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Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvement from Big Data
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Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvement from Big Data

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While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive …

While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive explosion in data that is occurring today. The shift started nearly 10 years ago with expanding usage of the internet and the introduction of social media. But the pace has accelerated in the past five years following the introduction of smart phones and digital devices such as tablets and GPS devices. The continued rise in these technologies is creating a constant increase in complex data on a daily basis.

The result? Many companies don't know how to get value and insights from the massive amounts of data they have today. Worse yet, many more are uncertain how to leverage this data glut for business advantage tomorrow. In this white paper, we will explore three important things to know about big data and how companies can achieve major business benefits and improvements through effective data mining of their own big data.

Dr. Cedric Alford provides a roadmap for organizations seeking to understand how to make Big Data actionable.

Published in: Marketing, Technology, Education

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  • 1. White Paper Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvement from Big Data
  • 2. 2 What is Big Data? Forrester defines big data as “techniques and technologies that make handling data at extreme scale affordable. It's about having the technology and people with the appropriate analysis skills to allow firms to make sense of huge volumes of data in an affordable manner.” What's So Special About Big Data? It is estimated that 90 percent of the data in the world today was created in just the past 24 months. One might ask, how is this possible? With 2.5 exabytes1 of new data created every day and more data crossing the internet every second than was on the entire internet 20 years ago, it is easy to see how this has occurred. While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive explosion in data that is occurring today. The shift started nearly 10 years ago with expanding usage of the internet and the introduction of social media. But the pace has accelerated in the past five years following the introduction of smart phones and digital devices such as tablets and GPS devices. The continued rise in these technologies is creating a constant increase in complex data on a daily basis. The result? Many companies don't know how to get value and insights from the massive amounts of data they have today. Worse yet, many more are uncertain how to leverage this data glut for business advantage tomorrow. In this white paper, we will explore three important things to know about big data and how companies can achieve major business benefits and improvements through effective data mining of their own big data. Insight #1: Big Data is a Management Revolution Understandably, there is a tremendous amount of interest, along with some confusion, about “big data”–what it is, how it can help create competitive advantage. If you ask the opinion of thought leaders at highly regarded academic and corporate institutions such as MIT, McKinsey and IBM, you will hear largely universal consensus that big data is real and that it is only going to get bigger. Harvard Business Review's editor in chief recently wrote of big data, “it has the potential to propel companies to levels of performance we haven't seen in two decades.”2 Imagine the strides companies could make if they had access to all the petabytes and zettabytes of data they have collected over the years. Not only would it allow for potentially groundbreaking 1 2 An exabyte is 1,000 times the size of a petabyte, which is the equivalent of roughly 20 million filing cabinets. “Big Data for Skeptics”, Harvard Business Review, October 2012 © Copyright 2013
  • 3. 3 customer and market insights, but it would also enable significantly improved real-time decision making. Key findings: MIT Sloan School of Management and IBM's first annual New Intelligent Enterprise Global Executive Study (2011)  50% of the 3,000 respondents across 30 industries and 100 countries said that improvement of information and analytics was a top priority in their organization.  60% said innovating to achieve competitive advantage was a top business challenge.  60% said their organization had more data than it can effectively use.  Those deemed as top performing organizations were twice as likely to apply analytics to activities. Additional findings among study's “top performing” organizations:  Analytics are used in the widest possible range of decisions, large and small.  Twice as likely to use analytics to guide future strategies as lower performers.  Twice as likely to use insights to guide day-to-day operations.  Make decisions based on rigorous analysis at more than double the rate of lower performers. The results of MIT and IBM's 2011 New Intelligent Enterprise Study showed that organizations “want analytics to exploit their growing data and computational power to get smart and innovative in ways they never could before.” Bottom line, the study shows a proven correlation between top performance and analytics-driven management. This has important implications to every organization -- whether they are seeking growth, greater efficiency or competitive differentiation. Smart business leaders see using big data for what it is: a management revolution. Through big data, managers can know more about their business and use the data to make decisions based on evidence rather than intuition, resulting in improved decision making and business performance. As part of this management revolution, leaders must embrace this evidence-based decision making or be replaced by others who do.3 Reflecting on technology's role with big data, Gartner analyst Mark Beyer recently said, “Because big data's effects are pervasive, it will evolve (over the next several years) to become a standardized requirement in leading information architectural practices, forcing older practices and technologies into early obsolescence.” 3 “Big Data: The Management Revolution”, Harvard Business Review, October 2012 © Copyright 2013
