Insights Decision-making Leveraging Technology - Komal Jain
In the five decades that management and business literature has evolved, numerous studies have been conducted on what really separates successful companies from the rest. These studies highlight multiple reasons for their success ranging from the presence of a visionary leader, ability of an organization to adapt to ever-changing conditions and seizing opportunities at the right time. However, one of the most important factors that stands out is the ability to make right decisions on a consistent basis.
According to Jim Collins, the author of best-selling book “Good to Great,”
“Breakthrough results come from a series of good decisions diligently executed and accumulated on top of one another. Great companies make many more good decisions and these decisions are based on brutal facts.” Given that making the right decisions consistently separates the leaders from the rest of the pack, the real question for any business leader is how can information, across the organization, be leveraged to make the right business decisions and create differentiation for the organization.
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On the one hand, we have seen many significant technological changes in terms of innovation ranging from widespread availability of mobile Internet, across both developed and developing countries, to enterprise mobility, availability of cheap computing power through Cloud and significant growth in the arenas of Big Data and analytics.
On the other hand, companies can capture a great volume of structured and unstructured data from multiple sources such as customer searches, preferences, order history, social networking sites, online forums, call records, photo archives, video archives, blogs and such like. According to some estimates, Wal-Mart handles more than 1 million customer transactions every hour, which are imported into databases that are estimated to contain more than 2.5 petabytes of data – the equivalent of 167 times the information contained in all the books in the US Library of Congress! Facebook handles more than 40 billion photos from its user base. In terms of cheap computing power, decoding the human genome originally took 10 years to process, and now it can be done in a week at significantly lower costs. All these changes have provided companies the opportunity to leverage technology and data to obtain insights that, in turn, help them improve customer satisfaction, identify opportunities for new product introduction, improve customer conversion rates, focus on premium customers. Here are some interesting examples:
Here are some more tales from the business side. Amazon The global leader in e-commerce was one of the first few companies to customize its website and provide personalized recommendations on the basis of users’ buying behavior, search and browsing history. This personalized experience enabled Amazon to maintain customer stickiness and led to Amazon’s fast growth. Now, Amazon has made pricing
extremely dynamic by leveraging yield management software. The company can analyze a large volume of data gathered from multiple sources including competitive pricing information. Today, Amazon can very quickly change its product pricing and offer best deals to its customers in a matter of minutes something that would take a traditional retailer days…even weeks.
Google Each week, millions of users around the world search for health information online. As part of Google Flu Trends, Google has diagnosed a close relationship between the number of people searching for flu-related topics and the number of people actually exhibiting symptoms of flu. Google Flu Trends currently reaches a number of countries around the world and is updated every day. For epidemiologists, this is an exciting development, because early detection of a disease outbreak can reduce the number of people affected. Such realtime estimates may enable public health officials and health professionals to improve response to seasonal epidemics and pandemics.
Capital One Capital One leverages its advanced data analytics expertise to discover, target and serve its most profitable customers; it runs more than 300 tests every day towards this end. The company effectively uses data on interest rates, rollover incentives, special promotions, and such like to decrease the cost of customer acquisition and increase its customer retention rate. According to a study, Capital One increased its customer retention by 87% and lowered the cost of acquiring a new customer by 83% over a period of 2-3 years by using advanced data analytics.
Marriott This leading hospitality organization employs fact-based decision-making to optimize its pricing model and increase occupancy levels. Marriott’s revenue-management system, One Yield, identifies its most profitable customers through its loyalty program and targets special marketing offers at them. Marriott realized an increase in revenue from leisure customers and an annual profit increase of $86 million. Marriott attributes these results, in part, to One Yield.
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As is evident from these instances, companies are constantly mining large volumes of data to obtain deeper insights and make improvements across a wide range of operations. If you consider these examples, few common themes emerge: 1) Availability of Relevant Data and Cheap Computing Power The availability of relevant data and the organization’s ability to capture it economically is primary and most significant. The advent of sensors has facilitated data availability. Similarly, Internet forums and social media platforms, like Facebook and Twitter, have turned into invaluable data sources of customer preferences and buying behavior. In addition, open source frameworks such as Apache™ Hadoop® have enabled companies to process petabytes of information cost effectively. This combination of information being readily available and low cost computing power has accelerated the pace of data analytics.
