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Empowering Business through Big Data Analytics

Empowering Business through Big Data Analytics






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    Empowering Business through Big Data Analytics Empowering Business through Big Data Analytics Document Transcript

    • Empowering Business through Big Data Analytics Introduction There is a new economy emerging, an economy based on data. This data is being generated, stored, sold, consumed and protected at a level commonly reserved for precious metals and currency. Companies gather this data every time they interact with a customer, partner, provider or competitor. Why is this data so valuable? Because companies can use it to better understand markets, customers and competitors and therefore greatly speed their time to market and quality of delivery. However, the data being generated today is more complex than ever, with more unstructured components. Therefore, organizations must discover and evaluate new technologies and paradigms for analyzing the data and using it to make decisions. In particular, the business side of an organization must clearly define a small number of use cases to enable the information technology (IT) team to deploy technologies that meet the needs of the business. This white paper outlines those key use cases for each of the common divisions in an organization and explains how big data analytics can help drive more effective strategies and decision making. How data is produced and consumed Two types of employees are involved with analytical technologies (see Figure 1): • The Analytical Consumer uses tools like visualization and search to view complex data sets, complex relationships and interpretations of cause-and-effect relationships, and uses these capabilities to drive decisions about how to operate the business and impact the bottom line in a positive way. • The Analytical Producer uses advanced technologies like machine learning and natural language processing to produce the analytical models that the Analytical Consumer uses to analyze data. The Analytical Producer has a skill set more tilted towards technology capabilities and their applicability to complex data sets, while the Analytical Consumer is an expert at the business.
    • Analytical consumers and analytical producers Analytics Sophisticated NLP, machine learning, predictive modeling, sentiment analysis, social network analysis, and visualization, No Hadoop/MapReduce programming expertise required. Analytical producer Analyze unstructured, structured and semistructured data from a single work bench Search Interactive and intuitive. Search interface allows business analyst to explore and exploit all data resources. Analytical consumer Visualization Interactive web-based authoring empowers business users to perform analysis, visualize results and take decisions. Analytics is about enabling effective decision making and measuring impact. Figure 1. The Analytical Producer uses advanced technologies like machine learning and natural language processing to produce the analytical models that the Analytical Consumer uses to analyze data. There is a corresponding distinction between analytics and big data: • Analytics is about the data and its impact on the business, about putting in the proper data models, algorithms and tools in place to manipulate and understand the data in a way that drives effective decision making. In other words, analytics is about enabling educated decisions and measuring impact (see Figure 2). • Big data is about the infrastructure, about ensuring that the underlying hardware and software have the ability to enable analytics. Big data is about SLAs, performance and speed. Often, analytics and big data are spoken about together because modern infrastructure technologies are needed to drive the new types of analytical technologies available. They have a clear impact on one another; in fact, big data is driving newer analytical technologies to enable organizations to take full advantage of the insight contained in their data. Different divisions in an organization face different business challenges (see Figure 4). Let’s look at how analytics can help each division improve the organization’s bottom line through better decisions, better measurement and faster response to changing market conditions. Analytics and big data Measuring impact Integration Curation Understanding Decision Figure 2. Analytics is about enabling effective decision-making and measuring impact. Share: 2
    • Big data is the plumbing; analytics is the business context. Figure 3. Big data provides the performance and speed to enable and drive analytics Employee retention More companies today are looking for ways to ensure that high-quality employees are effectively engaged so that they stay at the company, lowering costly turnover and project transitions. Many companies today struggle to engage with high-caliber employees until it is too late and the employee has given notice to leave the firm. That is, by the time they realize a high performer is unhappy, it is too late to make a meaningful change. With the current state of the job market, highly qualified staff can find new positions very quickly. Analytical tools, such as Dell™ Kitenga™ Analytics Suite, enable HR departments to more effectively analyze whether employees feel engaged with the company, their managers and their positions. By pulling in records from employee surveys, interactions with management, performance information, social media information and previous employee retention data, HR departments can create models that predict which staff are likely to leave the firm and should be proactively engaged. Customer retention Because customers can switch providers faster than ever today, companies must compete harder to retain their customers. To identify customers likely to leave, a company Key use for analytics and big data HR Customer support Sales Spend analysis Retention Marketing Upsell Competitive analysis Due diligence, intellectual property analysis Increase business Decrease costs Figure 4. Each division in an organization faces its own business challenges. Share: 3 The Kitenga Analytics Suite model proactively identifies events and experiences that affect customer retention.
    • must create a 360-degree view of its customers, their interactions, and the leading indicators that might mean customer loss is imminent. Kitenga Analytics Suite enables companies to analyze complex sets of data to identify potential buyers. The natural language processing capabilities in Kitenga Analytics Suite, combined with the scalability of Apache® Hadoop®, enables companies to combine disparate data sets—including CRM, support records, component failures, field dispatches and other events—into a single model of how customers react to changes in their experience. This model can be used to proactively identify events and experiences that affect customer retention, enabling staff to respond more quickly than with traditional triggers. Spend analysis In order to spend their marketing dollars effectively, marketing organizations have to regularly balance the available budget with a complex combination of possible events, campaigns and other methods of achieving maximum brand awareness and lead generation. Kitenga Analytics Suite enables marketing teams develop models of various combinations of spending and to analyze each model against market conditions and past campaign results so they can make better decisions about the most effective way to spend marketing dollars. Upsell Today’s