  • 4. 4 By 2020, Gartner expects big data features and functionality (such as data mining) will be routinely expected from traditional enterprise vendors. “Organizations resisting this technology change will suffer severe economic impacts,” warns Beyer. Insight #2: To Be Useful, Big Data Must Be Made Smaller Most CRM and automation technologies introduced in the past two decades are simply not equipped to handle the volume, velocity and variety of today's big data. This is especially true of unstructured data from the digital channel (e.g., social networks, online shopping, and digital marketing). While today's big data is too big for most existing technologies to handle, there are valuable insights waiting to be unlocked in the massive amounts of operational (e.g., sales, costs), non-operational (e.g., sales forecasts) and unstructured digital (e.g., search engine marketing) data being generated today. To get it, companies must choose technologies designed to handle big data and facilitate effective data mining to chunk data down to a manageable size. Such technology must also keep data constantly refreshed and synchronized for relevancy as new data comes in every day. In order to leverage big data, companies must make it smaller so current technologies can handle it. Making big data smaller can be accomplished through data mining. ________________________________________________________________________________ What is Data Mining? According to Gartner, “data mining is defined as the process of discovering meaningful correlations, patterns and trends by sifting through large amounts of data stored in repositories. It employs pattern recognition technologies, as well as statistical and mathematical techniques.” In essence, data mining allows companies to harness everyday business data to obtain valuable knowledge and insights that will allow for problem solving and business improvement. ________________________________________________________________________________________________________________________ Data mining uses automated data analysis to prepare big data for segmentation, dialog and reporting. Because most companies have an incredible amount of data, it is important to begin the data mining process by defining business objectives. This will help identify and address the specific data needed for collection and analysis in order to achieve the desired insights. Without a starting framework, companies might get overwhelmed trying to evaluate all the data in the company's data warehouse and never achieve any meaningful insight to a business objective or challenge. Sample business objectives for conducting a data mining exercise might include:  Explanatory: To explain some observed event or situation Example: Why have sales increased through Twitter?  Confirmatory: To confirm a hypothesis Example: Will a 5 channel touch strategy increase sales by 10% or more?  Exploratory: To analyze data for new or unexpected relationships Example: How is Facebook contributing to the company's blog traffic? © Copyright 2013
  • 5. 5 What are Data Warehouses? Data warehouses are formal repositories of large volumes of data that a company collects over a long period of time. The gathered data may be specialized and used by various corporate departments such as accounting, purchasing and marketing. Having a centralized repository of all the company's data allows for more manageable user access and analysis. What are Data Marts? Data marts contain a subset of data from the data warehouse, focused on a single subject. Data marts are the access layer of the data warehouse environment where specific departments or approved team users can access and utilize specific business data as needed, without altering the data from the central data warehouse. Examples of benefits derived from creating a data mart:  Less cluttered than a central data warehouse.  Easy access to frequently needed data.  Greater flexibility and agility than a central data warehouse. M While some vendors offer technology that bypasses the need to create a data mart, we believe that it is a necessary and beneficial step in an effective data mining process. By taking the time to create the logical and physical structure of a data mart focused on a single subject or functional area, companies are able to track and statistically analyze specific sub-set of data from the central data warehouse. This approach systematically drives designated appropriate data into a data mart and ensures the data stays fresh and relevant because only approved, relevant data will be updated in the data mart as new data comes in. Easy Access to Relevant Data Designated data in the data mart is categorized for review and consumption by select business teams or departments (e.g., marketing, finance). With easy access to relevant content, managers can ideally identifying hidden patterns in a data set to solve a problem, identify and validate triggers (e.g., evaluate results when an electronics retailer sends a discount for BlueRay DVD movies to shopper who purchased a new HDTV), and gain other insights for business benefit. In the case of sales and marketing, for instance, it delivers objective and actionable customer insights to show how each customer is interacting with each channel. Such data will shed light on the customer's preferred marketing content, timing and channels – potentially helping the company stop wasting time, money and effort on ineffective content or channels. Data mining has largely been used by traditional enterprises (including leading-edge and Fortune 500 companies and non-profit organizations) in the past decade to achieve tangible business © Copyright 2013
