Given the headlines each week, it is clear that global disruption and economic volatility are not slowing down. At the same time, information overload is far exceeding human capacity. Despite these pressures, business goals remain the same: improve revenue, increase margins, operate more efficiently and meet customer expectations. So, how do
Kim Kaluba explains how connecting data to decisions helps create resiliency.
Phil Simon flip-flops from his last post.
Learn why Jason Simon from UNT calls data governance critical.
There's a chasm in today's business world between "can" and "should." Let's hope that gap closes soon.
Joyce Norris-Montanari cites five things she considers essential to data management for analytics.
Avoid frustrations by following these 5 tips from David Loshin to create a successful data management strategy for analytics.
David Loshin says entity resolution isn't a bandage to fix errors – it should be part of your data strategy.
Once you have a data strategy for omnichannel, what's next? Kim Kaluba explains.
Kim Kaluba says start the journey toward ominchannel with a data strategy based on governance.
Joyce Norris-Montanari says focus on data quality and governance, privacy and security when providing data on demand.
Kim Kaluba describes how a customer data strategy can help you achieve an omnichannel vision.
Data-driven businesses outperform competitors. Matt Magne says SAS Data Governance and SAS MDM can help you get there.
Analise Polsky says analytics success for midsize business depends on getting the basics right and maintaining a data focus.
Does age matter? Perhaps not, but maturity certainly does. The level of analytics maturity, in particular, makes a big difference to the options open to companies, and the strategies that they can adopt to get best value from analytics. A model of analytics maturity I like Thomas Davenport’s model of
Platform and strategy are core to compliance, but Jim Harris says commitment from people across the organization is just as important and harder to achieve.
To show how they're compliant with regulatory mandates, organizations first need an enterprise data strategy. Joyce Norris-Montanari discusses the issues.
Data governance seems to be the hottest topic at data-related conferences this year, and the question I get asked most often is, “where do we start?” Followed closely by how do we do it, what skills do we need, how do we convince the rest of the organisation to get
Start with the end in mind -- wise words that apply to everything, and in the world of big data it means we have to change the way we look at managing the data we have. There was a time when we managed data quality, and the main goal was
What if you could predict with near-perfect accuracy what you’re going to sell and when your customer is going to buy? Right supply, right time is the goal German manufacturers have set themselves, without reducing the configuration options customers expect. Having almost completed stage 1 of their plan – changing
Data monetisation is a hot topic these days. Especially for people like me watching the movements of early adopters – companies who are using data to create new revenue streams or even create new businesses to capture those revenue streams. DataStreamX is a notable start-up whose sole business is cashing
.@philsimon on the role of MDM. TLDR: It depends.
.@philsimon says that it's never too early to think about the IoT and data management.
In the past, we've always protected our data to create an integrated environment for reporting and analytics. And we tried to protect people from themselves when using and accessing data, which sometimes could have been considered a bottleneck in the process. We instituted guidelines and procedures around: Certification of the data
As I explained in Part 1 of this series, creating a strategy for the data in an organization is not a straightforward task. Two of the most important issues you'll want to address in your data strategy are data quality and big data. Data quality There can be no data that is
Back before storage became so affordable, cost was the primary factor in determining what data an IT department would store. As George Dyson (author and historian of technology) says, “Big data is what happened when the cost of storing information became less than the cost of making the decision to
Creating a strategy for the data in an organization is not a straightforward task. Not only does our business change – our software solutions also change before we can ever get done with a data strategy. So, I choose to understand that a strategy has a vision, and my vision may change
In my previous post, I discussed the characteristics of a strong data strategy, the first of which was that a formal, well-defined strategy exists within your organization. This post discusses how often (and why) your organization’s data strategy needs to be updated. While strategy encompasses and sets the overall direction for
In my two prior posts, I discussed the process of developing a business justification for a data strategy and for assessing an organization's level of maturity with key data management processes and operational procedures. The business justification phase can be used to speculate about the future state of data management required
Data virtualization simplifies increasingly complex data architectures Every few months, another vendor claims one environment will replace all others. We know better. What usually happens is an elongated state of coexistence between traditional technology and the newer, sometimes disruptive one. Eventually, one technology sinks into obsolescence, but it usually takes much longer than we expect. Think of
