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Published By: DATAVERSITY     Published Date: Nov 05, 2014
Ask any CEO if they want to better leverage their data assets to drive growth, revenues, and productivity, their answer will most likely be “yes, of course.” Ask many of them what that means or how they will do it and their answers will be as disparate as most enterprise’s data strategies. To successfully control, utilize, analyze, and store the vast amounts of data flowing through organization’s today, an enterprise-wide approach is necessary. The Chief Data Officer (CDO) is the newest member of the executive suite in many organizations worldwide. Their task is to develop and implement the strategies needed to harness the value of an enterprise’s data, while working alongside the CEO, CIO, CTO, and other executives. They are the vital “data” bridge between business and IT. This paper is sponsored by: Paxata and CA Technologies
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chief data officer, cdo, data, data management, research paper, dataversity
    
DATAVERSITY
Published By: DATAVERSITY     Published Date: Jul 06, 2015
The growth of NoSQL data storage solutions have revolutionized the way enterprises are dealing with their data. The older, relational platforms are still being utilized by most organizations, while the implementation of varying NoSQL platforms including Key-Value, Wide Column, Document, Graph, and Hybrid data stores are increasing at faster rates than ever seen before. Such implementations are causing enterprises to revise their Data Management procedures across-the-board from governance to analytics, metadata management to software development, data modeling to regulation and compliance. The time-honored techniques for data modeling are being rewritten, reworked, and modified in a multitude of different ways, often wholly dependent on the NoSQL platform under development. The research report analyzes a 2015 DATAVERSITY® survey titled “Modeling NoSQL.” The survey examined a number of crucial issues within the NoSQL world today, with focus on data modeling in particular.
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DATAVERSITY
Published By: Ted Hills     Published Date: Mar 08, 2017
This paper explores the differences between three situations that appear on the surface to be very similar: a data attribute that may occur zero or one times, a data attribute that is optional, and a data attribute whose value may be unknown. It shows how each of these different situations is represented in Concept and Object Modeling Notation (COMN, pronounced “common”). The theory behind the analysis is explained in greater detail by three papers: Three-Valued Logic, A Systematic Solution to Handling Unknown Data in Databases, and An Approach to Representing Non-Applicable Data in Relational Databases.
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Ted Hills
Published By: Ted Hills     Published Date: Mar 08, 2017
Much has been written and debated about the use of SQL NULLs to represent unknown values, and the possible use of three-valued logic. However, there has never been a systematic application of any three-valued logic to use in the logical expressions of computer programs. This paper lays the foundation for a systematic application of three-valued logic to one of the two problems inadequately addressed by SQL NULLs.
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Ted Hills
Published By: Ted Hills     Published Date: Mar 08, 2017
Ever since Codd introduced so-called “null values” to the relational model, there have been debates about exactly what they mean and their proper handling in relational databases. In this paper I examine the meaning of tuples and relations containing “null values”. For the type of “null value” representing unknown data, I propose an interpretation and a solution that is more rigorously defined than the SQL NULL or other similar solutions, and which can be implemented in a systematic and application-independent manner in database management systems.
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Ted Hills
Published By: Ted Hills     Published Date: Mar 08, 2017
Ever since Codd introduced so-called “null values” to the relational model, there have been debates about exactly what they mean and their proper handling in relational databases. In this paper I examine the meaning of tuples and relations containing “null values”. For the type of “null value” representing that data are not applicable, I propose an interpretation and a solution that is more rigorously defined than the SQL NULL or other similar solutions, and which can be implemented in a systematic and application-independent manner in database management systems.
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Ted Hills
Published By: Io-Tahoe     Published Date: Jan 15, 2019
The white paper addresses the problem of managing data in an increasingly complex, hybrid and distributed world; acknowledging that it is too great even for highly-skilled analysts and scientists to handle alone. To solve this problem, organizations should understand why a single unified product from smart data discovery to AI-driven data catalog can vastly reduce the time it takes to do the kind of analytics that are necessary for digital transformation.
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Io-Tahoe
Published By: CData     Published Date: Jan 04, 2019
The growth of NoSQL continues to accelerate as the industry is increasingly forced to develop new and more specialized data structures to deal with the explosion of application and device data. At the same time, new data products for BI, Analytics, Reporting, Data Warehousing, AI, and Machine Learning continue along a similar growth trajectory. Enabling interoperability between applications and data sources, each with a unique interface and value proposition, is a tremendous challenge. This paper discusses a variety of mapping and flattening techniques, and continues with examples that highlight performance and usability differences between approaches.
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data architecture, data, data management, business intelligence, data warehousing
    