  • 6. 6 benefit. The uses of data mining will continue to expand--regardless of industry or the type of product or service offering--as analytics technologies are more widely adopted. Business leaders appreciate the objective nature and the credible insights data mining provides about their customers' preferences and consumer behaviors because it is based on the organization's own collected data (e.g., point of purchase, loyalty programs, direct mail, web analytics, social media chatter). Data Mining Techniques There is a wide variety of data mining techniques that can be used to help discover useful information, depending upon the nature of the data and the insights being sought. Some common methods include decision trees, regression modeling, clustering. (Editor's note: With so many different options and benefits, we cannot adequately address the various techniques in the confines of this white paper. If you would like to learn more, please talk with a member of our technical/consulting staff for further information.) Common Data Mining Techniques  Profiling Populations  Analysis of business trends  Target marketing  Usage Analysis  Campaign effectiveness  Product affinity  Customer Retention and Churn  Profitability Analysis  Customer Value Analysis  Up-Selling  Conversion Funnel Analysis  Revenue Attribution  Promotion and Price Optimization  Behavioral Segmentation  Lifecycle Optimization  Predictive Churn Modeling It should be noted that data mining technologies have been in use for years. According to a study in 2003, for example, only 35% of large companies surveyed at that time had deployed data marts and data warehouses for data mining purposes.4 Fortunately, in recent years, technologies equipped to handle the volume, velocity and variety of today's big data have improved greatly. Also good news, as with most technologies, prices have come down and are quite reasonable compared to the original cost of older CRM and automation technologies in use today. Insight #3: Beware of a DIY Approach While effective big data and data mining technology are readily available today, quite often a significant challenge is the acquisition, implementation and adoption of this advanced technology. According to Bill Franks, chief analytics officer of Teradata and author of Taming The Big Data Tidal Wave, “Today you can find products and solutions for whatever you need to do with big data. The real problems are getting budget, doing the implementation, getting people up to speed on how to 4 “How Large Corporations Use Data Mining to Create Value”, Management Accounting Quarterly, 2003 © Copyright 2013
  • 7. 7 use the tools, getting buy in from various stakeholders, and pushing against a culture averse to change.”5 Indeed, we have found similar challenges faced by our clients looking to adopt marketing analytics and marketing automation technologies. But an even greater challenge facing most companies today is the lack of necessary experience, best practices and seasoned staff capable of implementing a successful data mining program inhouse. Without these crucial elements, it is highly likely a data mining program conducted internally without the involvement and guidance of highly qualified and experienced experts will fail. This is why most companies turn to outside consultants with the necessary technology and proven expertise for guidance and support. Let us stress further the importance of securing buy-in by all levels within the organization. Often there must be organizational transformation in culture (e.g., management must come to view analytics as essential to problem solving) and staff capabilities (e.g., make sure employees fully understand and effectively use the methodologies) to achieve data mining success. According to 40% of respondents in the 2011 IBM and MIT New Intelligent Enterprise Study, the leading obstacle to widespread analytics adoption is lack of understanding of how to use analytics to improve the business. According to Gartner analyst Gareth Herschel, many organizations know they want customer data mining software as part of their enterprise analytics strategy, but they are uncertain about how to evaluate and deploy tools. Our advice to organizations interested to get full benefit from big data is simple: avoid a DIY approach if you want to save yourself time, money and frustration. Three Stages of Analytics Maturity Not sure how to characterize an organization's level of analytics capability? Consider the findings of a 2011 study by IBM and MIT which identified three levels of analytics capabilities: 1) Aspirational: These organizations are the furthest from achieving their desired analytical goals. Often they are simply searching for ways to cut costs. They have few of the necessary building blocks (people, processes or tools) to collect, understand, incorporate or act on analytic insights. 2) Experienced: With some analytical experience, these organizations are looking to go beyond cost management and want to develop better ways to collect, incorporate and act on analytics so can begin to optimize their organization. 5 Bill Franks work blog on November 13, 2012. © Copyright 2013
  • 8. 8 3) Transformed: These organizations have substantial experience using analytics across a broad range of functions. They use analytics as a competitive differentiator. Less focused on cutting costs, they want to increase customer profitability and make smart investments in niche analytics for further excellence. IBM and MIT Study 2011: THE THREE STAGES OF ANALYTICS ADOPTION Three capability levels - Aspirational, Experienced and Transformed - were based on how respondents rated their organization's analytical prowess ASPIRATIONAL Key obstacles TRANSFORMED Use analytics to guide actions Use analytics to prescribe actions Financial management and budgeting All Aspirational functions Operations and production Sales and marketing Strategy/business development Customer Service Product research/development All Aspirational and Experienced functions Competitive differentiation through innovation Competitive differentiation through innovation Revenue growth (primary) Cost efficiency (secondary) Lack of understanding how to leverage analytics for business value Lack of understanding how to leverage analytics for business value Executive sponsorship Culture does not encourage sharing information Business challenges EXPERIENCED Use analytics to justify actions Cost efficiency (primary) Revenue growth (secondary) Motive Functional proficiency Skills within line of business Ownership of data is unclear or governance is ineffective Moderate ability to capture, aggregate and analyze data Data Limited ability to capture, aggregate, management analyze or share information and insights Limited ability to share information and insights Analytics in Action Rarely use rigorous approaches to make decisions Limited use of insights to guide future strategies or day-to-day operations Some use of rigorous approaches to make decisions Growing use of insights to guide future strategies, but still limited use of insights to guide day-to-day operations Risk management Customer Experience Work force planning/allocation General management Brand and market management Competitive differentiation through innovation Revenue growth (primary) Profitability acquiring/retaining customers (targeted focus) Lack of understanding how to leverage analytics for business value Management bandwidth due to competing priorities Accessibility of the data Strong ability to capture, aggregate and analyze data Effective at sharing information and insights Most use rigorous approaches to make decisions Almost all use insights to guide future strategies, and most use insights to guide day-to-day operations Source: SLOANREVIEW.MIT.EDU Numeric Analytics would like to add its further commentary to the IBM and MIT study's categories based on our client experience: Aspirational: These organizations typically are trying to “plug the gap” and train their staff as they attempt to create a data-driven culture. Quite often, the harsh reality is some key staff will need to be replaced because they have the wrong background or experience with these technologies. © Copyright 2013
