Win With Advanced Business Analytics

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Win with Advanced Business Analytics: Creating Business Value from Your Data Chapter 1 - The Challenge of Business Analytics  The field of business analytics is evolving. It's becoming less about data silos and more about the integration of different data assets across the company.  There is a skills shortage for knowledgeable data professionals. It's expected to get worse, not better.  Business analytics is being driven by several external factors, such as increased competition, decreased customer loyalty, economic woes, and the proliferation of new media.  Business analytics requires many internal factors to succeed, including strong executive leadership support for analytics, effective technology infrastructure and tools, alignment with corporate priorities, and effective communication across departments. Chapter 2 - Pillars of Business Analytics Success—The BASP Framework 1. Five Stages of Analytical Maturity that was developed by Tom Davenport, a pioneer in the use of information and analytics effectively across the enterprise, and his coauthor Jeanne Harris in their 2007 work, Competing on Analytics  Stage 1 is labeled "analytically impaired" and is reflective of a company that has some data and management interest in analytics, yet no real center of excellence or organized capability.  Stage 2 is labeled "localized analytics" and reflects an organization where some isolated managers may support leveraging analytics, but there is no formal enterprise-wide effort or recognition at the senior-most level regarding the importance of analytics.  Stage 3 is labeled "analytical aspiration" and has some executive level sponsorship regarding the importance of analytics, and some organizational structure and effort have been put in place to leverage analytics within the enterprise. However, analytics is typically siloed to a few areas of the organization and lacks standards, support, and consistency.  Stage 4 is called the "analytical company" and involves a company-wide analytics priority that is actively under development, has the support of top executives, and has some standards and systems consistency.  In Stage 5, an "analytics competitor," the organization has consistent standards and practices, has thorough data integrity, and routinely capitalizes on all of the business benefits of its enterprise-wide analytics focus and capability. 2. Business Analytics Success Pillars (BASP) – for building analytics capability

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Win with Advanced Business Analytics: Creating Business Value from Your Data 3. Business Challenges Pillar- Any business analytics initiative must be grounded in "critical" business challenges. When we say critical, we mean challenges or questions for which the answers will lead to the company increasing its revenues or reducing its cost.  How can I increase customer acquisition and retention?  What prospects do I need to target in order to increase market share/customer spending?  What are the emergent competitive threats, and how can my organization manage them?  Who are my most profitable customers, and how do I bring in more like them?  What types of our customers are most loyal, and what can we do to increase loyalty among the others?  How are customer prospects using our online environment, and how can we increase conversion to a customer in our online experience?  What are customers saying about us in the marketplace?  What new products do customers want from us? 4. Data Foundation Pillar – Breaking the data silos and integrating data from different departments and sources. 5. Analytics Implementation Pillar – Identify the best solution that you will provide with your analytics capability- tool driven/ what type of info will be tracked/ resource planning. 6. Insights Pillar - addressing what’s happening, why it happened and what’s going to happen. Adopt the IMPACT approach (Identify, Master, Provide, Action, Communicate and Track).  First, you must identify your key business questions that are important to your business partner.  Then there is the mastery of the facts that have been uncovered or calculated, which may be presented in the form of a raw data table or some other data visualization technique  Provide the meaning. It is crucial that data be brought together, interpreted, and put in an appropriate context for your business audience  Action- This is often the most difficult step for the business analytics function, because it requires truly thinking about the broad needs of the business, understanding what is important to the business leaders across the organization, and learning how the organization operates  You must communicate your analytical findings and their recommended actions across the company.  The last step is to track the business outcomes of your analytical findings. What was done as a result of your work? What business impact did it have? 7. Execution & Measurement Pillar Describe what was happening without/before the solution.  Describe what is happening with the solution in place.  How has the solution increased business revenue (for example, retention, acquisition, CLTV, and so on)?  How has the solution improved business productivity (such as sales per rep, average order size, calls per service rep, customer satisfaction, and so on)?