CData
Published By: Couchbase     Published Date: Dec 04, 2014
Interactive applications have changed dramatically over the last 15 years. In the late ‘90s, large web companies emerged with dramatic increases in scale on many dimensions: · The number of concurrent users skyrocketed as applications increasingly became accessible · via the web (and later on mobile devices). · The amount of data collected and processed soared as it became easier and increasingly · valuable to capture all kinds of data. · The amount of unstructured or semi-structured data exploded and its use became integral · to the value and richness of applications. Dealing with these issues was more and more difficult using relational database technology. The key reason is that relational databases are essentially architected to run a single machine and use a rigid, schema-based approach to modeling data. Google, Amazon, Facebook, and LinkedIn were among the first companies to discover the serious limitations of relational database technology for supporting these new application requirements. Commercial alternatives didn’t exist, so they invented new data management approaches themselves. Their pioneering work generated tremendous interest because a growing number of companies faced similar problems. Open source NoSQL database projects formed to leverage the work of the pioneers, and commercial companies associated with these projects soon followed. Today, the use of NoSQL technology is rising rapidly among Internet companies and the enterprise. It’s increasingly considered a viable alternative to relational databases, especially as more organizations recognize that operating at scale is more effectively achieved running on clusters of standard, commodity servers, and a schema-less data model is often a better approach for handling the variety and type of data most often captured and processed today.
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database, nosql, data, data management, white paper, why nosql, couchbase
    
Couchbase
Published By: ASG     Published Date: Apr 02, 2014
This Case Study focuses on a highly successful data lineage project between ASG Software Solutions and a major global financial institution. The initial project which began in 2011 with the primary goal of achieving greater control, awareness, and ownership over the institution’s data assets due to new regulatory and federal audit controls. As the project progressed and the positive relationship between ASG and the Bank deepened, all stakeholders involved began to see much broader potential for the entire project than originally envisioned.
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metadata, data, data management, white paper, case study
    
ASG
Published By: Embarcadero     Published Date: Oct 21, 2014
Metadata defines the structure of data in files and databases, providing detailed information about entities and objects. In this white paper, Dr. Robin Bloor and Rebecca Jowiak of The Bloor Group discuss the value of metadata and the importance of organizing it well, which enables you to: - Collaborate on metadata across your organization - Manage disparate data sources and definitions - Establish an enterprise glossary of business definitions and data elements - Improve communication between teams
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data, data management, enterprise data management, enterprise information management, metadata, robin bloor, rebecca jozwiak, embarcadero
    
Embarcadero
Published By: Embarcadero     Published Date: Jul 23, 2015
Whether you’re working with relational data, schema-less (NoSQL) data, or model metadata, you need a data architecture that can actively leverage information assets for business value. The most valuable data has high quality, business context, and visibility across the organization. Check out this must-read eBook for essential insights on important data architecture topics.
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Embarcadero
Published By: MarkLogic     Published Date: Jun 17, 2015
Modern enterprises face increasing pressure to deliver business value through technological innovation that leverages all available data. At the same time, those enterprises need to reduce expenses to stay competitive, deliver results faster to respond to market demands, use real-time analytics so users can make informed decisions, and develop new applications with enhanced developer productivity. All of these factors put big data at the top of the agenda. Unfortunately, the promise of big data has often failed to deliver. With the growing volumes of unstructured and multi-structured data flooding into our data centers, the relational databases that enterprises have relied on for the last 40-years are now too limiting and inflexible. New-generation NoSQL (“Not Only SQL”) databases have gained popularity because they are ideally suited to deal with the volume, velocity, and variety of data that businesses and governments handle today.
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data, data management, databse, marklogic, column store, wide column store, nosql
    