2) Use of Right Skills and Processes Many companies spend millions of dollars on the latest software and hardware to get them the best data analytics engines. Having the right processes and the right talent is equally important to analyze aggregated data and turn it into actionable results. At times, not having the right talent (core skills like data scientists) could potentially slow the complete program.
3) Close Monitoring of the Feedback Loop A closely monitored feedback loop is important to fine-tune models and improve the accuracy of data-based analysis, which becomes significant when companies are trying to correlate multiple factors impacting their environment.
do to overcome this? There is merit in focusing on the following: Asking the right questions Companies are succeeding in leveraging big data not only because they have the right tools, but also because they are asking the right questions. In every company, it is the leadership’s responsibility to define vision, identify success parameters and set the right priorities. Business analytics cannot replace the power of human vision and insight. Efficient business leaders are invaluable assets to the company – because it is they who provide vision, spot great opportunities, understand market trends, and motivate both consumers and employees to embrace change.
Effective decision making Successful organizations provide decision makers the right information at the right time. Business analytics and big data can provide insights at a much faster pace than ever before. The organization structure should be flexible and agile to harness these opportunities. Leaders need to open their minds to out-of-the-box ideas and insights. They need to consciously look at minimizing the “Not-Invented-Here Syndrome.”
Openness to change Business analytics calls for change in the decision making culture of companies. The first question a data-driven organization asks itself is “What do we know and what do we not?” and not “What do you think?” Becoming such an organization requires a company to move from a culture of relying on raw instinct to adopting a very data-centric view. Leaders should promote an unbiased, methodical approach towards data analytics and right decision making; they must avoid focusing on what cannot be done and instead make a conscious effort to emphasize what can be done to realize the company’s vision.
That being said, technology alone can’t do it all. There are numerous examples of companies having invested millions of dollars in the latest technology and still falling short of accurately forecasting market needs. When it comes to path-breaking innovation or new market entry, data analytics can often fall short. Henry Ford once said, “If I had asked my customers what they wanted, they would have said faster horses!” More recent, is the example of Apple’s launch of the iPod and iTunes - online music store. While conventional research predicted that such a product would fail as customers download free music from the Internet and would be unwilling to pay for music, Apple co-founder Steve Jobs ignored the findings and said: “It isn’t the consumer’s job to know what she want. That is Apple’s job. Our job is to figure out what they’re going to want before they do.”
iPod and iTunes went on to completely revolutionize the music industry by providing consumers a viable option to counter pirated music. It’s clear that sometimes it is essential to create a vision of the future and build the required product lines, ignoring historical data if need be. While prior data analysis can provide some insights, it takes vision to conceptualize something new.
Avoiding selective use of data Organizations need to break the bad habit of pretending to be more data-driven than they actually are. Many unnecessarily spice up presentations with a lot of data in support of decisions that have already been made. Others make decisions and solicit the help of consultants to validate or support them.
Economic cost Economic cost is an important factor in defining technology investments. Companies go to great lengths to build the best decision-making engines possible, spending millions of dollars in the process. The availability of technology does not imply that it should be used. Apart from considering the cost of software, services, hardware, and ongoing maintaining costs, companies also need to consider the management time required for such initiatives. They should define the level of information required for decision making – for instance, is it a broad trend or granular customer information that’s needed?
Technology has its own limitations when it comes to identifying the sources of long-term business impact. What can companies
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The evidence is clear: Effective understanding of data leads to better decisions. Leaders of successful organizations are already embracing this mindset to create competitive differentiation. They are combining data with vision and foresight, and changing the structure and culture of their organizations to become truly data-centric. And hugely successful.
In summary
REFERENCES •
Big Data – Management Revolution in HBR
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NY times article
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Competing On Analytics by Thomas H. Davenport and Jeanne G. Harris.
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http://www.google.org/flutrends/
About the Author Komal Jain Associate Vice President and Head of Semiconductor Sector - North America, Infosys Komal joined the company in June 1998 and since then has played a variety of leadership roles in client services, sales & operations. Komal has been instrumental in helping clients develop and implement their Business Transformation programs, Global Sourcing strategy and IT initiatives. Komal participates in the steering committees of many of the key client relationships. Komal holds a Bachelor’s degree in Electronics Engineering from Kurukshetra University and an MBA from the Indian Institute of Management, Kolkata. Komal also is a marathon runner and lives in the Dallas, TX.
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