customers have more information than ever before about available products, and they are more proactive than ever about understanding their options before making purchases. Companies want to proactively engage with customers to ensure they have relevant information when making purchasing decisions. Kitenga Analytics Suite enables companies to analyze complex set of data to identify potential buyers. This data can be sourced from social media sites, public discussion forums Share: 4 and internal data, such as historical buying patterns, to develop models for potential buyers. With this information, the company can proactively contact the most likely buyers with product information in order to drive a customer engagement that leads to a product sale. Competitive analysis Organizations must be able to respond quickly to changing market conditions, competitor threats and technology changes. Competitive analysis requires understanding the market, the current landscape and how that landscape is evolving over time, such as through changes to messaging or introduction of new products. Kitenga Analytics Suite enables companies to pull in public information sources and assess changes in messaging and product offering. Those changes can be evaluated and compared with internal plans to ensure that product messaging is evolving ahead of the competition and enabling customers to clearly understand product value propositions. Intellectual property due diligence In today’s competitive business market, intellectual property is more valuable than ever. In particular, it is often the driving force behind company mergers and acquisitions (M&As). During these complex M&A activities, it is critical that all parties have a clear understanding of the intellectual property owned, managed and accessible by the firms involved in negotiations. Advanced analytical tools like Kitenga Analytics Suite enable companies to combine and analyze public and private documents relating to intellectual property, including patent filings, contracts, documentation and software source code. With Kitenga Analytics Suite, teams can quickly determine what documentation is relevant during due diligence processes and to assess the value of specific intellectual property, its applicability in complex markets and the
    • availability of similar technologies in the open market. Improving the bottom line In the simplest terms, a company has two ways to improve the bottom line: by increasing the amount of business they do or by reducing costs while maintaining the current business levels. Today’s analytical tools enable companies to evaluate a multitude of information when developing corporate strategies, targeting new customers, retaining existing customers, and managing an effective and stable workforce. Tips for a successful analytics project When beginning an analytics project, your company should focus on a small number of identifiable and measurable processes. Analytical projects should not be planned across an entire company, or even division-wide. Initial pilots should focus on small, identifiable challenges and work to resolve the single challenge. Once a project has been successfully piloted and measured, other teams within the organization will see the value to new analytical technologies, and also understand the organizational changes required to adopt a new mindset and technology. All successful analytics projects start with clearly defined and managed metrics. These metrics enable consistent communication across teams, show how changes to the project affect key decision factors, and enable separate teams to understand how they contribute to the end goals of an analytics project. Enterprise architecture standards are also important to analytics projects. They enable staff to clearly understand where data is coming from, assess any transformations that occur, and understand what impact decisions have on processes and infrastructure. Conclusion Analytics is about enabling business users to understand and drive their organizations with technologies that provide visibility into complex data sets. Big data is about providing the tools that deliver that increased visibility to make staff more effective. Kitenga Analytics Suite enables companies to make better decisions through analytics. The solution’s unique integration of natural language processing with visualization and a search interface enables a broad range of staff to quickly consume complex data sets, identify relationships and model outcomes from a variety of factors influencing the business, so everyone can better contribute to the organization’s success. For more information, please visit software.dell.com/products/kitengaanalytics-suite/. Share: 5 Advanced analytical tools like Kitenga Analytics Suite enable companies to combine and analyze public and private documents relating to intellectual property, including patent filings, contracts, documentation and software source code.
    • For More Information © 2013 Dell, Inc. ALL RIGHTS RESERVED. This document contains proprietary information protected by copyright. No part of this document may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying and recording for any purpose without the written permission of Dell, Inc. (“Dell”). Dell, Dell Software, the Dell Software logo and products—as identified in this document—are registered trademarks of Dell, Inc. in the U.S.A. and/or other countries. All other trademarks and registered trademarks are property of their respective owners. The information in this document is provided in connection with Dell products. No license, express or implied, by estoppel or otherwise, to any intellectual property right is granted by this document or in connection with the sale of Dell products. EXCEPT AS SET FORTH IN DELL’S TERMS AND CONDITIONS AS SPECIFIED IN THE LICENSE AGREEMENT FOR THIS PRODUCT, About Dell Software Dell Software helps customers unlock greater potential through the power of technology—delivering scalable, affordable and simple-to-use solutions that simplify IT and mitigate risk. The Dell Software portfolio addresses five key areas of customer needs: data center and cloud management, information management, mobile workforce management, security and data protection. This software, when combined with Dell hardware and services, drives unmatched efficiency and productivity to accelerate business results. www.dellsoftware.com. If you have any questions regarding your potential use of this material, contact: Dell Software 5 Polaris Way Aliso Viejo, CA 92656 www.dellsoftware.com Refer to our Web site for regional and international office information. Share: 6 WhitePaper-EmpoweringBusiness-US-PL-2013-9-6 DELL ASSUMES NO LIABILITY WHATSOEVER AND DISCLAIMS ANY EXPRESS, IMPLIED OR STATUTORY WARRANTY RELATING TO ITS PRODUCTS INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTY OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR NON-INFRINGEMENT. IN NO EVENT SHALL DELL BE LIABLE FOR ANY DIRECT, INDIRECT, CONSEQUENTIAL, PUNITIVE, SPECIAL OR INCIDENTAL DAMAGES (INCLUDING, WITHOUT LIMITATION, DAMAGES FOR LOSS OF PROFITS, BUSINESS INTERRUPTION OR LOSS OF INFORMATION) ARISING OUT OF THE USE OR INABILITY TO USE THIS DOCUMENT, EVEN IF DELL HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. Dell makes no representations or warranties with respect to the accuracy or completeness of the contents of this document and reserves the right to make changes to specifications and product descriptions at any time without notice. Dell does not make any commitment to update the information contained in this document.