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Files from McKnight Available for Download
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1. Introducing Teradata Columnar
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| The unique innovation by Teradata, in Teradata 14, is to add columnar structure to a table, effectively mixing row structure, column structure and multi-column structure directly in the DBMS which already powers many of the largest data warehouses in the world. With intelligent exploitation of Teradata Columnar in Teradata 14k, there is no longer the need to go outside the data warehouse DBMS for the power of performance that columnar provides, and it is no longer necessary to sacrifice robustness and support in the DBMS that holds the post-operational data. |
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2. Best Practices in the Use of Columnar Databases
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Columnar databases are becoming an essential component of an enterprise infrastructure for the storage of data designed to run specific workloads. When an organization embraces the value of performance, it must do everything it can to remove barriers to the delivery of the right information at the right time to the right people and systems. There is no "ERP" for post-operational data. No one-size-fits-all system. Some gave that role to the relational, row-based data warehouse, but that ship has sailed. In addition to columnar databases, very-large data stores like Hadoop, real-time stream processing, and data virtualization are required today to bring together result sets across all data systems. This paper focuses on conveying an understanding of columnar databases and the proper utilization of columnar databases within the enterprise. |
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3. Strategic Information Management Technology: Workloads Matter in Managing Gigabytes to Petabytes
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This white paper is intended to provide a consolidated starting point for information technology managers who need to select systems to store retrievable analytic data for their business. The paper covers recommended use of information stores including relational row-based data warehouses and marts, multi-dimensional databases, columnar databases and MapReduce. |
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4. Data Driven Design for Data Warehousing
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In the data modeling area, rather than starting out with the grandiose goal of building the enterprise data model, as if it were a respectable end in itself, to be successful, data warehouse teams must leave the spotlight firmly on the business deliverables. The data model, being a means to an end, is grounded in reality and constructed through a series of iterative progressions, staying in synch and not ahead of the partner components. |
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5. The CIOs Guide to NoSQL
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Co-authored with Dan McCreary. NoSQL is a new and fast-growing category of data management technologies that uses non-relational database architectures (hence NoSQL, or Not-Only SQL). NoSQL is not the best solution for every data management requirement, however it is often better suited to handle the requirements of high-performance, web-scalable systems and big data analysis. Organizations like Facebook, Twitter, Netflix and Yahoo are notable examples of innovators which have used NoSQL solutions to gain greater scale and performance, and at a fraction of the cost of traditional relational database systems.
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6. Making Information Management the Foundation of the Future
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More complex and demanding business environments lead to more heterogeneous
systems environments. This, in turn, results in requirements to synchronize master data. Master Data Management (MDM) is an essential discipline to get a single, consistent view of an enterprise's core business entities – customers, products, suppliers, and
employees. MDM solutions enable enterprise-wide master data synchronization. Given that effective master data for any subject area requires input from multiple applications and business units, enterprise master data needs a formal management system. Business approval, business process change, and capture of master data at optimal, early points in the data lifecycle are essential to achieving true enterprise master data. |
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7. Mobile Business Intelligence: When Mobility Matters
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Mobile business intelligence is a process, not a project, and a journey rather than a destination. The case studies included represent two forms that mobile business intelligence can take to empower the mobile worker and port existing applications. This paper discusses two different companies, their environments, reasons for going mobile, and key success factors. The examples provide a framework of information architecture evaluation reference points, lay out options for mobile business intelligence, and provide best practices for those considering, planning, or doing some form of mobile business intelligence evaluation.
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8. Analyzing the Potential of the Contact Center Data Mart
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| Customer contact center data contains hidden nuggets of insight about customers, products, and business operations, and it provides the foundation for effective customer relationship management (CRM). Mining this data for insights can be daunting, however. |
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9. Data Mart Consolidation: Repenting for Sins of the Past
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| This paper details the process of DMC at eight different organizations while capturing the keys to success from each. These case studies were specifically selected to demonstrate several variations on the concept of consolidation. While there is no such thing as a cookie-cutter DMC process, there are common best practices and lessons to be shared. |
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10. Chapter 1: 90 Days to Success in Consulting
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| Chapter 1 of "90 Days to Success in Consulting" where William defines consulting, helps you define your consulting profile, talks about consulting in hard times and adding real value to clients. |
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11. Improving Data Quality Through Data Modeling
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| Sections include The State of Enterprise Data Quality, Data Quality Defined, The Causes of Poor Data Quality, Modeling Cures for Data Quality Assurance, Modeling for Data Entry, Accepting External Source Data, Modeling Master Data Management and Data Integration and Cleaning Up to Get Started. |
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12. Evolving Analytic Platforms - A True Story
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| New technologies threaten the status quo when it comes to analytic price-performance and simplicity. This research paper describes one client and their quest for data warehouse modernization. |
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13. Chapter 8 of "90 Days to Success in Consulting": The Role of the Consultant
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| Consulting is a personal relationship. You do not want to compound the fact (and it is a fact) that a client’s problems are usually people problems and not technical ones. The consulting you aspire to means you must deal with the people issues behind the complex problems at a client. Though brought in under the guise of implementing a technical solution to solve a problem, I have seldom seen real problems
be caused by technology. Technical implementations, if they go well, will undoubtedly gloss over corporate problems for a while,but the corporate problems will return. Attending to the post-implementation client roles and responsibilities for success is part
of true consulting. |
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14. Data Warehouse Buyer's Guide
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| Welcome to SearchDataManagement.com’s DataWarehouse Platforms Directory.
This directory is designed to be a valuable resource for those getting started with research or evaluating optimized data warehouses. Inside, you’ll find basic information
about vendors and the platforms they sell. Each listing is accompanied by
a short description and a long description, including limited information about functionality and product use. You’ll find products for businesses of all sizes as well as products that can be deployed on-demand and on-premise. Use this list to
get started with the evaluation process. |
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15. Business Intelligence Buyer's Guide
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| Welcome to SearchDataManagement.com’s Business Intelligence Product
Directory. This directory is designed to be a valuable resource for those getting started with research or evaluating vendors in the business intelligence market. Inside, you’ll find basic information about the major vendors in the business intelligence market and the products they sell. Each listing is accompanied by a short description and a long description including limited information about functionality
and product use. You’ll find products for businesses of all sizes as well as products that can be deployed on-demand and on-premise. Use this list to get started with the evaluation process. |
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16. The Selection of Netezza For A Data Warehouse Platform
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| Netezza is a leading data warehouse appliance vendor. Their preconfigurations range from 1.0 TB to 100 TB. Netezza's philosophy is that parallelism is a good thing and they take parallelism to a new level. They utilize an SMP node and up to 896 single-CPU SPUs (snippet processing units) configured in an MPP arrangement in the overall architecture, referred to as AMPP for asymmetric massively parallel processing. The SPUs are connected by a Gigabyte Ethernet, which serves the function of the interconnect. |
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17. Achieving Business Intelligence Query Performance
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| "The biggest challenge is, not surprisingly, query performance...many organizations manage their query performance issues by actually limiting user queries." |
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18. Overall Approach To Data Quality ROI
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| Data quality is an elusive subject that can defy measurement and yet be critical enough to derail any single IT project, strategic initiative, or even a company as a whole. The data layer of an organization is a critical component because it is so easy to ignore the quality of that data or to make overly optimistic assumptions about its efficacy. Having data quality as a focus is a business philosophy that aligns strategy, business culture, company information, and technology in order to manage data to the benefit of the enterprise. Put simply, it is a competitive strategy. Just as our markets today expect operational excellence, rich product features, everyday low prices, high product quality, and short time-to-market, one day they will also expect data quality. In the meantime, each company has the opportunity to differentiate itself through the quality of its data. Leading companies are now defining what the marketplace data quality expectation will be. |
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19. Modernizing And Advancing Information Management Across The Enterprise
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| Information management is key to business growth. It is a competitive advantage with the same merit as product knowledge and inventory availability. These once-held corporate competitive advantages are now considered tickets to entry and rather indistinguishable. Regulatory protections are largely gone, and when comparing your company's features and functions, demo parity is the norm, especially within the larger industries. |
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20. Data Warehouse Solutions For Achieving Compliance And Managing Operational Risk
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| Risky derivatives investments by major banks gone bad — really bad, as in billions of dollars bad —
and unencrypted data tapes from two major U.S. financial institutions going missing while being transferred to backup centers are types of incidents which make it clear that appropriate safeguards are not in place in our enterprises.
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21. A Fresh Look At Data Warehouse Technology: Introducing The Data Warehouse Appliance
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| appliance
• noun 1 a device designed to perform a specific task. 2 the application of something.
So goes the dictionary definition of an appliance. But how does that fit into our business intelligence world? Well, if you haven't heard, solutions adopting the appliance nomenclature - as in "data warehouse appliances" - have emerged as viable short-list solutions for new or refurbishing data
warehouse efforts.
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22. Choosing Microsoft SQL SERVER 2005 For Data Warehousing
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| The warehousing of corporate data into an architecture from which it can all be accessed is no longer an option for midmarket and large companies that want to be successful. Consolidating the information so that it is readily accessible, and to give the team visibility for reporting, analytics,
spot queries, and predictive capabilities is now necessary. Fortunately, pioneers established this long ago and this requirement is met in the field of data warehousing (DW) and business intelligence (BI). There is a lot more to achieving information access than simply copying data from one system to another. Data warehouses are now the major use of database management systems.
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23. Choosing A DBMS For Data Warehousing
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| When making product decisions for a data warehousing (DW) environment, the
database management system (DBMS) is the most important. Though not the most
expensive component, it should be chosen with care and with active discernment over the issues and marketing messages.
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24. Healthcare Business Intelligence: The Time is (Still) Now
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| In "Building Business Intelligence: Business Intelligence in Healthcare Today" featured in the May 2005 issue of Information Management, William McKnight stated, "Perhaps in no other industry, at any other time, was there such a need for business intelligence as there is in healthcare today."
Well, it's two years later; and while we have moved the needle forward a little, by and large that statement remains true, only more pronounced. Some forward-thinking organizations in healthcare have seized upon the opportunity and have improved their access to clean and correct patient,
provider and outcome metrics. Some have become the evidence-based culture mentioned then. But far too often, entrenched information is found in silos and conservative cultures are working against progress.
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25. Starting Small,But Thinking Large And Scaling Fast
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| As companies take steps to manage their information asset, choosing a platform and database management system (DBMS) is absolutely fundamental. In fact, the platform is the foundation of architecture and business intelligence and the starting point for tool selection, consultancy hires, and more. In short, a company's platform is key in defining its information culture. |
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