We all have challenges in getting an accurate and consistent view of our customers across multiple applications or sources of customer information. Suggestion-based matching is a technique found in SAS Data Quality to improve matching results for data that has arbitrary typos and incorrect spellings in it. The suggestion-based concept
Electronic health records (EHRs) and the overall advancement of information technology have produced a tsunami of data that must be stored, managed and used. Some had naively hoped that EHRs would bring a simpler, more streamlined industry. Instead, we’re finding that the delivery and management of health care is more
With DataFlux Data Management 2.7, the major component of SAS Data Quality and other SAS Data Management solutions, every job has a REST API automatically created once moved to the Data Management Server. This is a great feature and enables us to easily call Data Management jobs from programming languages
Analytics provides better insights into why something happened, or helps provide decision makers with information about what will happen in the future. That allows organizations to act now to improve outcomes instead of reacting to events after they happen. But it takes more than analytics alone. Achieving this level of
In a previous blog post, I demonstrated combining the power of SAS Event Stream Processing (ESP) and the SAS Quality Knowledge Base (QKB), a key component of our SAS Data Quality offerings. In this post, I will expand on the topic and show how you can work with data from
Start with the end in mind -- wise words that apply to everything, and in the world of big data it means we have to change the way we look at managing the data we have. There was a time when we managed data quality, and the main goal was
What if you could predict with near-perfect accuracy what you’re going to sell and when your customer is going to buy? Right supply, right time is the goal German manufacturers have set themselves, without reducing the configuration options customers expect. Having almost completed stage 1 of their plan – changing
Have you ever had problems matching data that has typographical errors in it? Because of the nature of arbitrary typos and incorrect spelled words a specific matching technique is required to tackle those cases. SAS Data Quality, with its traditional, in nature deterministic matching approach is by nature not best
Some organizations I visit don’t seem to have changed their analytics technology environment much since the early days of IT. I often encounter companies with 70s-era base statistical packages running on mainframes or large servers, data warehouses (originated in the 80s), and lots of reporting applications. These tools usually continue
Two years ago, I found myself the proud, first-time owner of a garage. My wife and I quickly started to add new items to the garage – a battery-powered lawn mower, two beach cruisers and four Tommy Bahama beach chairs. They were stored with ease. What a fantastic world I'd been missing out on. But it wasn't long before we outstripped our
"Tap into all your demand signals. Organize. Visualize. Analyze. Predict. Orchestrate. Optimize." The availability and collection of data are compelling companies to invest in demand signal management solutions to take advantage of the vast amount of information to support their planning processes. However, many have not gotten the return on
The digital disruption is creating unforeseen events, such as new competitors, products and services that threaten the performance and positioning of consolidated players. Big data and analytics prove themselves, through successful user cases, as the answer to intercept the demand, prevent churn, draw an integrated view of the customer, manage
Some weeks have passed since the United Kingdom voted, by a margin of 52 per cent for and 48 per cent against, to leave the European Union, the organization it's been a leading member of since 1973. The tumultuous global reaction to the vote has those of us in information
SAS Business Data Network is SAS’ solution for managing glossaries of common business terms. This is part of the SAS Data Governance offering as well as bundled with Advanced versions of all SAS Data Management bundles. One thing that is important regarding Data Governance in general, and this solution in
In DataFlux Data Management Studio, the predominate component of the SAS Data Quality bundle, the data quality nodes in a data job use definitions from something called the SAS Quality Knowledge Base (QKB). The QKB supports over 25 languages and provides a set of pre-built rules, definitions and reference data
Modernization is a term used to describe the necessary evolution of information technologies that organizations rely on to remain competitive in today’s constantly changing business world. New technologies – many designed to better leverage big data – challenge existing data infrastructures and business models. This forces enterprises to modernize their approach to data
We all find change easier when it starts with something we’re familiar with. That’s why I think sports analytics examples are popular – most of us are sports fans, so we get it more easily. It’s also why automotive examples that illustrate the potential reach of the Internet of Things
In my last post, I started to look at the use of Hadoop in general and the data lake concept in particular as part of a plan for modernizing the data environment. There are surely benefits to the data lake, especially when it's deployed using a low-cost, scalable hardware platform.
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
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
Ciao a tutti e ben sintonizzati sulle frequenze di Radio SAS Technical Support! Oggi trasmettiamo la prima parte della super-classifica delle domande più frequenti che riceviamo dai SAS Administrators che operano presso le aziende nostre clienti. Il SAS Administrator è la super-star della Architettura e della Piattaforma SAS, le sue attività
Using a standardized data model is an essential condition to achieve data governance in an enterprise. A standard data model supports data governance processes by implementing industry standards wherever possible: standards for contract and claims representation, mapping of data content with standard definitions (glossary function), use of code attributes
Trusted data is key to driving accurate reporting and analysis, and ultimately, making the right decision. SAS Data Quality and SAS Data Management are two offerings that help create a trusted, blended view of your data. Both contain DataFlux Data Management Studio, a key component in profiling, enriching monitoring, governing
Todas las organizaciones están buscando una ventaja competitiva para sobresalir entre la competencia, mejorar los procesos internos y adaptarse a las necesidades actuales de los clientes. Uno de los pasos primordiales de cualquier iniciativa de negocio es tener una gestión de datos adecuada, que permita asegurar una toma de decisiones
These: Zum Daten-MAN-gen braucht man Software. Hat man mehr Daten, nimmt man mehr Software… „Mehr vom selben!“ – jener Kulturtechnik aus der Ecke „viel hilft viel“. Stimmt das so? Man kann dies‘ knifflige Thema von verschiedenen Seiten beleuchten – im Folgenden einige mehr oder minder ernste Ansätze dazu:
In my first blog article I explained that many insurance companies have implemented a standard data model as base for their business analytics data warehouse (DWH) solutions. But why should a standard data model be more appropriate than an individual one designed especially for a certain insurance company?
A soccer fairy tale Imagine it's Soccer Saturday. You've got 10 kids and 10 loads of laundry – along with buried soccer jerseys – that you need to clean before the games begin. Oh, and you have two hours to do this. Fear not! You are a member of an advanced HOA
As I explained in Part 1 of this series, spelling my name wrong does bother me! However, life changes quickly at health insurance, healthcare and pharmaceutical companies. That said, taking unintegrated or cleansed data and propagating it to Hadoop may only help one issue. That would be the issue of getting the data
Does it upset you when you log onto your healthcare insurance portal and find that they spelled your name wrong, have your dependents listed incorrectly or your address is not correct? Well, it's definitely not a warm fuzzy feeling for me! After working for many years in the healthcare, pharmaceutical and
