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Published By: CMMI Institute     Published Date: Sep 03, 2014
To drive strategic insights that lead to competitive advantage, businesses must make the best and smartest use of today’s vast amount of data. To accomplish this, organizations need to apply a collaborative approach to optimizing their data assets. For organizations that seek to evaluate and improve their data management practices, CMMI® Institute has developed the Data Management Maturity (DMM)? model to bridge the perspective gap between business and IT. Download the white paper Why is Measurement of Data Management Maturity Important? to enable you to: - Empower your executives to make better and faster decisions using a strategic view of their data. - Achieve the elusive alignment and agreement between the business and IT - Create a clear path to increasing capabilities
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white paper, enterprise data management, data model, data modeling, data maturity model, cmmi institute
    
CMMI Institute
Published By: CMMI Institute     Published Date: Mar 21, 2016
CMMI Institute®’s Data Management Maturity (DMM)SM framework enables organizations to improve data management practices across the full spectrum of their business. It is a unique, comprehensive reference model that provides organizations with a standard set of best practices to assess their capabilities, strengthen their data management program, and develop a custom roadmap for improvements that align with their business goals.
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CMMI Institute
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
Published By: AT&T     Published Date: Sep 11, 2014
The age of Big Data is upon us. Storage costs are going down, and data analytics is becoming more capable and more user-friendly. Even your auto mechanic will be storing a petabyte of data soon. Big Data will give businesses new insights and help improve operations. With these new tools come questions about how to use them. But your mechanic knows more about fixing a transmission than developing a Hadoop cluster, and similar concerns hold true for larger enterprises. Businesses everywhere are looking for guidance.
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AT&T
Published By: Melissa Data     Published Date: Oct 27, 2014
Noted SQL Server MVP and founder/editor of SSWUG.org, Stephen Wynkoop shares his take on the challenge to achieve quality data and the importance of the “Golden Record” to an effective data quality regiment. Wynkoop explores the different approaches to achieving the Golden Record - which involves collapsing duplicate records into a single version of the truth – the one single customer view (SCV), and Melissa Data’s unique approach that takes into consideration the actual quality of the contact data as the basis of survivorship.
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data, data management, melissa data, data quality
    
Melissa Data
Published By: Melissa Data     Published Date: Mar 23, 2017
In this eBook published by Melissa, author David Loshin explores the challenges of determining when data values are or are not valid and correct, how these values can be corrected, and how data cleansing services can be integrated throughout the enterprise. This Data Quality Primer eBook gives an overview of the five key aspects of data quality management (data cleansing, address data quality, address standardization, data enhancement, and record linkage/matching), as well as provides practical aspects to introduce proactive data quality management into your organization.
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Melissa Data
Published By: Melissa Data     Published Date: Jan 18, 2018
Maintaining high quality data is essential for operational efficiency, meaningful analytics and good long-term customer relationships. But, when dealing with multiple sources of data, data quality becomes complex, so you need to know when you should build a custom data quality tools over canned solutions. To answer this question, it is important to understand the difference between rules-based data quality, where internal subject matter expertise is necessary – and active data quality, where different domain expertise and resources are required.
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Melissa Data
Published By: Skytree     Published Date: Nov 23, 2014
Critical business information is often in the form of unstructured and semi-structured data that can be hard or impossible to interpret with legacy systems. In this brief, discover how you can use machine learning to analyze both unstructured text data and semi- structured log data, providing you with the insights needed to achieve your business goals.
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log data, machine learning, natural language, nlp, natural language processing, skytree, unstructured data, semi-structured data, data analysis
    
Skytree
Published By: Basho     Published Date: Mar 08, 2015
Many companies still use relational databases as part of the technology stack. However, others are innovating and incorporating NoSQL solutions and as a result they have simplified their deployments, enhanced their availability and reduced their costs. In this whitepaper you will learn: - Why companies choose Riak over a relational database. - How to analyze the decision points you should consider when choosing between relational and Nosql databases - Simple patters for building common applications in Riak using its key/value design Learn how you can lead your organization into this new frontier.
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data, data management, basho, database, nosql, data models
    
Basho
Published By: Basho     Published Date: Sep 30, 2016
The Internet of Things (IoT) or the Internet of Everything is changing the way companies interact with their customers and manage their data. These connected devices generate high volume time series data that can be created in milliseconds. This fast growth of IoT data and other time series data is producing challenges for enterprise applications where data must be collected, saved, and analyzed in the blink of an eye. Your application needs a database built to uniquely handle time series data to ensure your data is continuously available and accurate.Learn about the only NoSQL database optimized for IoT and Time Series data in this technical overview. Riak TS stores and analyzes massive amounts of data and is designed to be faster than Cassandra.
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Basho
Published By: iCEDQ     Published Date: Feb 05, 2015
The demand for using data as an asset has grown to a level where data-centric applications are now the norm in enterprises. Yet data-centric applications fall short of user expectations at a high rate. Part of this is due to inadequate quality assurance. This in turn arises from trying to develop data-centric projects using the old paradigm of the SDLC, which came into existence during an age of process automation. SDLC does not fit with data-centric projects and cannot address the QA needs of these projects. Instead, a new approach is needed where analysts develop business rules to test atomic items of data quality. These rules have to be run in an automated fashion in a business rules engine. Additionally, QA has to be carried past the point of application implementation and support the running of the production environment.
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data, data management, data warehousing, data quality, etl testing, malcolm chisholm
    
iCEDQ
Published By: AnalytixDS     Published Date: Feb 28, 2015
With future business intelligence solutions clearly evolving from data that comes from highly efficient and well behaved systems, to data that comes from the extended enterprise where data is not necessarily so well structured and behaved - Organizations are forced into a more collaborative mode of operation with their core infrastructure being adapted from the consumer space, and to the extent possible, conformed to their existing repositories. This whitepaper attempts to address various challenges consumers face while managing enormous data sets within the context of this complex scenario. Further, we’ll try to answer the question: Is Big Data Governance really that different from traditional data governance initiatives? Finally, we’ll see how AnalytiX™ Mapping Manager™ can help organizations accelerate the development and deployment of a successful Big Data/ Business Intelligence platform and accelerate delivery of all sorts of data – structured, semi-structured as well as unstruc
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big data, big data governance, data governance, analytixds
    
AnalytixDS
Published By: AnalytixDS     Published Date: May 09, 2016
This paper seeks to provide insight into how AnalytiX Data Services can address many of the data related issues that come with combining disparate source data as is often the case in mergers and acquisitions. It will cover some of the best features and functionality offered by AnalytiX Data Services’ flagship product AnalytiX Mapping Manager, which have specific capabilities that are of high value in this scenario.
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AnalytixDS
Published By: AnalytixDS     Published Date: Mar 21, 2017
AnalytiX DS Mapping Manager is an award winning platform that has enabled organizations worldwide to meet challenges, bring wide-spread collaboration, and put structure and governance in place, regardless of the size of their architecture, data, or user base. Scalable, adaptable, and value-added through its feature rich modules, it can make a difference as to whether you see BASEL III as a challenge or an opportunity.
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AnalytixDS
Published By: AnalytixDS     Published Date: May 04, 2018
The General Data Protection Regulation (GDPR) is a regulation by which the European Parliament, the Council of the European Union and the European Commission intend to strengthen and unify data protection for all individuals within the European Union (EU). It also addresses the export of personal data outside the EU. The General Data Protection Regulation (GDPR) will go into effect from May 25 2018, making organizations accountable for personal data protection including how and where data is stored and how it is processed within the organization. Get ready for the most comprehensive governance and automation platform in the industry.
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AnalytixDS
Published By: Expert System     Published Date: Mar 19, 2015
Establishing context and knowledge capture In today’s knowledge-infused world, it is vitally important for organizations of any size to deploy an intuitive knowledge platform that enables delivery of the right information at the right time, in a way that is useful and helpful. Semantic technology processes content for meaning, allowing for the ability to understand words in context: it allows for better content processing and interpretation, therefore enabling content organization and navigation, which in turn increases findability.
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enterprise data management, unstructured data, semantic technology, expert system
    
Expert System
Published By: CapTech     Published Date: May 26, 2015
Big Data is the future of business. According to CloudTweaks.com, as much as 2.5 quintillion bytes of data are produced each day, with most of this data being captured by Big Data. With its ability to transfer all data sources all into one centralized place, Big Data provides opportunities, clearer visions, customer conversations and transactions. However, with the dazzling big promise of Big Data comes a potentially huge letdown. If this vast pool of information resources is not accessible or usable, it becomes useless. This paper examines strategies for building the most value into your Big Data system by enabling process controls to effectively mine, access and secure Big Data.
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big data, captech, data, data management, nosql
    
