David Loshin says entity resolution isn't a bandage to fix errors – it should be part of your data strategy.
Tag: data strategy
Joyce Norris-Montanari says focus on data quality and governance, privacy and security when providing data on demand.
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
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 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
Ever been stumped as you tried to find something in a huge, complex data environment that encompasses a hybrid of all types of internal and external data? It used to be that data systems were tactical, technically focused systems that provided point-to-point data access. In that era, it wasn’t so
In my last post, we touched on the importance of data migration in an overall data strategy. The reason I wanted to do this is because so many organizations see the migration of data as a technical challenge that can be outsourced and largely ignored by their internal teams. I contend
In my last post, I discussed some practical steps you can take to collect the right information for justifying why your business should design and implement a data strategy. Having identified weaknesses in your environment that could impede business success, your next step is to drill down deeper to determine where there may be
With data now impacting nearly every business activity, there should no longer be any doubt that data needs to be managed as a strategic corporate asset. This post examines the top five characteristics of a strong data strategy. Existence As I previously blogged, in today’s fast-moving business world now often takes priority
While setting up meetings with business consumers developing a data warehouse environment, I was involved in some very interesting conversations. Following are some of the assumptions that were made during these conversations, as well as a few observations. To get a well-rounded view of this topic, read my earlier post that focuses on the IT perspective.
.@philsimon on the convergence between tools such as Hadoop and strategy.
People often seek out our company for guidance related to master data management, data governance and data quality. But I see a frequent pattern, where the customer presumes that they need a particular data management solution – even if there is no specific data management problem. This approach is often triggered in reaction