June 8, 2017
by Eric Carrell, Cloudwards.net
Planning for the future with data management in mind means that you’ll have to deal with the realities of the Internet of Things sooner rather than later. The IoT is likely going to be the source of the very biggest in Big Data and keeping track of it all is going to be a major challenge in the years ahead.
If you want to know more about the IoT, the editorial team over at Cloudwards.net has put together an article about what the Internet of Things is. The article discusses what’s possible with the IoT, how it works, as well as its security and privacy implications. We recommend interested people give it a read and think about all the changes in data governance ahead of us.
What is the Internet of Things? by Fergus O’Sullivan, Cloudwards.net
August 16, 2016
#10 Data is like money. If you invest it, manage it, and protect it well, it can pay off immensely. But do any of these poorly, and you’ll regret it.
#9 Development methodologies keep changing. …Mostly in name.
#8 The only thing more expensive than free software is free software implemented by the lowest bidder.
#7 Master Data Management is a transitional state until you get to the fully integrated environment… And once you’re there, you’ll need to add another system.
#6 Big data is incredibly valuable unless someone forgot to govern it.
#5 Agile is great, but knowing your real requirements is better.
#4 If data governance is painful, too slow, or too costly, it’s being done wrong.
#3 Choosing the lowest cost integrator is like choosing the cheapest plumber… Once they’re done, it looks great!! ...
August 3, 2016
There is a lot of truth and wisdom in the quote, “The perfect is the enemy of the good.”
The quote definitely applies when trying to get the most out of data. However, in this context, it should really be “Perfect is the enemy of budget and time.”
Pragmatic data governance protects your budget and timelines against perfectionism. The end goal is, of course, to have all issues resolved, but being able to start governing the data before the end is vital. To do this, data governance has to be built around the concept of managing “imperfect” data and incrementally improving that data.
A key failure of data governance in almost every organization is the wanting to come to agreement before making data available. A far more practical and useful ...
March 11, 2016
What Makes Data Management Successful and Sustainable
You may find that the higher up in the organization you go, the greater the support you’ll receive for data governance in relation to big data and big data success, but most of the work to manage data is not done at the top of the organization.
Success or failure will actually be determined by how willing the people dealing with the data on a daily basis are to adopt and be involved in the data management. The better defined the value proposition and implementation process are, the greater the level of success your organization will enjoy.Data management and governance is an evolutionary process, so there is no final process or last step. To continue to maintain the standards that you have ...
March 10, 2016
How “Pragmatic” Data Management Works
Whether your big data management plan for success contains two steps or twenty steps, the initial point for most successful companies is data governance. Typically, the focus of data governance tends to be on only inbound data feeds with little regard for the metadata, but if attention is not paid to determining the true purpose of the data, to applying and following business rules, and to managing change, downstream applications will most likely continue to receive lousy data.
Data governance for big data can become complicated, so maintaining practicality is critical. Without a pragmatic governance approach, the value of data is easily undermined. Attaining a high degree of data accuracy is not always necessary because the art of pragmatic data governance is to ...
February 23, 2016
Why Data Management Is Critical to Big Data Success
According to recent TDWI research, 89% of organizations still consider big data to be more of an opportunity. Only 11% of organizations view big data to be a problem because few of these “opportunities” yield outcomes that generate revenues higher than the cost of getting the “insights” from big data. Unfortunately, what many organizations don’t realize is that big data equates to big problems for organizations without an effective data management plan in place to handle it.
Many organizations still struggle with “small” data, so for them, more data does not necessarily mean better. To make data management even more off-putting, organizations that do recognize big data as an adversary characterize it as something new: “Big Data Management” (BDM)—yet another ...
January 28, 2016
How to Choose the Right Data Movement: Real-time or Batch?
We all want a “zero wait” infrastructure. This has spurred many organizations to push all data through a real-time infrastructure. It’s important to recognize that “zero wait” means that the information is in ready form when a user needs it, so if the user needs information that includes averages, sums, and/or comparisons, there is a natural need to have a data set that has been fully processed (e.g., cleaned, combined, augmented, etc.). Building the data infrastructure with this in mind is very important.
The popular point of view is that real-time processing is the “modern” solution and that batch processing is the “archaic” way. However, real-time processing has also been around for a long time, and each mode ...
January 5, 2016
Effective and efficient information sharing creates business value. That means collaboration is key to your BI approach and deployment. Collaboration throughout your entire business ecosystem allows everyone to bring their individual expertise to the table but still work collectively to solve the same business problem efficiently. So human intelligence becomes collective intelligence that we can use to make better business decisions. This begins to empower organic intelligence, which means that the workers within your business ecosystem start to address problems before they actually become problems.
Collective intelligence is much more valuable to a company than any individual person’s intelligence.
That’s because very few decisions are made independently within a company. The bigger the decision, the more people involved. Even though we’ve eliminated silos of data with servers and software, most ...
June 16, 2014
By Robert Springer
Takeaway: Yup, the robots really are taking over. But while it’s easy to make light of interconnected kitchen appliances, the IoT also has the potential to do some real good.
“The machines rose from the ashes of the nuclear fire. Their war to exterminate mankind had raged for decades, but the final battle would not be fought in the future. It would be fought here, in our present. Tonight…”
These lines, scrolling on the screen during the beginning of the classic science fiction film “The Terminator,” inform us that the machines (computers and the cybernetic organisms they created) have vanquished humanity. The slaves are now the masters.
It’s hard not to think of machines-versus-man movies like “The Terminator” and “The Matrix” when you hear the term the
April 25, 2014
Relational database management systems (RDBMSs) have had a dominant role in data warehousing for the last 20+ years. Many software architectures, modelling techniques, organizations’ hardware and software investments, RDBMS and Business Intelligence (BI) vendors’ wellbeing and peoples’ skills and careers have been based on that fact. Now things are changing. The world of Data Warehousing and Analytics is getting more complex – due to the demands of Big Data and the emergence of new technologies, such as the Hadoop framework. However, it is important to say that these new technologies should be seen as complementary to existing and future RDBMS investments.
In his Harvard Business Review article “Preparing for Analytics 3.0” Tom Davenport talks about Analytics 3.0. He characterizes traditional BI and reporting applications (on RDBMSs) as Analytics 1.0, the analysis of large, fast moving, external, and unstructured data from new data sources (on Hadoop and NoSQL) as Analytics ...