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Win with Advanced Business Analytics: Creating Business Value from Your Data  How satisfied are the internal customers with the solution (for example, satisfaction ratings, anecdotal quotes, and so on)? 8. Distribution Knowledge Pillar- Focus on business impact more than data/ sources. Understand and make accessible client needs and solutions. 9. Innovation Pillar- Innovate and adapt analytical solutions to the changing business needs. Repetitive and same solution without value addition will not help business clients. Challenge your business analytics team to innovate relentlessly, always thinking critically about how past activities can be done better and how the team can affect the business in new and different ways.

Chapter 3 - Aligning Key Business Challenges across the Enterprise  Before starting any analytics initiative, you must define your analytics road map by identifying the most critical business challenges across the enterprise. In other words, those business challenges where overcoming them will result in either incremental revenue or cost savings and where analytics can have the greatest potential impact.  Listing every business challenge across the company can be overwhelming. We recommend you follow the IRIS framework to ruthlessly prioritize the most critical challenges that analytics will address. Execute this process regularly, ideally within the same cycle, as your corporate strategic planning.  We also recommend providing SMART business analytics solutions to address your business challenges following a baby-steps, or incremental, approach. This should help underscore the value of the analytics solution to the business without over-promising.  Finally, we provided an Analytics Recipe Matrix that includes most common business challenges, with examples of underlying analytics solutions. Use this matrix to develop your own list of business challenges and analytics solutions that can be socialized throughout your company Chapter 4: Big and Little Data—Different Types of Intelligence 1. Big Data- As defined best by IBM, “ Big data is a data that has volume, velocity and variety”. a.Volume: Changing technology to tackle large data sets. Requires scalable storage and distribution approach for querying. Hadoop is a platform for distributing computing problems across a number of servers; it was first developed and released as open source software by Yahoo. It implements the MapReduce approach pioneered by Google in compiling its search indexes and involves three steps. The first step is the "map" stage, which is where data are distributed among multiple servers and compiled. The partial results are then recombined in what is known as the "reduce" step, in order to simplify the data. The last step is the "retrieval" step, in which the data models are retrieved from the Hadoop file system and used. For example, Hadoop is one of the tools Facebook uses to be able to personalize its site experience for you. b.Velocity: data are flowing 24/7, every day of the year. Therefore, if you are an Internet company, your data architecture and tools must accommodate the processing of high data velocity and volume all the time, nonstop. As a result, companies such as online retailers are able to compile large histories of customers' every click and interaction, not only the final sales. Successful companies are able to use that information in real time by recommending additional products and services. For example, Walmart began using a 10-node Hadoop cluster as a way to analyze the online shopping experience and to make it more personal. It worked so well for Walmart that it is moving to consolidate ten different data processing platforms into one 250-node Hadoop cluster to deal with the

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Win with Advanced Business Analytics: Creating Business Value from Your Data increased streams of data it needs to process in order to create the strongest possible online customer experience c. Variety: the data sources and formats are widely diverse and don't fall into consistent structures that can be easily used by a company for processing or analysis. Examples of the variety of big data flows with high volume and velocity might include customer comments on a social media website, search terms on a website, click-stream data from an online shopping experience, location data from GPS or wifi tracking, and image or video uploads, among others. The main advancement in the data management world related to the principle of "variety" in big data is that traditional structured data are now able to be joined with semi structured and unstructured data.  In this chapter, we underscored that data are the most important raw material and the foundation of any business analytics solution.  We outlined a Customer Knowledge Framework (CKF) for how to think strategically and organize the data assets in your organization.  With the explosion of different data sources, companies must successfully build a solid data foundation based on consistent governance and standard setting across the organization.  Creating an effective data foundation is a long-term competitive advantage for attaining company success and will become increasingly more important over time.  A bad data foundation is likely to create harm or missed opportunities for your company.  The standardization of data questions across your organization (such as "What is a customer?") is required in order to effectively leverage business analytics.  The quality and quantity of data will drive the quality and quantity of analytics that are derived from it.

Chapter 5 - Who Cares about Data?—How to Uncover Insights