Blend, cleanse and prepare data for analytics, reporting or data modernization efforts
Absent a strong executive presence, most mature organizations will continue to muddle through data integration.
How many companies are using Hadoop as part of their master data management initiative? Come on, raise your hands! Well, maybe a better question is this: How many companies are using Hadoop for enterprise data? From what I have seen, Hadoop is coming along quite nicely. However, it may not be the
At this stage our organization has already defined the business objectives for Data Governance programme (step 0) and started to manage business terms, as described in step 1. Step 2: tracking data flow in organization Data Governance is not only related to understanding the data – it also focuses on
Data monetisation is a hot topic these days. Especially for people like me watching the movements of early adopters – companies who are using data to create new revenue streams or even create new businesses to capture those revenue streams. DataStreamX is a notable start-up whose sole business is cashing
At some point, your business or IT leaders will decide – enough is enough; we can't live with the performance, functionality or cost of the current application landscape. Perhaps your financial services employer wants to offer mobile services, but building modern apps via the old mainframe architecture is impractical and a replacement
More and more organizations are considering the use of maturing scalable computing environments like Hadoop as part of their enterprise data management, processing and analytics infrastructure. But there's a significant difference between the evaluation phase of technology adoption and its subsequent production phase. This seems apparent in terms of how organizations are
What's more, CXOs who believe that they can substitute data scientists for real data integration are as foolish as the duffer who consistently uses the wrong club.
Having introduced the term of Data Governance and defined business objectives, we can start to fulfill the first tasks within Data Governance programme construction. Step 1: business meaning of data While conducting their activity, organizations use many industry-specific terms. The mere definition of who a customer is for the company
Fellow Roundtable writer David Loshin has commented in the past that: "MDM is popular because it is presented as a cure-all solution to all data problems in the organization." Many people see master data management (MDM) as the silver bullet to all of their business and data woes. But in
.@philsimon on whether organizations need MDM to gather valuable insights about their customers.
Collecting, managing, standardizing and analyzing clinical data during (and after) a clinical trial is crucial in the process for submission and regulatory approval of a new compound, biological, device or other therapy. A central clinical platform requires: Robust and auditable analytics to prove the result to the authorities and external
Master data management (MDM) is distinct from other data management disciplines due to its primary focus on giving the enterprise a single view of the master data that represents key business entities, such as parties, products, locations and assets. MDM achieves this by standardizing, matching and consolidating common data elements across traditional and big
“Good afternoon, Mr. Yakamoto. How did you like that three-pack of tank tops you bought last time you were in?” Washington D.C. Year 2054. Chief of PreCrime John Anderton is running from the law for a crime he has not committed yet. After a risky eye transplant in order to
SAS is widely used in clinical research activities including: Managing and transforming data. Generating tabular and graphical summaries. Performing powerful statistical analyses such as safety and efficacy evaluations. In addition, SAS provides a number of interfaces from which a user can select to work with the data. One of these
.@philsimon on what's next for MDM applications.
Matchcodes spielen bei der Identifizierung von Dubletten eine zentrale Rolle. Um die Dubletten anhand von Matchcodes zu finden, müssen die Daten meistens erst noch aufbereitet werden. Stehen beispielsweise Anrede und Vor-/Nachname oder Straße und Hausnummer im selben Feld, müssen diese separiert werden, dadurch können bessere Match-Ergebnisse erzielt werden.
Una necesidad primordial para todas las organizaciones es conocer y entender a sus clientes a lo largo de su ciclo de vida. Conocer los gustos, necesidades y hábitos de compra del cliente permite generar estrategias analíticas enfocadas en incrementar el valor hacia ellos y el que éstos representan para la
Master data management (MDM) is distinct from other data management disciplines due to its primary focus on giving the enterprise a single view of the master data that represents key business entities, such as parties, products, locations and assets. MDM achieves this by standardizing, matching and consolidating common data elements across traditional and big
21st century is the era of information revolution, when mass exploitation of data is a part of daily routine. Nowadays any business requires that the information driving the decision-making process be of a high quality, timely delivered and, above all, reliable and that it ensure security of the activity conducted.
The UK’s National Health Service (NHS) Confederation has done lots of great research on how to enhance decision making so that every decision delivers greater value to patients (in terms of clinical outcome) and to health care organisations (in terms of operational effectiveness). In its most recent report on this
Single view of customer. It's a noble goal, not unlike the search for the Holy Grail – fraught with peril as you progress down the path of your data journey. If you're a hotelier, it can improve your customer's experience by providing the information from the casinos and the spa at check-in to better meet your
In DataFlux Data Management Studio, the data quality nodes (e.g., Parsing, Standardization, and Match Codes) in a data job use definitions from the SAS Quality Knowledge Base (QKB). These definitions are based on a locale (Language and Country combination). Sometimes you would like to work with multi-locale data within the
Most companies are battling with master data challenges whether they realise it or not. When you're consolidating financials from multiple billing systems, you're doing MDM. When you're migrating legacy systems to a new target environment, you're doing MDM. When you're trying to perform root-cause analysis across multiple systems for a
Na tym etapie nasza organizacja posiada określone dla programu Data Governance cele biznesowe oraz zarządza i współdzieli definicje pojęć biznesowych, którymi się posługuje. Ten logiczny obszar zarządzania danymi i informacją uzupełniony został o pomost do metadanych technicznych - w poprzednim kroku uzyskaliśmy jedno miejsce łączące informacje o technicznym przepływie danych w organizacji
Financial institutions evaluating fraud management solutions face a crowded vendor landscape. Dozens of vendors claim to offer various pieces of the puzzle. With so many choices available, how will you sort through the marketing rhetoric to find the best fit for your organization? You could assemble a team of analysts
La grandeza de sus datos probablemente no es la característica más importante. De hecho, puede que ni siquiera figure dentro de los aspectos relevantes por los cuales usted debería preocuparse. La calidad, la integración de los silos, la manipulación y la extracción de valor de los datos no estructurados siguen
.@philsimon on the role of MDM. TLDR: It depends.
Na tym etapie nasza organizacja posiada już określone cele biznesowe dla programu Data Governance (krok 0) oraz rozpoczęła zarządzanie pojęciami biznesowymi, które opisywał krok 1. Krok 2: śledzenie przepływu danych w organizacji Data Governance to nie tylko rozumienie znaczenia danych - to także świadomość, w jaki sposób te dane i
In my last post, I discussed the issue of temporal inconsistency for master data, when the records in the master repository are inconsistent with the source systems as a result of a time-based absence of synchronization. Periodic master data updates that pull data from systems without considering alignment with in-process
The numbers are daunting. More than 40 million Americans have their identities stolen each year. Credit card companies lose more than $200 billion annually due to fraud. Cybercrime-related losses exceed $3 million per claim for large companies. If you’re like me, those stats are enough to give pause. To fuel the concern,
