Many people who plan data governance initiatives ignore the need for a business case. "We've already had approval for the project; why do we need a business case when we've got the budget signed off?" The perception is that because they have a strong commitment, there is no need to get
Operationalizing data governance means putting processes and tools in place for defining, enforcing and reporting on compliance with data quality and validation standards. There is a life cycle associated with a data policy, which is typically motivated by an externally mandated business policy or expectation, such as regulatory compliance.
.@philsimon on whether companies should apply some radical tactics to DG.
Yes. But since this post needs to be more than a one-word answer to its title, allow me to elaborate. Data governance (DG) enters into the discussion of all enterprise information initiatives. Whether or not DG should be the opening salvo of these discussions is akin to asking whether the
If your organization is large enough, it probably has multiple data-related initiatives going on at any given time. Perhaps a new data warehouse is planned, an ERP upgrade is imminent or a data quality project is underway. Whatever the initiative, it may raise questions around data governance – closely followed by discussions about the
In recent years, we practitioners in the data management world have been pretty quick to conflate “data governance” with “data quality” and “metadata.” Many tools marketed under "data governance" have emerged – yet when you inspect their capabilities, you see that in many ways these tools largely encompass data validation and data standardization. Unfortunately, we
.@philsimon on the new challenges of data governance.
Data governance and data virtualization can become powerful allies. The word governance is not be understood here as a law but more as a support and vision for business analytics application. Our governance processes must become agile the same way our business is transforming. Data virtualization, being a very versatile
Data governance and data virtualization can become powerful allies. The word governance is not be understood here as a law but more as a support and vision for business analytics application. Our governance processes must become agile the same way our business is transforming. Data virtualization, being a very versatile
I've been in many bands over the years- from rock to jazz to orchestra - and each brings with it a different maturity, skill level, attitude, and challenge. Rock is arguably the easiest (and the most fun!) to play, as it involves the least members, lowest skill level, a goodly amount of drama, and the
Adoption of Hadoop, a low-cost open source platform used for processing and storing massive amounts of data, has exploded by almost 60 percent in the last two years alone according to Gartner. One primary use case for Hadoop is as a data lake – a vast store of raw, minimally processed data. But, in many ways, because
In the movie Big, a 12-year-old boy, after being embarrassed in front of an older girl he was trying to impress by being told he was too short for a carnival ride, puts a coin into an antique arcade fortune teller machine called Zoltar Speaks, makes a wish to be big,
Data Management has been the foundational building block supporting major business analytics initiatives from day one. Not only is it highly relevant, it is absolutely critical to the success of all business analytics projects. Emerging big data platforms such as Hadoop and in-memory databases are disrupting traditional data architecture in
This isn't Kansas anymore. Oz has become a sprawling, smart metropolis filled with sensor data. How do we make sense of, clean, govern and glean value from this big data so we can get Dorothy home? The answer is SAS Data Management. With the latest portfolio updates, customers will be
The last three parts of our conversion blog (see all of the posts here) go hand-in-hand and require the most time on the project plan. Development - During development of the conversion routines, you may want to consider using error handling standards based on corporate standards. This is where data
In my previous post I explained that even if your organization does not have anyone with data steward as their official job title, data stewardship plays a crucial role in data governance and data quality. Let’s assume that this has inspired you to formally make data steward an official job title. How
Data. Our industry really loves that word, making it seem like the whole world revolves around it. We certainly enjoy revolving a lot of words around it. We put words like master, big, and meta before it, and words like management, quality, and governance after it. This spins out disciplines
Data has value IF you can analyze it, said participants at a big data analytics roundtable at the Premier Business Leadership Series in Las Vegas. In attendance were executives from some of the largest Communications companies in the world including from the US, Canada, Turkey, Japan, Australia and the Philippines as well
With big data, data governance challenges escalate in many ways: The diversity of data sources means that there are minimal standards for data structure, definition, semantics and content. The lack of control over data production means that you can’t enforce data quality at the source as you can do with
La gestión de datos es la práctica que permite a las empresas organizar y administrar los datos como un activo valioso e impulsar tanto los principales procesos operativos, como la toma de decisiones estratégicas al interior de las organizaciones. El gobierno de datos, entre tanto, hace referencia al esquema de
El gobierno de datos se ha convertido en un sello para todo lo que tiene que ver con los datos. De hecho, si busca el término en Google, encontrará referencias a la calidad de los datos, los metadatos, el almacenamiento de datos, la propiedad de los datos o la seguridad
Sometimes you have to get small to win big. SAS Data Management breaks solution capabilities into smaller chunks – and deploys services as needed – to help customers reduce their total cost of ownership. SAS Master Data Management (MDM) is also a pioneer in "phased MDM." It's built on top of a data
In a previous blog, I wrote about the top ten fallacies of why data governance is perceived to be too burdensome and costly. Hopefully I dispelled the preconception that data governance is slower and less nimble than today's informal data management practices. In this post, we'll examine the concept of governance
One of the biggest impediments to (and failures of) a new data governance program is the perceived level of “extras” required. Let’s enumerate some of the concerns that I hear consistently from our clients: Extra people will be required to staff the implementation. Extra budget money will be needed to fund the
Interest in "data" is at an all-time high. The popularity of search terms like "big data," "Hadoop" and the "Internet of Things" spiked dramatically in the past year. The fact is, organizations are more interested in the potential of big data platforms and data management solutions than ever before. That’s
Lately, there's been lots of buzz around the logical data warehouse (LDW). In fact, Gartner is hearing LDW mentions as part of data warehouse (DW) inquiries almost 20% of the time and considers it a "megatrend." The definition usually includes some use of data virtualization or data federation capabilities to complement
One of the benefits of running an online data quality and data governance community is that over the course of many interviews, you start to see common threads and patterns emerging in the way practitioners create success in their data-driven programs. Data governance is a relatively new discipline, so it’s
In the past few weeks I have presented training sessions on data governance, master data management, data quality and analytics at three different venues. At each one of these events, during one of the breaks a variety of people in my course noted that the technical concepts of implementing programs
In my previous post, I outlined the main components needed for a phased approach to MDM. Now, let's talk about some of the other issues around approaching MDM: data governance and the move to enterprise MDM. Where does governance come in? Throughout your MDM program, it's important that deep expertise
In my last post I introduced the term “behavior architecture,” and this time I would like to explore what that concept means. One approach is to start with the basics: given a business process with a set of decision points and a number of participants, the behavior architecture is the
