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A question of confidence- governmentIT article
Whilst fears about data accuracy have slowed the adoption of information sharing in the public sector, a growing number of authorities are transforming their service delivery with cross-departmental data integrity.
Find out more about governmentIT at their website
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Financial Service and MiFID
MiFID, the European Union’s “Market in Financial Instruments Directive” comes into force on November 1 2007. It is meant to increase investor protection, increase market transparency and efficiency, and enable EU firms to work in any EU state. This document is a guide to joined up implementation of MiFID, CRD and ERR. |
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Police and MOPI
MOPI forms a package that Chief Officers will be bound to implement under the terms of the Police Act 1996. This document is intended as a guide to implementing MOPI. |
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Exploiting the next technology sea change
The dramatic shift toward internet based services and pay as you go software, are the vanguard of a revolution which will disrupt the existing IT industry revenue model and deliver greater value to clients. |
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Pooling intelligence from structured and unstructured data
20% of an organisation's intelligence resides in structured data such as spreadsheets and 80% in unstructured data such as emails. Using one without the other gives an incomplete picture. |
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Criminal Justice and Semantics
Semantics may be a new term to many of us but it will transform information
technology in a way similar to the introduction of databases. |
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Supply Chain data Synchronisation
Supply chain efficiency depends on the effective transfer of accurate data about items between trading partners. 100% control of data is needed to make the supply chain work. This paper describes how to tackle this issue… |
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The Equation between Semantics and Data Quality
Semantics makes the meaning of data items, business rules and relationships between different parts of systems explicit. But how can we uncover meaning, if we do not know which meanings are spurious and which are genuine? Semantics is therefore inseparable from data quality…. |
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The Essential Ingredient: Why Business Intelligence needs data quality
Business Intelligence (BI) traditionally delivered long-term ROI for companies that could benefit from economies of scale. Today, however, a wide range of companies are accumulating large amounts of data on both their customers and their product lines and hoping to benefit from BI technology. Despite this growing interest in BI, many companies are still ignoring the fact that a BI tool is only as good as the quality of data it is processing. |
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Data quality: the virtual data model’ - produced by Dal Cais Research
This paper argues that, as integration technology becomes increasingly commoditised, enterprises are less concerned about managing technology and more concerned about improving the efficiency of the way they manage data and information. |
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'Data quality: an introduction’ - produced by Dal Cais Research
This paper aims to provide that understanding. It looks at some common data quality problems faced by the enterprise, and introduces the IT tools most commonly used to address them.
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BPM, Data Quality and the future of data sharing - produced by Dal Cais Research
Modern data sharing projects stand and fall on their ability to reconcile two different disciplines. The first discipline belongs to the board-room. The second discipline concerns itself, not with processes, but with the accuracy of the data used within them ... |
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Trust and Location Services: The role of data matching within LBS - produced by Dal Cais Research
Good data-matching tools are essential to any customer facing service that relies on multiple partners. Such tools enable the service provider to independently assess the relative accuracy of data from multiple, competing elements, and have confidence in the quality of the service they deliver to their customer ... |
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Extreme Data Quality: Lessons from the Public Sector
Data quality is not a new problem, and companies and consultants have developed in-house tools that address its various facets from the beginning of IT. This need for a systematic approach to the problem is new however, and a direct consequence of the explosion in data sharing. Consultants that specialise in data quality may well enjoy a new boom in contracts once serviceorientated
architecture and web services take off. |
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‘Data Diagnostics Tool' - the Future of Data Quality
Data quality is not an IT process it is an organisational strategy. Organisations need to accurately measure the size of a data quality problem, produce detailed reports on actions to be taken, and to monitor/evaluate the effectiveness of these actions… |
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Sweating the IT Assets
The most sophisticated IT is brought down by bad data but experts can’t tell if it is a data, an IT or a business process problem. A lot of the time they can’t tell whether IT is effective or if it is surplus…. |
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Real Time Enterprise Without Breaking the Bank
If networked computers transmit data at the speed of light how come Deutsche Bank publishes its accounts only 4 weeks faster than in 1904? As Gartner states, 25% of data in top companies is suspect and 99% of CFO’s produce accounts manually in spreadsheets. The cost of “real time” will exceed the payback unless data quality is controlled… |
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Citizen Identifiers, Biometrics and the Biographical Footprint
No government has successfully introduced and maintained an authenticated unique identifier for each citizen. For example, in the UK there are 83m national insurance numbers but only 47m eligible citizens. If 98% accurate biometric data is cross-referenced with 56% accurate NI data the outcome is only 55% reliable… |
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The Data Quality Risk to NHS Modernisation
There are about 3 million more patients registered with GP’s than the estimated 50 million resident in England. There is a 1 in 15 chance of NHS Care Records being linked via an NHS number to the wrong patient. NHS mistakes due to mistaken identity can be fatal… |
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Glendale Police, Arizona: a Law Enforcement Data Sharing Project
21st century law enforcement relies on analyzing high data volumes from multiple agencies in a rapidly changing environment, to track back and fix errors and to apply data protection for data sharing. This paper describes such a solution at present being trialled in 4 US states – Arizona, Nebraska, Florida and South Carolina… |
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Data Protection, Privacy Enhancing Technology and Data
Without data accuracy there can be no effective data protection. For example, wrongful arrests due to identity mix-ups are common. The real offender forfeits privacy in the public interest. The innocent victim forfeits privacy due to duplicate or wrong data. This paper explores the relationship between data protection legislation and data accuracy… |
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