MarkLogic
Published By: TopQuadrant     Published Date: Mar 21, 2015
Data management is becoming more and more central to the business model of enterprises. The time when data was looked at as little more than the byproduct of automation is long gone, and today we see enterprises vigorously engaged in trying to unlock maximum value from their data, even to the extent of directly monetizing it. Yet, many of these efforts are hampered by immature data governance and management practices stemming from a legacy that did not pay much attention to data. Part of this problem is a failure to understand that there are different types of data, and each type of data has its own special characteristics, challenges and concerns. Reference data is a special type of data. It is essentially codes whose basic job is to turn other data into meaningful business information and to provide an informational context for the wider world in which the enterprise functions. This paper discusses the challenges associated with implementing a reference data management solution and the essential components of any vision for the governance and management of reference data. It covers the following topics in some detail: · What is reference data? · Why is reference data management important? · What are the challenges of reference data management? · What are some best practices for the governance and management of reference data? · What capabilities should you look for in a reference data solution?
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data management, data, reference data, reference data management, top quadrant, malcolm chisholm
    
TopQuadrant
Published By: TopQuadrant     Published Date: Jun 01, 2017
This paper presents a practitioner informed roadmap intended to assist enterprises in maturing their Enterprise Information Management (EIM) practices, with a specific focus on improving Reference Data Management (RDM). Reference data is found in every application used by an enterprise including back-end systems, front-end commerce applications, data exchange formats, and in outsourced, hosted systems, big data platforms, and data warehouses. It can easily be 20–50% of the tables in a data store. And the values are used throughout the transactional and mastered data sets to make the system internally consistent.
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TopQuadrant
Published By: TopQuadrant     Published Date: Jun 11, 2018
Data governance is a lifecycle-centric asset management activity. To understand and realize the value of data assets, it is necessary to capture information about them (their metadata) in the connected way. Capturing the meaning and context of diverse enterprise data in connection to all assets in the enterprise ecosystem is foundational to effective data governance. Therefore, a data governance environment must represent assets and their role in the enterprise using an open, extensible and “smart” approach. Knowledge graphs are the most viable and powerful way to do this. This short paper outlines how knowledge graphs are flexible, evolvable, semantic and intelligent. It is these characteristics that enable them to: • capture the description of data as an interconnected set of information that meaningfully bridges enterprise metadata silos. • deliver integrated data governance by addressing all three aspects of data governance — Executive Governance, Representative Governance, and App
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TopQuadrant
Published By: CA Technologies     Published Date: Feb 25, 2016
As combinations of both internal and externally-imposed business policies imply dependencies on managed data artifacts, organizations are increasingly instituting data governance programs to implement processes for ensuring compliance with business expectations. One fundamental aspect of data governance involves practical application of business rules to data assets based on data elements and their assigned values. Yet despite the intent of harmonizing data element definitions and resolution of data semantics and valid reference values, most organizations rarely have complete visibility into the metadata associated with enterprise data assets.
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CA Technologies
Published By: MapR Technologies     Published Date: Mar 29, 2016
Add Big Data Technologies to Get More Value from Your Stack Taking advantage of big data starts with understanding how to optimize and augment your existing infrastructure. Relational databases have endured for a reason – they fit well with the types of data that organizations use to run their business. These types of data in business applications such as ERP, CRM, EPM, etc., are not fundamentally changing, which suggests that relational databases will continue to play a foundational role in enterprise architectures for the foreseeable future. One area where emerging technologies can complement relational database technologies is big data. With the rapidly growing volumes of data, along with the many new sources of data, organizations look for ways to relieve pressure from their existing systems. That’s where Hadoop and NoSQL come in.
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MapR Technologies
Published By: MapR Technologies     Published Date: Aug 01, 2018
How do you get a machine learning system to deliver value from big data? Turns out that 90% of the effort required for success in machine learning is not the algorithm or the model or the learning - it's the logistics. Ted Dunning and Ellen Friedman identify what matters in machine learning logistics, what challenges arise, especially in a production setting, and they introduce an innovative solution: the rendezvous architecture. This new design for model management is based on a streaming approach in a microservices style. Rendezvous addresses the need to preserve and share raw data, to do effective model-to-model comparisons and to have new models on standby, ready for a hot hand-off when a production model needs to be replaced.
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MapR Technologies
Published By: Cambridge Semantics     Published Date: Mar 13, 2015
As the quantity and diversity of relevant data grows within and outside the enterprise, how can IT easily deploy secure governed solutions that allow business users to identify, extract, link together and derive value from the right data at the right time, at big data scale, while keeping up with ever changing business needs? Smart Enterprise Data Management (Smart EDM) is new, sensible paradigm for managing enterprise data. Anzo Smart Data solutions allow IT departments and their business users to quickly and flexibly access all of their diverse data. Based upon graph data models and Semantic data standards, Anzo enables users to easily perform advanced data management and analytics through the lens of their business at a fraction of the time and cost of traditional approaches, while adhering to the governance and security required by enterprise IT groups. Download this whitepaper to learn more.
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enterprise data management, data governance, data integration, cambridge semantics
    
Cambridge Semantics
Published By: Cambridge Semantics     Published Date: Aug 17, 2015
As the quantity and diversity of relevant data grows within and outside of the enterprise, business users and IT are struggling to extract maximum value from this data. Current approaches, including the rigid relational data warehouse and the unwieldy Hadoop-only Data Lake, are limited in their ability to provide users and IT with the answers they need with the proper governance and security required. Read this whitepaper to learn how The Anzo Smart Data Lake from Cambridge Semantics solves these problems by disrupting the way IT and business alike manage and analyze data at enterprise scale with unprecedented flexibility, insight and speed.
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Cambridge Semantics
Published By: Cloudant - an IBM Company     Published Date: Jun 01, 2015
Whether you're a DBA, data scientist or developer, you're probably considering how the cloud can help modernize your information management and analytics strategy. Cloud data warehousing can help you get more value from your data by combining the benefits of the cloud - speed, scale, and agility - with the simplicity and performance of traditional on-premises appliances. This white paper explores how a cloud data warehouse like IBM dashDB can reduce costs and deliver new business insights. Readers will learn about: - How data warehousing-as-a-service helps you scale without incurring extra costs - The benefits of in-database analytics in a cloud data warehouse - How a cloud data warehouse can integrate with the larger ecosystem of business intelligence tools, both on prem and off prem
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nosql, ibm, dashdb, database, cloud
    
Cloudant - an IBM Company
Published By: MemSQL     Published Date: Jun 25, 2014
Emerging business innovations focused on realizing quick business value on new and growing data sources require “hybrid transactional and analytical processing” (HTAP), the notion of performing analysis on data directly in an operational data store. While this is not a new idea, Gartner reports that the potential for HTAP has not been fully realized due to technology limitations and inertia in IT departments. MemSQL offers a unique combination of performance, flexibility, and ease of use that allows companies to implement HTAP to power their business applications.
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MemSQL
Published By: Pentaho     Published Date: Jul 23, 2014
Pentaho Analytics for MongoDB will teach you MongoDB and Pentaho integration points and developer skills needed to create turnkey analytic solutions that deliver insight and drive value for your organization.
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nosql, ebook, pentaho, analytics, business intelligence, mongodb
    
Pentaho
Published By: Access Sciences     Published Date: Sep 07, 2014
Few organizations have fully integrated the role of the Data Steward due to concerns about additional project complexity, time away from other responsibilities or insufficient value in return. The principles of the Agile methodology (whether or not Agile is followed for projects) can offer guidance in making the commitment to designating and empowering the Data Steward role. By placing insightful people in a position to connect innovators, respond to change and spur development aligned with business activities, organizations can expect to see a more efficient and effective use of their information assets.
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Access Sciences
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