Everything is not a hammer: How new tools can make sense out of streaming data
@philsimon says that old stalwarts sometimes just don't cut it.
Author
Phil SimonRSS
Author, Speaker, and Professor
Author, Speaker, and Professor
Phil Simon is a keynote speaker and recognized technology expert. He is the award-winning author of eight management books, most recently Analytics: The Agile Way. His ninth will be Slack For Dummies (April, 2020, Wiley) He consults organizations on matters related to strategy, data, analytics, and technology. His contributions have appeared in The Harvard Business Review, CNN, Wired, The New York Times, and many other sites. He teaches information systems and analytics at Arizona State University's W. P. Carey School of Business.
Event stream data: When motive meets opportunity
.@philsimon sees these two colliding sooner rather than later.
IOT's twin challenges: Compliance and privacy
.@philsimon raises some chilling questions about the IoT and current threats.
Data privacy and governance: No easy answers
.@philsimon advises to be wary of those promising obvious and facile solutions to increasingly challenging governance and privacy issues.
Recommendations on privacy, data access and compliance
.@philsimon chimes in with trust- and privacy-related recommendations
When privacy and compliance collide with the need for speedy access to data
.@philsimon says that, once again, there's quite a bit to learn from Amazon.
Where challenge and opportunity collide: Big data meets data governance
.@philsimon looks at the challenges and opportunities that big data pose for data governance.
Data privacy in a mobile world
.@philsimon says that privacy is your issue as well whether you believe it or not.
Is it wise to crowdsource data governance?
@philsimon says that it's downright silly to ignore the benefits of thinking about data-related issues in different and unexpected ways.
Data preparation: Empowering business users via self-service
.@philsimon says don't treat data self-service as a binary.
What does security mean in the world of big data?
.@philsimon says that even seemingly useless information can be useful under the right circumstances.
The next data governance challenge: Agility
.@philsimon says that data-governance professionals will need to be more agile than ever.
What AirBNB teaches us about traditional data governance
.@philsmion says that even the "best governed" organization today isn't safe from inquiring minds.
Should organizations separate analytics governance and data governance?
.@philsimon asks, Rather than trying to tackle a new form of governance, wouldn't your organization do better to shore up its existing data-governance practices?
Why an agile mind-set will lead to better data prep and analytics
.@philsimon on the need to adopt agile methodologies for data prep and analytics.
Better tech means better analytics and less data prep
.@philsimon chimes in on new data-gathering methods and what they mean for analytics.
Data prep should not be a one-time exercise
.@philsimon on the downside of the Band-Aid approach.
Data prep considerations for analytics, Part 2
.@philsimon continues his series on data prep and anlytics.
Data prep considerations for analytics, Part 1
I'm hard-pressed to think of a trendier yet more amorphous term today than analytics. It seems that every organization wants to take advantage of analytics, but few really are doing that – at least to the extent possible. This topic interests me quite a bit, and I hope to explore
Modernization requires unification
.@philsimon channels his inner Queen Latifa.
ETL and data warehouses are dead! Long live ETL and data warehouses!
Why they will still play a valuable role in organizational data-management and -integration efforts.
Four DI modernization mistakes
.@philsimon with some recommendations on modernization.
Modernization starts at the top
Absent a strong executive presence, most mature organizations will continue to muddle through data integration.
Thoughts on data scientists, data modernization and golf
What's more, CXOs who believe that they can substitute data scientists for real data integration are as foolish as the duffer who consistently uses the wrong club.
Examining the relationship between MDM and customer intelligence
.@philsimon on whether organizations need MDM to gather valuable insights about their customers.
The next wave of MDM: Integrating structured and unstructured data
.@philsimon on what's next for MDM applications.
Does MDM need to be part of your data strategy?
.@philsimon on the role of MDM. TLDR: It depends.
What's the relationship between big data and MDM?
.@philsimon on the collision between the two.
Considerations and tips for big data integration
.@philsimon asks some fundamental questions about taking the next step with #bigdata.
How will the IoT affect your data management strategy?
.@philsimon says that it's never too early to think about the IoT and data management.