  • 9. 9 Experienced: This group “gets it” and have staffed appropriately for it (e.g., most likely experienced executives have been brought in). It's highly likely there is still a tendency to make decisions that are not based on the findings from the company's analytical data. Transformed: These organizations are “analytics stars” but they are in the minority. They have the right people with the right experience, along with cutting-edge business titles to reflect their role (e.g., Director of Analytics). But while a data-driven culture is in place and decisions are made based on data, they typically don't have broader use or business cases to draw from. Outside consulting and process and technology expertise is still needed. Years ago, Numeric Analytics created its own digital intelligence maturity model based on our extensive client work and observations. It is interesting to see the similarities between our Digital Intelligence Maturity Model and the IBM and MIT Study's Three Stages of Analytics Adoption, as shown below: As you can see, the major difference between the two is that we have identified two levels of what the IBM and MIT study identified as “experienced”. We call these levels intermediate and advanced. Regardless of which stage an organization fits into regardless of which model, without question there is a need for outside experts who have relevant cross-channel and cross-industry experience to skillfully guide the way. To be certain, creating an effective data mart(s) from the central data warehouse can be an arduous task, but the long-term business benefits and efficiency gains are well worth the initial hard work. “Organizations that do not include big data analyses as part of their sales strategies will miss opportunities to recognize demand early and shape it,” according to Gartner analyst Gene Alvarez. “(Organizations) must access and analyze new sources of data, including mobile customers' activities and social graphs, to spot trends, predict demand, and provide an appealing customer experience.”6 6 Information Week, July 31, 2012 © Copyright 2013
  • 10. 10 Where to Begin? Many organizations are uncertain how to begin a data mining project and can be overwhelmed by all the big data and data mining chatter in the media and by all the different vendors. If your organization is feeling this way, it is understandable. We are pleased that you are reading this white paper and hope that it has brought you more insights and clarity on this important topic. Armed with this valuable information, it's time to proceed. Rest assured that we have “been there, done that” when it comes to a solid track record of numerous successful data mining projects. When beginning a data mining project with clients, we begin by assessing their current analytical maturity level, which provides valuable insights and direction how best to proceed. Over the years, our data mining clients have told us they are able to drive better business decision making and have gained valuable insight on what marketing content and channels are working best to engage target audiences. By leveraging their big data to obtain key insights, they have improved data-based decision making capability and can solve business problems more quickly than ever before. Are you ready to achieve similar business improvements from your big data? Give us a call today. About Numeric Analytics At Numeric Analytics, our core competency is built on measurement and analytics. We help organizations capitalize on data to drive better business decisions, automate their efforts, and continuously drive measurable improvements through experience and proven optimization techniques. Our Analytics and Optimization Team helps organizations move away from poor analytics and fragmented tactics resulting from the challenge of growing volume and complexity of data. We help clients move to world-class optimization programs that effectively manage growing data and create competitive advantage with analytics. We understand the challenges and the issues surrounding marketing technologies, from consideration to execution. We provide answers and direction to ensure all the variables are taken into account so that the best automation solution is selected to drive your organization's integrated marketing efforts. With proven consulting expertise and a network of leading technology providers, we have successfully reworked entire testing programs--redefining the processes, the people and the platforms used--to create a proven methodology that delivers exponential returns. Since our founding in 2007, we’ve completed over 600 successful client engagements, including many Fortune 1000 companies. Over 500 million web pages are tracked through our implementations. And 65% of our clients re-engage us on other projects. No one else in the market provides the full spectrum of services that Numeric Analytics offers. © Copyright 2013
  • 11. 11 Numeric Analytics takes pride in our mission to turn “insight into action” for our clients. If you found this information useful, check out these other recent white papers that have more information on topics addressed in this document:  “The Role and Value of Automation in Integrated Marketing Success”  “Multi-Channel Analytics: The Answer to the 'Big Data' Challenge and Key to Improved Customer Engagement” All these documents plus other white papers and case studies can be accessed at www.numericanalytics.com/experienceWhitePapers.asp or call us at 972-496-7033 to request further information or to schedule a free consultation. M Visit us at http://www.NumericAnalytics.com © Copyright 2013