to carry, where to place them, and how and when to adjust prices. Methods such as these helped one leading retailer to reduce the number of items it stocked by 17 percent, while raising the mix of higher-margin private-label goods-with no loss of market share.

3. How would your business change if you used big data for widespread, real-time customization?

Customer-facing companies have long used data to segment and target customers. Big data permits a major step beyond what until recently was considered state of the art, by making real-time personalization possible. A next-generation retailer will be able to track the behavior of individual customers from Internet click streams, update their preferences, and model their likely behavior in real time. They will then be able to recognize when customers are nearing a purchase decision and nudge the transaction to completion by bundling preferred products, offered with reward program savings. This real-time targeting, which would also leverage data from the retailer's multitier membership rewards program, will increase purchases of higher-margin products by its most valuable customers.

Retailing is an obvious place for data-driven customization because the volume and quality of data available from Internet purchases, social-network conversations, and, more recently, location-specific smartphone interactions have mushroomed. But other sectors, too, can benefit from new applications of data, along with the growing sophistication of analytical tools for dividing customers into more revealing microsegments.

One personal-line insurer, for example, tailors insurance policies for each customer, using fine-grained, constantly updated profiles of customer risk, changes in wealth, home asset value, and other data inputs. Utilities that harvest and analyze data on customer segments can markedly change patterns of power usage. Finally, HR departments that more finely segment employees by task and performance are beginning to change work conditions and implement incentives that improve both satisfaction and productivity.

4. How can big data augment or even replace management?

Big data expands the operational space for algorithms and machine-mediated analysis. At some manufacturers, for example, algorithms analyze sensor data from production lines, creating self-regulating processes that cut waste, avoid costly (and sometimes dangerous) human interventions, and ultimately lift output. In advanced, "digital" oil fields, instruments constantly read data on wellhead conditions, pipelines, and mechanical systems. That information is analyzed by clusters of computers, which feed their results to real-time operations centers that adjust oil flows to optimize production and minimize downtimes. One major oil company has cut operating and staffing costs by 10 to 25 percent while increasing production by 5 percent.

Products ranging from copiers to jet engines can now generate data streams that track their usage. Manufacturers can analyze the incoming data and, in some cases, automatically remedy software glitches or dispatch service representatives for repairs. Some enterprise computer hardware vendors are gathering and analyzing such data to schedule preemptive repairs before failures disrupt customers' operations. The data can also be used to implement product changes that prevent future problems or to provide customer use inputs that inform next-generation offerings.

Some retailers are also at the forefront of using automated big data analysis: they use "sentiment analysis" techniques to mine the huge streams of data now generated by consumers using various types of social media, gauge responses to new marketing campaigns in real time, and adjust strategies accordingly. Sometimes these methods cut weeks from the normal feedback and modification cycle.

But retailers aren't alone. One global beverage company integrates daily weather forecast data from an outside partner into its demand and inventory-planning processes. By analyzing three data points-temperatures, rainfall levels, and the number of hours of sunshine on a given day-the company cut its inventory levels while improving its forecasting accuracy by about 5 percent in a key European market.

The bottom line is improved performance, better risk management, and the ability to unearth insights that would otherwise remain hidden. As the price of sensors, communications devices, and analytic software continues to fall, more and more companies will be joining this managerial revolution.

5. Could you create a new business model based on data?

Big data is spawning new categories of companies that embrace information-driven business models. Many of these businesses play intermediary roles in value chains where they find themselves generating valuable "exhaust data" produced by business transactions. One transport company, for example, recognized that in the course of doing business, it was collecting vast amounts of information on global product shipments. Sensing opportunity, it created a unit that sells the data to supplement business and economic forecasts.

Another global company learned so much from analyzing its own data as part of a manufacturing turnaround that it decided to create a business to do similar work for other firms. Now the company aggregates shop floor and supply chain data for a number of manufacturing customers and sells software tools to improve their performance. This service business now outperforms the company's manufacturing one.

Big data also is turbocharging the ranks of data aggregators, which combine and analyze information from multiple sources to generate insights for clients. In health care, for example, a number of new entrants are integrating clinical, payment, public-health, and behavioral data to develop more robust illness profiles that help clients manage costs and improve treatments.

And with pricing data proliferating on the Web and elsewhere, entrepreneurs are offering price comparison services that automatically compile information across millions of products. Such comparisons can be a disruptive force from a retailer's perspective but have created substantial value for consumers. Studies show that those who use the services save an average of 10 percent-a sizable shift in value.

Confronting complications

Up to this point, we have emphasized the strategic opportunities big data presents, but leaders must also consider a set of complications. Talent is one of them. In the United States alone, our research shows, the demand for people with the deep analytical skills in big data (including machine learning and advanced statistical analysis) could outstrip current projections of supply by 50 to 60 percent. By 2018, as many as 140,000 to 190,000 additional specialists may be required. Also needed: an additional 1.5 million managers and analysts with a sharp understanding of how big data can be applied. Companies must step up their recruitment and retention programs, while making substantial investments in the education and training of key data personnel.

