Dylan Jones is the founder of Data Quality Pro and Data Migration Pro, popular online communities that provide a range of practical resources and support to their respective professions. Dylan has an extensive information management background and is a prolific publisher of expert articles and tutorials on all manner of data related initiatives.
We've witnessed a significant rise in data governance adoption in recent years. Careers, technology, education, frameworks, practitioners – there's growth in all aspects of the discipline. Regulatory compliance across many sectors is a typical driver for data governance. But I also believe one of the main reasons is the realisation by
Hadoop has driven an enormous amount of data analytics activity lately. And this poses a problem for many practitioners coming from the traditional relational database management system (RDBMS) world. Hadoop is well known for having lots of variety in the structure of data it stores and processes. But it's fair to
The demand for data preparation solutions is at an all-time high, and it's primarily driven by the demand for self-service analytics. Ten years ago, if you were a business leader that wanted to get more in-depth information on a particular KPI, you would typically issue a reporting request to IT
In my last post, I talked about how to observe the impact of modernisation through a data quality lens. I asked you to consider the quality of your legacy data and what that means on the "shiny new toy" you intend to buy in the future. In this post, I
At some point, your business or IT leaders will decide – enough is enough; we can't live with the performance, functionality or cost of the current application landscape. Perhaps your financial services employer wants to offer mobile services, but building modern apps via the old mainframe architecture is impractical and a replacement
Fellow Roundtable writer David Loshin has commented in the past that: "MDM is popular because it is presented as a cure-all solution to all data problems in the organization." Many people see master data management (MDM) as the silver bullet to all of their business and data woes. But in
Most companies are battling with master data challenges whether they realise it or not. When you're consolidating financials from multiple billing systems, you're doing MDM. When you're migrating legacy systems to a new target environment, you're doing MDM. When you're trying to perform root-cause analysis across multiple systems for a
We had just completed a four-week data quality assessment of an inside plant installation. It wasn't looking good. There were huge gaps in the data, particularly when we cross-referenced systems together. In theory, each system was meant to hold identical information of the plant equipment. But when we consolidated the
I'm frequently asked: "What causes poor data quality?" There are, of course, many culprits: Lack of a data culture. Poor management attitude. Insufficient training. Incorrect reward structure. But there is one reason that is common to all organizations – poor data architecture.
When you spend long enough writing and working in any industry, you inevitably see trends emerge and reach varying levels of maturity. Data governance is one such trend, as you can see from the following Google Trends chart:
