An IT mindset has dominated the way organizations view and manage their data. Even as issues of quality and consistency raise their ugly head, the solution has often been to turn to the tool and approach data governance in a project oriented manner. Sustainability has been a challenge, relegated often to IT managing and updating data management tools (MDM, data quality, metadata management, information lifecycle management, and security). Forrester research has shown that less than 15% of organizations have business lead data governance that is linked to business initiatives, objectives and outcomes. But, this is changing. More and more organizations are looking toward data governance as a strategic enterprise competence as they adopt a data driven culture.
This shift from project to strategic program requires more than basic workflow, collaboration, and data profiling capabilities to institutionalize data governance policies and rules. The conversation can't start with data management technology (MDM, data quality, information lifecycle management, security, and metadata management) that will apply the policies and rules. It has to begin with what is the organization trying to achieve with their data; this is a strategy discussion and process. The implication - governing data requires a rethink of your operating model. New roles, responsibilities, and processes emerge.
Recent news of a a computer program that passed the Turing Test is a great achievement for artificial intelligence (AI). Pulling down the barrier between human and machine has been a decades long holy grail pursuit. Right now, it is a novelty. In the near future, the implications are immense.
Which brings us to why should you care.
Earlier this week the House majority leader, Eric Cantor, suffered an enormous defeat in Virginia's Republican primary by Tea Party candidate David Brat. No one predicted this - the polls were wrong, by a long shot. Frank Luntz, a Republican pollster and communication advisor, offered up his opinion on what was missing in a New York Times Op-Ed piece - lack of face-to-face discussions and interviews with voters. He asserts that while data collection was limited to discrete survey questions, what it lacked was context. Information such as voter mood, perceptions, motives, and overall mind set were missing. Even if you collected quantitative data across a variety of sources, you don't get to these prescient indicators.
The new wave of AI (the next 2 - 5 years) makes capturing this insight possible and at scale. Marketing organizations are already using such capabilities to test advertising messages and positioning in focus group settings. But, if you took this a step further and allowed pollsters to ingest full discussions in person or through transcripts in research interviews, street polls, social media, news discussions and interviews, and other sources where citizen points of view manifest directly and indirectly to voting, that rich content translates into more accurate and insightful information.
On June 9, Docker.com announced that it will release version 1.0 of Docker, an open source platform that could automate the deployment of various types of applications as lightweight, portable, self-sufficient containers and run them virtually on any infrastructure. This announcement indicates that the platform is ready for commercial use, including lightweight, portable runtime support and packaging via Docker Engine and cloud services for application sharing and process automation via Docker Hub.
We talked to some early adopters of Docker, including global ISVs and local solution providers. We believe that Docker-based solutions will disrupt the server virtualization market segment and further drive the adoption of cloud because of their:
Technology advantages. Today’s componentized applications often rely on other components, applications, or services. For instance, your Ruby on Rails applications might rely on MongoDB as a persistence layer while using nginx as a web server. Each component might also have its own set of dependencies, which could conflict with each other. Docker can easily package the necessary dependencies and separate them within their own containers.
Banks are burdened with sizable infrastructure, struggle to service traditional and emerging channels, are severely boxed in by increasing compliance demands, and are not particularly nimble — also due to overly seasoned business applications. At the same time, the banking industry is ripe for digital disruption, as banks’ traditional strengths of size and breadth aren’t sufficient to ward off a mix of alternate financial service digital disruptors such as Google, new digital banks, emerging payment networks, and traditional institutions like Wal-Mart entering this market.
Business agility will be their most fundamental strength and competitive weapon. But how do leading banks today compare on agility? We surveyed 30 banks and determined that high performers excelled in market agility dimensions. We then ranked US banks using customer experience and product expansion scores. This report is due out this month so stay tuned.
Clients now have a report that helps them make more informed choices about selecting a PIM solutions. PIM is not always a well understood master data solution option for Enterprise Architects. Questions arise about, do I need PIM or MDM or do both? Aren't PIM and Product MDM the same? How does this fit in my architecture? This report takes away the confusion, answers these questions. It gives insight into how vendors satisfy PIM demands, differentiate from MDM and operate in hybrid scenarios.
The first Forrester Wave collaboration across the Business Technology and Marketing and Strategy groups. In the age of the customer, tighter collaboration between business decision makers and technology management professionals is critical. This wave addresses both perspectives providing the business requirements for marketing and product professionals while also addressing the technical questions that are important when selecting tools. Yes, business and technology management can work together, be on the same page, and produce great results!
Master data management is a hot topic. And, this is at times surprising to me because the noise of big data is deafening. Big data is certainly sexier. MDM is like mom nagging to clean up the room - necessary, but a total buzz kill.
Here is some of the anecdotal evidence that is raising my eyebrows:
Our Forrester Wave for MDM was at the top of most read reports during Q1.
MDM inquiries from clients keep me very busy.
Vendors see MDM as a key growth area in their portfolios.
Consultancies are consistently pointing to client gaps in data governance and data architectures that point toward a master data problem.