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
Mit fortschreitender Digitalisierung entdecken immer mehr Versicherer neue Möglichkeiten der Tarifgestaltung. Hierzu zählen aktuell sogenannte Telematik-Tarife, die das individuelle Fahrverhalten der Versicherten berücksichtigt. Zur Bewertung des Fahrverhaltens werden mittels einer Blackbox Daten wie z. B. gefahrene Kilometer, Beschleunigung und Bremsverhalten gesammelt. Aus diesen unterschiedlichen Kennzahlen wird mittels analytischer Verfahren ein fahrerspezifischer
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.
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
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
Kunde: Ah, Sie zeigen uns in dieser Demo so ein Glossar. Ach, jaja, nett, das haben wir schon! Ich: Oh, und benutzen Sie persönlich das in Ihrem Unternehmen oft? Kunde: Nee, das ist veraltet. Da schaut keiner rein, weil das pflegt ja keiner richtig… Oder doch? Hm.
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
.@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
Po wprowadzeniu w zagadnienia Data Governance oraz określeniu celów biznesowych możemy przystąpić do realizacji pierwszych zadań w budowie programu Data Governance. Krok 1: biznesowe znaczenie danych Organizacje w prowadzeniu swojej działalności posługują się wieloma, specyficznymi np. dla branży, pojęciami. Samo zdefiniowanie kto dla firmy jest klientem jest niejednoznaczne w zależności
XXI wiek to rewolucja informacyjna, gdzie wykorzystywanie danych na masową skalę jest codziennością. Każda działalność biznesowa wymaga dzisiaj, aby informacje wspierające podejmowanie decyzji były wysokiej jakości, dostarczane na czas, i co najważniejsze, były pewne i gwarantowały bezpieczeństwo prowadzonej działalności biznesowej. Rozwój ekosystemów analitycznych, oferowanych przez firmy innowacyjnych produktów i usług
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?
Erst kürzlich habe ich mit einem CIO über die Zukunft seiner IT-Infrastruktur gesprochen. In einem erstaunlich offenen Gespräch war fast schon Verzweiflung zu hören: Jedes Jahr kämen neue Trends, alle seien megawichtig, und dazu werde neuerdings alles im Big-Data-Umfeld mit CEO-Blick durchleuchtet.
While the growth of big data is an issue that preoccupies both the public and private sector, it’s also high on the UK government’s legislative agenda. All eyes are currently on the authorities, as we wait with bated breath to see what’s next in the quest to manage data escalation.
Das ist sicher: Ostern werden die Meta-Eier versteckt – und meist auch gefunden. Das Finden dauert vielleicht doppelt so lange wie das Verstecken: Anhand intuitiv entwickelter Metadaten im Kopf begeben sich die Kleinen auf eine mühsame Eierrecherche in Omas Garten – mit wenig Struktur und viel Glück.
When you spend long enough writing and working in any industry, you inevitably see trends emerge and reach varying levels of maturity. Data governance is one such trend, as you can see from the following Google Trends chart:
Aktuell sprechen wir mit vielen Banken über das Management ihrer Daten. Historisch schien die Bereitstellung von Information hinlänglich gelöst: Die IT-Abteilungen stellten diverse Mart-Daten & Analyse-Tools bereit. Punkt. Banken-Daten-Management: Mehr Schein als Sein?
Creating a strategy for the data in an organization is not a straightforward task. Not only does our business change – our software solutions also change before we can ever get done with a data strategy. So, I choose to understand that a strategy has a vision, and my vision may change
In my previous post, I discussed the characteristics of a strong data strategy, the first of which was that a formal, well-defined strategy exists within your organization. This post discusses how often (and why) your organization’s data strategy needs to be updated. While strategy encompasses and sets the overall direction for
With data now impacting nearly every business activity, there should no longer be any doubt that data needs to be managed as a strategic corporate asset. This post examines the top five characteristics of a strong data strategy. Existence As I previously blogged, in today’s fast-moving business world now often takes priority
The other day I was in a meeting with a client and there was an argument about who owns the data. Those arguing were IT people. In this scenario, the assumption was that data from source systems would flow into and integrate with a data warehouse. I found the discussion very interesting. Here are some of the
It’s obvious that an enterprise data strategy involves data – but we sometimes disregard the fact that it should also involve metadata. Why? Because it’s key to unlocking the value of data. Metadata shows you what data is available and how people can use it. It also reveals which data
Superhelden gibt es viele, wie Batman, Robin, Spiderman, oder der Flash und fast alle haben das gleiche Problem – sie wollen nicht erkannt werden. Heute geht es um Superhelden und ihr Maskierungsproblem
In this two-part series, which posts as the calendar turns to a new year, I revisit the top data management topics of 2015 (Part 1) and then try to predict a few of the data management trends of 2016 (Part 2). Data management in 2016 The Internet of Things (IoT) made significant
In this two-part series, which posts as the calendar prepares to turn 2015 into 2016, I revisit the top data management topics of 2015 (Part 1) and then try to predict a few of the data management trends of 2016 (Part 2). Data management in 2015 Big data continued to make
In my recent posts, I've been exploring the issues of integrating data that originates from beyond the organization. But this post looks at a different facet of extra-enterprise data management: data availability. In many organizations, there's a growing trend of making internal analytical data accessible to external consumers. I can
Jeff Stander passes along some of the lessons he's learned about third-party metadata collection.
Most people have logged on to a social media site, maybe to look up an old friend, acquaintance or family member. Some people play games, or post funny pictures or other information they want to share with everyone. Do you ever ask yourself what happens with this information? What if your business wanted to purchase this information and
In 2014, big data was on everyone’s mind. So in 2015, I expected to see data quality initiatives make a major shift toward big data. But I was surprised by a completely new requirement for data quality, which proves that the world is not all about big data – not