The greater access to personal information that big data often demands will place a spotlight on another tension, between privacy and convenience. Our research, for example, shows that consumers capture a large part of the economic surplus that big data generates: lower prices, a better alignment of products with consumer needs, and lifestyle improvements that range from better health to more fluid social interactions. As a larger amount of data on the buying preferences, health, and finances of individuals is collected, however, privacy concerns will grow.

That's true for data security as well. The trends we've described often go hand in hand with more open access to information, new devices for gathering it, and cloud computing to support big data's weighty storage and analytical needs. The implication is that IT architectures will become more integrated and outward facing and will pose greater risks to data security and intellectual property. For some ideas on how leaders should respond, see " Meeting the cybersecurity challenge."

Although corporate leaders will focus most of their attention on big data's implications for their own organizations, the mosaic of company-level opportunities we have surveyed also has broader economic implications. In health care, government services, retailing, and manufacturing, our research suggests, big data could improve productivity by 0.5 to 1 percent annually. In these sectors globally, it could produce hundreds of billions of dollars and euros in new value.

In fact, big data may ultimately be a key factor in how nations, not just companies, compete and prosper. Certainly, these techniques offer glimmers of hope to a global economy struggling to find a path toward more rapid growth. Through investments and forward-looking policies, company leaders and their counterparts in government can capitalize on big data instead of being blindsided by it.

About the authors

Brad Brown is a director in McKinsey's New York office; Michael Chui is a senior fellow with the McKinsey Global Institute (MGI) and is based in the San Francisco office; James Manyika is a director of MGI and a director in the San Francisco office.

Every second of the day, a wealth of data stream from a global maze of social networks, smartphones, point-of-sale devices, medical records, financial transactions, automobiles, energy meters, and other digital sources. Such big data, fueled largely by personal data about all of us, represent an asset class every bit as valuable as gold or oil.

In fact, freely flowing data-infinitely generated, distributed, mined, combined, tracked, and connected-play a particularly critical role in many of the products and services that make up the Internet economy. According to The Boston Consulting Group's research, by 2016 the Internet economy will reach $4.2 trillion in value in the developed markets of the G-20, or 5.3 percent of their GDP. In these countries, the Internet economy is growing at 8 percent annually, far outpacing just about every traditional sector in many otherwise-struggling economies-and growth rates are more than twice as fast in developing markets.

Consumers are the real beneficiaries of the Internet economy. The value they place on the many Internet services that have been built atop the sharing of personal data-such as search engines, e-mail, news sites, and social-network services-reflects that benefit. Consumers value the Internet at many times more than its cost, BCG has found.

For executives who have long wished for better answers, "big data" can look like an easy win. They might be tempted to think that an expensive big data solution, on its own, can sort through data and deliver the new insights they are looking for.

As with any technological innovation, the question of how to use it is ultimately a business question. No big data product or service can substitute for the rigorous and demanding process of figuring out what questions to ask.

We find that when companies go through the work of determining the right questions to ask and where to find the answers, they typically find that the tools are already inhouse, either in business intelligence software or in existing database tools. To help executives decide whether they need to invest in new talent, tools and capabilities, we have developed a framework that describes four characteristics of big data (see Figure 1). If executives are not facing at least three of these four issues, it's unlikely they're confronting a real big data problem, which means their organization's existing capabilities and tools may be sufficient for now.

Are you facing a big data problem?

Volume: Does the problem require that large volumes (currently, from tens of terabytes up through petabytes) be analyzed simultaneously? Some applications demand it. Consider Amazon's recommendation engine, which aggregates and analyzes hundreds of terabytes of shopping cart and click-through data from millions of users to determine which products are related. Combining this information with a user's online behavior generates real-time product recommendations personalized to each Amazon customer.

However, online retailers can often deliver fairly accurate recommendations by analyzing just a statistically relevant sample of a large data set. To reduce the size of the data set, most e-commerce sites get their recommendations by noting products that were purchased together. While this approach lacks the personalization available to Amazon, most retailers consider it good enough because it provides many of the same up-sell and cross-sell opportunities at a fraction of the computational cost.

Velocity: Does the problem require analysis in real time? Wall Street traders need to analyze and execute trades in fractions of a second. They pay millions to gain millisecond advantages by locating their servers as close as possible to the stock exchange, and their firms are developing proprietary big data solutions. For them, big data tools that reduce the processing times from milliseconds to microseconds-and enable throughputs from hundreds of thousands to millions of transactions-offer enough return to justify the expenditure.

Few organizations need to make decisions that quickly. In brick-and-mortar retail for example, only a handful of companies monitor their supply chain as closely as Wal-Mart does, with its highly publicized RFID (radio frequency identification) tags that track products through their distribution network. But even those organizations still make decisions based on time increments of a few minutes or hours. Spending big money to generate instantaneous insights would be a waste today, because those insights are put to use only in a more traditional time cycle. That may change in the future, but for now a far better investment for most retailers would be to improve their analytical capabilities to make more effective use of existing data and tools, to help them avoid running out of inventory or to group deliveries into fewer shipments.

