The 10 columns that were reader favorites for 2018
Data science skills, blockchain, master data management and the General Data Protection Regulation were topics that most captured reader attention in 2018.
1. 4 steps to conducting a GDPR compliance audit
Many organizations are starting to feel stressed and perhaps a bit confused by the General Data Protection Regulation that is quickly approaching us and will hit May 25, 2018. Indeed, the more I talk to companies, the more I hear the same question: “What exactly is it that I am supposed to do?” Read the full column here.
2. 10 best practices for master data management
Master data management is all about solving business issues and improving data trustworthiness through the effective and seamless integration of information with business processes. Unfortunately, a common mistake that some organizations make is to treat MDM as a technical issue. While this approach helps an organization quick start its MDM initiative, it leaves most critical problems unattended, and dilutes the overall benefits of the MDM program. Read the full column here.
The top skills needed by data scientists in 2019
These days, it seems like everyone wants to be a data scientist. In fact, more and more universities are attracting and producing data science graduates— demand for data analytics masters programs grew by over 70 percent last year, while traditional MBAs only by about 30 percent. So why are organizations finding hiring data scientists remains a challenge? Read the full column here.
4. If you thought GDPR was bad – just wait for ePrivacy Regulation
While the Facebook-Cambridge Analytica situation catapulted the GDPR into national prominence and jolted many non-EU companies into action vis-a-vie data protections, there is an even more concerning regulation - some say nightmare - waiting in the wings; ePrivacy Regulation or ePR for short.
EPR is essentially a complement to the GDPR (in force since May 25) covering all electronic communications and data. It is intended to provide a single digital data privacy framework under which all companies doing business with EU residents must conform, and the penalties are similar to those enforced under the GDPR. Read the full column here.
5. 6 data modeling best practices for better business intelligence
It’s crucial to understand data modeling when working with big data to solidify important business decisions. Although specific circumstances vary with each attempt, there are best practices to follow that should improve outcomes and save time. Here are six of them. Read the full column here.
GDPR will be a harsh wake-up call for most U.S. companies
While European businesses brace themselves for the May 2018 deadline to comply with the General Data Protection Regulation, recent surveys have shown that just 25 percent of U.S. companies believe the regulation applies to them. That misconception could end up costing them up to four percent of global revenues or €20 million (approximately $24.5 million), whichever is greater. Read the full column here.
The role of blockchain in helping organizations meet GDPR compliance
While blockchain and the General Data Protection Regulation are currently two of the data management industry’s hottest buzzwords, they have more than just buzz in common as the industry continues to ponder their respective impact. They share the same level of excitement as well as the same level of scepticism. Interestingly, they may be linked in another way – the fact that blockchain could play an important role in helping organizations comply with GDPR rules. Read the full column here.
8. Understanding the difference between a data dictionary and a data glossary
A lot of people get confused about what a data glossary is and how it is different from a data dictionary. IT people are generally happy that they understand what a data dictionary is and in my experience some business people also understand what one is (and on the rare occasion may even want to refer to one). But there is often a lack of clarity over what a data glossary is. Read the full column here.
9. The role of the data curator: Make data scientists more productive
The ability to harness data to solve critical business challenges is an essential skill for every organization today. There are two primary roles responsible for this function—data scientists and data analysts. Unfortunately these people spend the majority of their time performing tasks that are not core to their high value responsibilities, such as finding data, preparing data, and optimizing data for their analysis. Read the full column here.
10. Graph databases and machine learning will revolutionize MDM strategies
Every organization today is in the “data business,” as they continue to collect volumes of information about their customers. When organized effectively – by integrating data from variety of different sources through master data management – this data can provide important and actionable insights.
Some enterprises are tackling this challenge with a traditional approach to MDM, which limits what can be done and how quickly they can do it. But coming soon to an MDM hub near you are two key enabling technologies that both augment and/or re-invent MDM as we know it ... graph database and machine learning (ML). Read the full column here.