Agile planning is different from predictive planning done in traditional project management. Often we come across posts by certain agile evangelists who claim that agile doesn’t need planning. To believe these claims would mean there would be endless iterations with a fully-funded product development team to get to a perfect end-state without concern for time or cost. This is utopian and untrue. Usually these claims are made by developers or people far removed from business. No business runs without planning or without any estimates of how much a product/service would cost. Agile projects also plan.The difference is in the planning.
The Project Management Institute PMI is the premier standards and professional organization for project management. It works in collaboration with American National Standards Institute (ANSI) and the International Organization for Standardization (ISO) to develop and promote project management standards. Its certifications recognize knowledge and competency and the Project Management Professional (PMP® ) certification is widely considered as the gold standard. PMI has been criticised for being slow to adapt to changes e.g. acknowledging that agile methods were being increasingly adopted in the tech industry as well as these being particularly suited to changing requirements and initiatives with uncertainties e.g. those in startups. However there’s not much generalized awareness in tech about standards beyond those related directly with tech.
Disruption has gone mainstream. Ever since the media picked up on the disruption thread and the technology revolution where startups have disrupted established slow-moving corporations, there has been a flurry of activity at these staid organizations to get onto the innovation bandwagon. Advisory firm Gartner, who gave us “hype cycles” and who thrive on publishing “magic quadrants” has coined the term “bimodal” to sanctify an exploratory, experimental approach to IT. Organizations are rushing to instill “intrapreneurship”, accelerators, corporate venture capital funds, and innovation labs to preempt being disrupted.
With the increasing adoption of agile, there’s been a lot of talk about moving from the project-centric delivery to product-centric delivery model. Initially software/IT borrowed project management practices mainly from manufacturing / construction industries, and such projects generally used sequential, waterfall processes. The technology revolution and the pace of change in IT has made such approaches difficult to sustain and the software industry has been quick to adopt agile methods like scrum which are adapted to product-centric delivery instead of projects.
The last few years has seen a massive change in the data landscape. With the rise of big data, there’s been rapid innovation in the tools, skills and roles working on data systems. Data architectures have evolved beyond monolithic, centralized databases and unwieldy analytic applications to distributed, scalable architectures with simpler collaborative and interactive analytic tools. In this post, I look at the defining features of modern data architectures.
Modern data architectures generally feature the following (though not all of these may be present in the same system):
