Showing 17–32 of 61 results
Master Data is data that is shared between computer systems, such as customer, product, asset, location or contract. The management of this data is known as master data management (MDM). The mounting pressure on businesses to increase fiscal data compliance, accountability and transparency has driven a growing number of organizations to put a tentative toe into the waters of MDM. This has in part been fuelled by the explosion of publications in this area aimed at convincing businesses that the route to consistent business information lies in effective management of their master data. There is, however, scant concrete information relating to the motivation and adoption of MDM by business.
Against this background, The Information Difference has conducted a survey in April 2008 into the take-up and adoption of master data management (MDM) software. Data was collected for the survey from 112 participants with 65% representing businesses with revenues in excess of USD 1 billion. Some 56% were from North America and 24% from Europe. The purpose of the study was to gain understanding of amongst other factors the level of take up, the business motivation and the preferred approach to implementation.
The report has 20 pages.
Many authors have recently highlighted in the media the crucial importance of data governance and data quality initiatives to ensure the success of MDM implementations. There is, however, scant information on the approaches being adopted by organizations that have implemented or plan to implement MDM, or indeed those who have chosen not to implement data governance.
We were therefore interested to explore the linkage between data governance, master data and data quality. In particular, to discover how organizations are tackling this area in practice. Additionally, we wanted to understand the scale, scope and success rates of data governance in relation to MDM and data quality initiatives in business.
We have conducted a survey to examine the link between data governance, master data management and data quality. A total of 257 respondents from across the world completed the survey. 56% came from North America and 26% from Europe. Fully two thirds of the respondents were from larger organizations having annual revenues greater than US $ 1 billion (62%). The respondents were drawn from a wide spectrum of industries including banking, finance and manufacturing.
A key finding from the survey is that 31% have already implemented data governance and have had active data governance implementations for a median of 2 years. A further 40% plan to implement within one year. Further, a significant number of organizations (39%) are electing to implement data governance alongside MDM (and data quality).
The report has 51 pages.
This Information Difference survey returns to investigate the relationships between data governance, master data management (MDM) and data quality (DQ). In 2010 we conducted an in depth survey on this topic against the background of considerable media focus on the crucial importance of data governance and data quality initiatives to ensure the success of MDM implementations.
Now, six years on, we return to this area in an effort to once again explore and understand what progress has been made over the years. The aim was to find the current state of practice in data governance, the levels of effort involved and its success rate, and understand how well integrated it is with data quality and MDM initiatives. In other words, to address the question “How Does Your Data Governance Initiative Stack Up?”.
The report has 54 pages.
Recent surveys of the Information Technology market have suggested an overall increase in expenditure in 2015. What proportion of this increase will be devoted to the area of data management (including data governance, data quality, data warehousing and master data management) in 2015? How do you compare with your peers? Indeed, what are organizations planning for the future in this key area?
In this survey we investigated data management spending in depth. Respondents from some 100 companies and organizations across the world were invited to participate.
The report has 27 pages.
Data warehousing has been with us for almost three decades now. More than 25 vendors currently offer a wide range of approaches and software products dedicated to data warehousing. These range from packaged applications to cloud-based solutions and data warehouse appliances. We have recently reviewed this area in our Data Warehouse Landscape 2011.
In the past, many data warehouse implementations gained the reputation of having a high failure rate, often delivering inconsistent data, with the consequence that businesses inevitably lost trust in their reliability. A root cause of this lack of reliability was that little or no attention was paid to ensuring the quality of the data loaded into the warehouse. Has the state of data warehousing in organizations improved over the past two decades?
At The Information Difference, we believe it is important for both organizations and vendors to understand the current state of data warehousing in organizations. We have therefore conducted a survey, sponsored by IBM, aimed at gaining deeper insight into the views of businesses regarding their current data warehousing initiatives.
Some 100 respondents completed the survey from all around the world, the majority (48%) from North America and 35% from Europe. A high proportion (57%) of the respondents were from companies having annual revenues greater than US $ 1 billion; 25% of the respondents were drawn from the banking and financial industries with the remainder covering a wide spectrum of industries.
The report has 32 pages.
or How I learned to stop worrying and love federation
A paper by Andy Hayler published April 2009
Master data management (MDM) has emerged as a key area of information management in the last few years. Initially much of the architectural debate was about whether to be data domain-specific or not – “one hub and for customer and a separate one for product?” – but since around 2006 the industry has increasingly listened to its customers and realised that organisations want a uniform approach to all their master data. However there is still a significant divide about the style of implementation, with some vendors specialising in certain areas, and some confusion has arisen around terms such as “operational MDM”, “registry” and “analytic MDM” used for various approaches to implementing MDM. This paper argues that there is a second architectural dimension that has not had enough attention paid to it, and that is the one of a centralised versus federated approach to MDM.The white paper has 16 pages.
The master data management (MDM) market has seen a battle of ideas over the last couple of years between “cross domain” approaches and specialist hubs for separate classes of master data, particularly “customer” and “product”. In this new white paper Andy Hayler charts this battle and discusses the types of MDM architectures which play out in large enterprises.
Andy argues that the specialist hub approach has clear drawbacks from an architectural standpoint, especially in situations where companies are multi-national. There are many types of master data beyond customer and product, something beginning to be acknowledged by the industry. The whole idea of MDM is to improve the current messy situation where application silos compete over the ownership of master data. Yet a proliferation of separate specialist hubs may result in a new generation of silos.
In many cases it is impractical for enterprises to deploy a single hub or hubs at the enterprise level. What is needed is the ability for cross-domain hubs to be deployed in a managed federation, yet few vendors have even begun to address this issue.
Since 2006 there has been a sea change in vendor marketing, as companies who previously defended the specialist hub approach have set out roadmaps to integrate their separate technologies. Smaller independent MDM vendors have a window of opportunity to prosper by offering a cross-domain approach today, while the industry giants execute on their long-term integration roadmaps.
The white paper has 9 pages.
A paper by Cliff Longman published December 2009
The idea of “Six degrees of separation” is that any human being can be connected to every other human being through six relationships at most. Data is a little like this: through data relationships, all data is connected. When it comes to managing shared data (the primary focus of master data), you should plan to include all data types within your scope because, ultimately, it is all connected. However, placing all shared data under management is a significant task involving many parts of the business, changes to people’s ways of working, new processes and potentially risky technology implementations. Not an exercise that can be undertaken in a single step, it needs to be done in stages. At present many companies are conducting pilot MDM projects without giving sufficient thought to the endgame: how to control all their master data. Cliff Longman has identified four different “strata” of data: transactional data (e.g. a payment is made), dependent data (e.g. sales regions), independent data (e.g. customer, product) and policy and reference data (e.g. categories of products, currencies) which have different characteristics. There are then four alternative strategies which ultimately end up with all shared data under management through a system of data mastering.
Far from being just one approach to an MDM program, Longman believes there are these four main viable approaches with very different cost, risk, and benefit profiles. Each shares a common end-goal: that of a managed master for all data types across an organization, but MDM thought leaders should select a strategy appropriate for their organization to get there. The full white paper, which discusses these approaches in detail, is available for purchase below.
The white paper has 15 pages.
The recent financial crisis has had widespread impact on both individuals and businesses. It is against this background that a number of comments have appeared in the media urging businesses not to be distracted from their current IT plans but to focus even more on the areas that will bring growth. It seems entirely likely that consulting firms will be faced with delayed or cancelled projects and many software vendors will experience a slowdown in orders. But will most businesses realize that Master Data Management (MDM) and Data Quality (DQ) initiatives hold the key to delivering improved business information and therefore that it makes sense to pursue these initiatives even against the current economic background? In other words, are MDM and DQ immune from the crisis?
There is little hard information concerning the response of businesses to the impact of the financial crisis in this area. At The Information Difference we have conducted a survey, sponsored by Harte-Hanks Trillium Software, aimed at gaining greater insight into the views and plans of businesses regarding their current or planned MDM and DQ initiatives.
The objective of the survey was to explore the views of businesses on the impact of the financial crisis and “credit crunch” on their current and future plans for implementation of MDM and DQ.
92 respondents completed the survey from all around the world, the majority from North America (57%) and Europe (31%). Most of the respondents were from companies having annual revenues greater than US $ 1 billion and represented a wide spectrum of industries.
The key findings from the survey are summarized below.
These results taken together support the view that we are unlikely to see a major retrenchment in the implementation of DQ and MDM in the business community. We believe this is positive news for both vendors and end users/enterprises alike.
The report has 16 pages.
Big Data has generated much interest and attention in the media of late. Indeed, several authors have recently raised the question of whether Big Data approaches, such as Hadoop, will pronounce the death sentence on the conventional data warehouse.
In this survey we investigate the current state of the data warehouse and examine its recent challenger in the form of Big Data solutions as an alternative. Is the new technology really complementary or is the reign of the data warehouse nearing an end?
The report has 25 pages.