Data types: Are the data easy to split into meaningful units that can be sorted, evaluated and compared? Traditional analytical software doesn't cope well with unstructured data, including multilingual audio, video, images and text. National security organizations face this challenge every day, as they process petabytes of communication data, security video footage, calls and other raw data. To meet this challenge, governments spend hundreds of millions of dollars to develop and operate big data computer clusters and analytics.

Analytical complexity: Think of the complexity of a business problem as the number of operations required to transform a set of data into actionable insights. One example is Lexis- Nexis's program to determine whether individuals are related, using a variety of data types, including court records, birth certificates and online records. Financial institutions depend on LexisNexis's system to help prevent identify theft and fraud.

By comparison, determining a mobile phone subscriber's monthly bill is comparatively simple, even though the telecom is dealing with massive amounts of call data describing call duration, location, interconnections and roaming. It is relatively simple for the telecom to identify every call (due to the unique mobile number) and aggregate total usage and fees at the end of the month. The analytic process doesn't have to cope with fuzziness, just simple yes or no rules.

Where to invest now

Companies should focus investments on hiring and developing data scientists who can ask the right questions using their current data systems to determine answers, rather than purchasing new solutions that may be more than they need.

Improve your data capabilities. Decision makers and their teams will need to develop the capability to ask questions that push the business forward. It won't be easy: Figuring out which questions to ask requires creativity, a deep understanding of the available data and a thorough knowledge of the business. Managers should place a premium on recognizing opportunities in data and on thinking about what could be possible if there were no constraints to getting answers to the questions they might ask. Metrics that show how well teams are using data to meet their goals will become increasingly important.

Don't mistake reports for insights. The risk of being overloaded by information becomes greater with every terabyte stored. With storage costs continually declining, organizations may be tempted to save everything indiscriminately. But that practice can actually increase the challenge of locating the data that really matters. Managers and teams will need periodically to ask themselves if the data they are storing and reporting is yielding meaningful insights that affect the success of the business. They may even want to stop issuing reports to see who notices and complains-which is a proven and effective way to show that data handling was geared toward busywork and reports, overlooking opportunities to create valuable business insights.

Restructure your organization to focus on insights from data. No organization can hope to gain insights that move their business forward simply by handing their data problem over to the IT department and purchasing a big data solution. The goals of the business will continue to guide the way that companies use data, just as it guides the way they use all their resources. Consider pairing traditional executives with quantitative people who understand and are comfortable working with data. Pairing their complementary skills can help guide teams to decisions that exploit the data opportunity in service of the business's progress.

Whether you face a true big data challenge or just have lots of data, the key success factor is building strong capabilities that move the business. Talented decision makers, solid analytical skills and good technology are essential. But the ultimate source of competitive advantage lies in the art of abstracting the potential value within data and turning it into meaningful insights, which should be the goal of any transformation aimed at overcoming your data challenge.

Getting better insights from data-Bain's approach

  1. Audit your current data systems. Are you capturing and storing the types of information that will help you gain the insights you need?
  2. Benchmark insights and analysis. How do your insights compare with those of competitors and with businesses in other industries that have identified value in their data?
  3. Identify and prioritize the opportunities for improving data utilization. How well do you use your data? Do you see opportunities for developing better analytics or asking better questions? Will you need more or different data? Will you need more or faster processing?
  4. Identify the resources necessary to realize those opportunities. What new tools, people, analyses, systems and service providers will you need to address the opportunities?

Rasmus Wegener is a partner with Bain & Company in Atlanta. Velu Sinha is a partner in Bain's Palo Alto office. Both are members of Bain's Technology practice.

By now, most companies recognize that they have opportunities to use data and analytics to raise productivity, improve decision making, and gain competitive advantage. "Analytics will define the difference between the losers and winners going forward," says Tim McGuire, a McKinsey director.


Building a data-driven organization

But actually mapping out an analytics plan is complicated. You have to set a strategy; draw a detailed road map for investing in assets such as technology, tools, and data sets; and tackle the intrinsic challenges of securing commitment, reinventing processes, and changing organizational behavior. Our collection of content, which synthesizes key insights drawn from many analytics projects, sets out the key issues, whether you are launching a pilot project or a large-scale transformation.


Transforming data

" Big data: What's your plan? " sets out the imperative task: to develop a plan that brings together data, analytics, frontline tools, and people to create business value. Only by spending the time to craft a plan can executives establish a common language to focus on goals and on ways of getting started.

Above, in the first of three videos, Tim McGuire sets out the triple challenge that companies face: deciding which data to use (and where outside your organization to look), handling analytics (and securing the right capabilities to do so), and using the insights you've gained to transform your operations. Misconceptions around these tasks trip up many companies.

In another video, Matt Ariker, of McKinsey's Consumer Marketing Analytics Center, focuses on the human element: the skills needed; how to organize and integrate new capabilities, people, and roles; and the mind-set and behavioral changes organizations must make to become data driven. Finally, McKinsey expert Matthias Roggendorf outlines the essentials of a business case for implementing a data transformation.