To get full value from analytics programs, Todd Wright says be sure you can first access, integrate, cleanse and govern your data.
Tag: data quality
Jim Harris says more reusable data quality processes mean less reliance on IT and higher productivity across the board.
You have to be able to trust the data that you are working with, whether it’s data processing or analysis that you are involved with. And there is a strong correlation between that trust and data quality. Is it possible to determine data quality without monitoring mechanisms?
Die Geburtenrate in Deutschland befindet sich derzeit auf dem höchsten Niveau seit 33 Jahren. Eine erfreuliche Entwicklung, und zugleich stellt es Eltern vor die schwere Entscheidung, welchen Namen der Nachwuchs tragen soll. Zahlreiche Webseiten und Bücher bieten Hitlisten und Namensbeschreibungen an, um die Auswahl zu erleichtern. Oder sollte man das
Get on with your day faster by taking a self-service approach to data preparation.
Many of the regulations coming into effect after 2010, are the result of the financial crisis that has significantly re-shaped the financial industry worldwide and especially in Europe. One of the major projects that has been undertaken by the statistics team of the ECB, launched in 2011, is the setup
Matthew Magne describes how SAS Data Quality can help you build a trusted data foundation, one stone at a time.
Lenin und ich sitzen im Publikum und applaudieren heftig: Seine Chefin hat ihren Vortrag beendet über „Datenqualität als Erfolgsfaktor im Internet of Things“. „Kein Datenqualitätsprojekt ohne Hilfe von oben“, raunt Lenin mir zu, "Unterstützung vom Boss ist manchmal wichtiger als tolle Software." Ich will beleidigt darauf hinweisen, dass seine Chefin
In a recent presentation, Jill Dyche, VP of SAS Best Practices gave two great quotes: "Map strategy to data" and "strategy drives analytics drives data." In other words, don't wait for your data to be perfect before you invest in analytics. Don't get me wrong -- I fully understand and
The rise of self-service analytics, and the idea of the ‘citizen data scientist’, has also brought a number of issues to the fore in organizations. In particular, two common areas of discussion are the twin pillars of data quality and data preparation. There is no doubt that good quality, well-prepared
David Loshin extends his exploration of ethical issues surrounding automated systems and event stream processing to encompass data quality and risk considerations.
Lenin hatte gelächelt und von seinen Erfolgen im Internet of Things berichtet; richtig begeistert war er gewesen. – Aber jetzt murrt er: „Das ist alles Müll! Internet of Trash sollte es heißen! Die Daten stimmen nicht, die Leute schimpfen über das Projekt, der Fachbereich und meine Chefin sitzen mir im
In my last post I described "4 adaptability attributes for analytical success," and in the past I've discussed the strategic role analytics play in helping organizations succeed now and into the future. Now I'd like to discuss three attributes that define a powerful analytics environment: Speed Accuracy Scalability [NOTE: Any
Streaming technologies have been around for years, but as Felix Liao recently blogged, the numbers and types of use cases that can take advantage of these technologies have now increased exponentially. I've blogged about why streaming is the most effective way to handle the volume, variety and velocity of big data. That's
What are the most useful skills a data quality leader can possess? As an editor of an online data quality magazine, I naturally get asked this type of question regularly at events and meetups. My answer may surprise some who are expecting a data-centric response. I firmly believe that sales and
"Two weeks to go," Santa said to himself, with millions of toys stacked up on the shelves. Each year worry hit at the same time – "How do I get the right toy to the right child without losing my mind?" Though Old St. Nick didn't have a computer science degree, deep down
During a data quality assessment, one of my clients discovered that a large chunk of data that ultimately fed into their business analytics engine was sourced externally. After examining the contracts surrounding this data, I found that 100% of it failed to possess service-level agreements (SLAs) for the quality of
Historically, before data was managed it was moved to a central location. For a long time that central location was the staging area for an enterprise data warehouse (EDW). While EDWs and their staging areas are still in use – especially for structured, transactional and internally generated data – big
Data quality initiatives challenge organizations because the discipline encompasses so many issues, approaches and tools. Across the board, there are four main activity areas – or pillars – that underlie any successful data quality initiative. Let’s look at what each pillar means, then consider the benefits SAS Data Management brings
Digitalisierung, Big Data, IoT, Smart Data – die Liste an Ansätzen, die den Klassiker „aus Daten mach Umsatz“ neu definieren wollen, wird länger und länger. An cleveren, schlüssigen und vielversprechenden Methoden mangelt es sicher nicht, ihnen allen gemein ist aber der mahnende Zeigefinger und ewige Spielverderber Datenqualität. Und wie das
Our world is now so awash in data that many organizations have an embarrassment of riches when it comes to available data to support operational, tactical and strategic activities of the enterprise. Such a data-rich environment is highly susceptible to poor-quality data. This is especially true when swimming in data lakes –
We often talk about full customer data visibility and the need for a “golden record” that provides a 360-degree view of the customer to enhance our customer-facing processes. The rationale is that by accumulating all the data about a customer (or, for that matter, any entity of interest) from multiple sources, you
Most enterprises employ multiple analytical models in their business intelligence applications and decision-making processes. These analytical models include descriptive analytics that help the organization understand what has happened and what is happening now, predictive analytics that determine the probability of what will happen next, and prescriptive analytics that focus on
In my prior posts about operational data governance, I've suggested the need to embed data validation as an integral component of any data integration application. In my last post, we looked at an example of using a data quality audit report to ensure fidelity of the data integration processes for
Data governance plays an integral role in many enterprise information initiatives, such as data quality, master data management and analytics. It requires coordinating a complex combination of factors, including executive sponsorship, funding, decision rights, arbitration of conflicting priorities, policy definition, policy implementation, data stewardship and change management. With so much overhead involved in