From machine learning and artificial intelligence (AI) to robotics, more and more companies are embracing automation to drive process improvements across their business. Unfortunately, they’re falling short of achieving that result. The leading factor holding them back is data accuracy.
Now that the initial GDPR deadline has passed, what are companies doing to ensure that they have the steps in place to operationalize compliance?
I read with interest a Gartner report, Modern Data Management Requires a Balance Between Collecting Data and Connecting to Data, that made the case for a bi-modal approach to connecting and collecting data. The case being made is that being able to react “at the edges” of broader data infrastructure (making decisions based on real time data displayed on a tablet, for example) requires direct connection of processes and devices, while collection of data for operations and management insight requires a central collection point and rigorous validation of data accuracy and quality. However, ALL data requires a series of integration processes that describe, organize and integrate the information. That first step, the description, includes the location, trustworthiness and meaning of the data in question. Read more
From Gartner’s ‘Modern Data Management Requires a Balance Between Collecting Data and Connecting to Data:’ Data and analytics leaders need to take an aggressive approach that creates an appropriate balance between data collection and data connection.
As the value of data as a core asset to digital business is now widely accepted, the most immediate reaction is attempting to collect it as if that was the key to delivering business value. The very popular data lake trend, for example, puts the collection process at the center. But collecting data doesn’t necessarily deliver business value, and collecting data may not even be possible.
A theme that emerged from this year’s Gartner Data & Analytics Summit is that the future of data governance is evolving from a centralized compliance-focused strategy to one where decision-making is being democratized across the organization. According to Gartner, by 2020, 50 percent of information governance initiatives will be enacted with policies based on metadata alone.
So how does metadata apply to the democratization of decision-making? Consider the potential of machine learning for improving data governance processes. Read more
As readers of this blog are aware, we have frequently discussed a variety of topics related to master data management. Over the course of the last decade we’ve enabled a good number of companies to deliver better reference and master data, across all domains (customer, product, financial, supplier, employee, etc.), and for operational as well as analytical systems. You can read about a few of them here. Read more
In a previous blog I discussed the four primary MDM architectural styles: consolidated, registry, coexistence, and transactional. In case you missed it, read it here: MDM Architecture Styles – Do you have the right mix? Each has their individual strengths and weaknesses, but no single MDM architectural style is ideal for every application. Read more
“I can explain it to you, but I can’t understand it for you.”
A good friend of mine recently alienated the person that pays his salary with the above statement. While you might question his wisdom – I know I did (after a quiet chuckle) – I’m reminded of how often the same thought has gone unsaid when business and IT “collaborate” on traditional data warehouse projects. The business is baffled by the technical jargon that IT uses, and the IT team wishes the business could articulate their requirements in a form from which they can build the solution. The lack of a common language to explain what each side means hinders this understanding. Read more
(Updated October 2018) What is the right MDM architecture style for your organization? It’s definitely an open-ended question that deserves an informed answer, especially before making new investment decisions. But before tackling the question, it is useful to define and understand the MDM styles themselves. Dr. Dave Waddington of The Information Difference defined four broad styles in an article published in Information Management. If you follow the MDM space, you’ll see similar descriptions from most of the MDM industry analysts.