CapTech
Published By: Neo Technology     Published Date: Jun 28, 2015
The future of Master Data Management is deriving value from data relationships which reveal more data stories that become more and more important to competitive advantage as we enter into the future of data and business analytics. MDM will be about supplying consistent, meaningful views of master data and being able to unify data into one location, especially to optimize for query performance and data fit. Graph databases offer exactly that type of data/performance fit. Use data relationships to unlock real business value in MDM: - Graphs can easily model both hierarchical and non-hierarchical master data - The logical model IS the physical model making it easier for business users to visualize data relationships - Deliver insights in real-time from data relationships in your master data - Stay ahead of the business with faster development Download and read the white paper Your Master Data Is a Graph: Are You Ready? to learn why your master data is a graph and how graph databases like Neo4j are the best technologies for MDM.
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database, nosql, graph database, big data, master data management, mdm
    
Neo Technology
Published By: Neo Technology     Published Date: Feb 15, 2018
By itself, data offers finite value. But when connected, its value is infinite. Discover how enterprise organizations such as Airbnb, eBay and Telia used connected data and graph technology in order to create a sustainable competitive advantage. This white paper shows business leaders how to take advantage of data relationships with graph technology.
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Neo Technology
Published By: Ted Hills     Published Date: Jul 02, 2015
Entity-relationship (E-R) modeling is a tried and true notation for use in designing Structured Query Language (SQL) databases, but the new data structures that Not-Only SQL (NOSQL) DBMSs make possible can’t be represented in E-R notation. Furthermore, E-R notation has some limitations even for SQL database design. This article shows how a new notation, the Conceptual and Objective Modeling (COM) notation, is able to represent NOSQL designs that are beyond the reach of E-R notation. At the end, it gives a peek into the tutorial workshop to be given at the 2015 NOSQL Conference in San Jose, CA, US, in August, which will provide opportunities to apply COM notation to practical problems.
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nosql, sql, data modeling, data model, er modeling, entity relationship, database, relational, dbms, schema-less, xml, conceptual, logical, physical
    
Ted Hills
Published By: GBG Loqate     Published Date: Jul 09, 2015
Businesses are vulnerable when they assume that their data is accurate, because they are almost always losing money without their knowledge. When it comes to data quality, the problems that you don’t suspect are often worse and more pervasive than the ones you are aware of. Addresses are subject to their own specific set of rules. Detecting and correcting address errors is a complex problem, and one that can only be solved with specialized software.
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data, data management, data quality, loqate
    
GBG Loqate
Published By: VoltDB     Published Date: Jul 09, 2015
What is fast data? It's data in motion, and it creates Big Data. But handling it requires a radically different approach. Download the Fast Data Stack white paper from VoltDB. Learn how to build fast data applications with an in-memory solution that’s powerful enough for real-time stateful operations.
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data, data management, data stack, bug data, voltdb, database, nosql
    
VoltDB
Published By: VoltDB     Published Date: Feb 12, 2016
The need for fast data applications is growing rapidly, driven by the IoT, the surge in machine-to-machine (M2M) data, global mobile device proliferation, and the monetization of SaaS platforms. So how do you combine real-time, streaming analytics with real-time decisions in an architecture that’s reliable, scalable, and simple? In this report, Ryan Betts and John Hugg from VoltDB examine ways to develop apps for fast data, using pre-defined patterns. These patterns are general enough to suit both the do-it-yourself, hybrid batch/streaming approach, as well as the simpler, proven in-memory approach available with certain fast database offerings.
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VoltDB
Published By: Trillium Software     Published Date: Mar 29, 2016
We are living in a new age in which your business success depends on access to trusted data across more systems and more users faster than ever before. However, your business information is often incomplete, filled with errors, and beyond the reach of people who need it. Whether you’re responsible for technology or information strategy, you need to enable your business to have real-time access to reliable information. Otherwise, your company will be left behind. Download Trillium’s whitepaper, “How to Succeed in the New Age of Data Quality”, to learn how you can create a successful data quality strategy.
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Trillium Software
Published By: Trillium Software     Published Date: Apr 10, 2017
For the 11th consecutive year, the Gartner Magic Quadrant for Data Quality Tools1 research report positions Trillium Software as a leader in the Data Quality Software industry. Data Quality is vital to ensuring trust in your data-driven, decision making business processes. Confidence is the result of a well thought out and executed data quality management strategy and is critical to remaining competitive in a rapidly and ever-changing business world. The 2016 Gartner Magic Quadrant for Data Quality Tools report is a valuable reference, providing the latest insights into the strengths and cautions of leading vendors. Access the report to learn how a leading data quality solution can help you achieve your long-term strategic objectives.
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Trillium Software
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