Strategic advice to leverage new technologies
Technology is at the heart of nearly every enterprise, enabling new business models and strategies, and serving as the catalyst to industry convergence. Leveraging the right technology can improve business outcomes, providing intelligence and insights that help you make more informed and accurate decisions. From finding patterns in data through data science, to curating relevant insights with data analytics, to the predictive abilities and innumerable applications of AI, to solving challenging business problems with ML, NLP, and knowledge graphs, technology has brought decision-making to a more intelligent level. Keep pace with the technology trends, opportunities, applications, and real-world use cases that will move your organization closer to its transformation and business goals.
Insight
Executive Summary
The Role of Master Data Management in the Enterprise
A NEW NAME FOR AN OLD PROCESS
IT moves in a perpetual continuum. Themes recur throughout time, reemerging with different titles and small changes. To anyone who has been in the IT world for any length of time, it should come as no surprise that the care and treatment of corporate assets should include a place for the corporate data. For ages, enterprises have stored vast quantities of data gathered from operational and back-office systems.
There seems to be a mantra in many agile development organizations that the shorter the iteration, the more agile the team. While I believe strongly in short iterations (on the order of one to four weeks), sometimes the "shorter is better" mantra gets in the way of progress. It may also be a symptom of a process being too developer centric.
Although practitioners from both camps still tend to view them as separate -- yet complementary -- in truth, business rules management (BRM) and business process management (BPM) technologies are becoming more and more intertwined. This is due to the fact that BRM technology aligns very well with process optimization -- in particular, for automating repeatable decisions as part of some process or business activity.
Evaluating an IT project once it is completed is one of those activities that everyone recognizes as necessary but few organizations approach methodically. Post-project fatigue and pressures to redeploy resources make retrospection into what went right or wrong seem like an expendable activity. Yet organizational learning in the effective application and use of IT requires that IT projects be evaluated for their effectiveness as well as their contribution to business value.
Any doubt about where the market for business intelligence (BI) and data management is heading was shattered by IBM's recent announcement that it is launching a company-wide initiative that will invest US $1 billion over the next three years to expand its information management software and services offerings.
In recent years, application developers have pioneered techniques that enable them to work in an evolutionary (iterative and incremental) manner, and now we're going one step further with agile methods that are also highly collaborative. Unfortunately, many data professionals are still mired in the traditional, serial approaches of the late 1970s and 1980s. As a result, they're discovering that they need to play catch-up if they're to remain relevant within the new environment.
In my last Advisor (see "Service Component Architecture," 1 February 2006, I introduced the service component architecture (SCA), a new joint industry specification that decouples the implementation of services from their assembly of components. This week's Advisor is about a special type of component: the Service Data Object (SDO), which complements SCA by providing a common way to access different kinds of data.