Hadoop has driven an enormous amount of data analytics activity lately. And this poses a problem for many practitioners coming from the traditional relational database management system (RDBMS) world. Hadoop is well known for having lots of variety in the structure of data it stores and processes. But it's fair to
In my last post, I talked about how data still needs to be cleaned up – and data strategy still needs to be re-evaluated – as we start to work with nontraditional databases and other new technologies. There are lots of ways to use these new platforms (like Hadoop). For example, many
If your enterprise is working with Hadoop, MongoDB or other nontraditional databases, then you need to evaluate your data strategy. A data strategy must adapt to current data trends based on business requirements. So am I still the clean-up woman? The answer is YES! I still work on the quality of the data.
The demand for data preparation solutions is at an all-time high, and it's primarily driven by the demand for self-service analytics. Ten years ago, if you were a business leader that wanted to get more in-depth information on a particular KPI, you would typically issue a reporting request to IT
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
At this stage, our organization has defined business objectives for Data Governance programme and shares business term definitions which it uses. This logical area of data and information management has been supplemented with a bridge to technical metadata – in the previous step we obtained one place, which combines the
Auditability and data quality are two of the most important demands on a data warehouse. Why? Because reliable data processes ensure the accuracy of your analytical applications and statistical reports. Using a standard data model enhances auditability and data quality of your data warehouse implementation for business analytics.
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
Das „Recht auf Vergessen“ hat nichts mit der Demenzerkrankung Alzheimer tun und betrifft auch nicht nur Personen im fortgeschrittenen Alter. Das „Recht auf Vergessen“ und „Portabilität“ bezeichnen Rechte im Rahmen der neuen europäischen Datenschutzverordnung, die nach vier Jahren Arbeit die nun doch schon 20 Jahren alte Verordnung ablöst, und die
The “big” part of big data is about enabling insights that were previously indiscernible. It's about uncovering small differences that make a big difference in domains as widespread as health care, public health, marketing and business process optimization, law enforcement and cybersecurity – and even the detection of new subatomic particles.
.@philsimon on whether organizations need MDM to gather valuable insights about their customers.
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
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
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
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.
Tutti siamo a conoscenza del fatto che avere acqua pulita è una condizione necessaria alla sopravvivenza. Senza, è possibile restare in vita per circa tre giorni. Quindi cosa succede quando la fonte è inquinata? A meno di non filtrare acqua con particolare attenzione, le conseguenze per l’organismo saranno sicuramente negative.
.@philsimon on the collision between the two.
I've spent much of my career managing the quality of data after it was moved from its sources to a central location, such as an enterprise data warehouse. Nowadays not only do we have a lot more data – but a lot of it is in motion. One of the
In my previous post I discussed the practice of putting data quality processes as close to data sources as possible. Historically this meant data quality happened during data integration in preparation for loading quality data into an enterprise data warehouse (EDW) or a master data management (MDM) hub. Nowadays, however, there’s a lot of
We had just completed a four-week data quality assessment of an inside plant installation. It wasn't looking good. There were huge gaps in the data, particularly when we cross-referenced systems together. In theory, each system was meant to hold identical information of the plant equipment. But when we consolidated the
In erster Linie wird der Begriff Datenqualität mit Kunden- und Adressinformationen in Zusammenhang gebracht. Neben der Dublettensuche und Bereinigung von Adressdatenbeständen ist die Qualität der Produktstammdaten aber ebenfalls äußert wichtig, um automatisierte Prozessabläufe zu verbessern oder beispielsweise die Trefferquote bei Suchanfragen im Onlineshop zu erhöhen.
In my last post we started to look at two different Internet of Things (IoT) paradigms. The first only involved streaming automatically generated data from machines (such as sensor data). The second combined human-generated and machine-generated data, such as social media updates that are automatically augmented with geo-tag data by
The concept of the internet of things (IoT) is used broadly to cover any organization of communication devices and methods, messages streaming from the device pool, data collected at a centralized point, and analysis used to exploit the combined data for business value. But this description hides the richness of
Anwender in Risiko- oder Controlling-Abteilungen haben – in aller Regel – keine tiefer gehenden Kenntnisse in Abfragen von Datenbanken. Excel ist die Welt, in der sie zu Hause sind und sich wohlfühlen. Komplexe Datenbankfragen, wenn etwa Zusammenhänge zwischen Datenbanktabellen identifiziert werden sollen, führt die IT-Abteilung durch und stellt die Ergebnisse
Was zeichnet ein erfolgreiches Unternehmen aus? Entscheidender Indikator für den Erfolg ist der Umsatz und der daraus resultierende Gewinn des laufenden Geschäftsjahres. Was auf der einen Seite hart erarbeitet wird, geht allerdings auf der anderen Seite oft leichtfertig verloren. So büßen viele Unternehmen laut Analystenstudien etwa acht Prozent ihres Gewinns
Throughout my long career of building and implementing data quality processes, I've consistently been told that data quality could not be implemented within data sources, because doing so would disrupt production systems. Therefore, source data was often copied to a central location – a staging area – where it was cleansed, transformed, unduplicated, restructured
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
While it’s obvious that chickens hatch from eggs that were laid by other chickens, what’s less obvious is which came first – the chicken or the egg? This classic conundrum has long puzzled non-scientists and scientists alike. There are almost as many people on Team Chicken as there are on Team
