When great chefs and first-tier enterprise architects are at the top of their game, they can create art works. These experts are the people they need to watch and learn from as they hone their craft and evolve into artists who are bona fide. There’s a lot in common between both groups. Master chefs and chefs of data understand a wide range of ingredients and select from the freshest available. They reduce waste to a minimum. They know how to follow a recipe–and more importantly, when needed, they can adjust on the fly. Both are incredibly creative as well–understanding how ingredients can be used in many ways. In addition to art, both master chefs and data chefs are scientists who are under constant pressure to be innovative, adding techniques and recipes that yield consistent results of high quality. If you’re still not sure what the perfectly cooked steak of a master chef has to do with the Big Data warehouse of a data architect, here are three recipes that will give you something to think about.
Maintain Control in Big Data Warehouse
Recipes and cooking techniques just like IT trends come in and go out of vogue. Think about how Smitten Kitchen and the cloud have made way for Julia Child and client-server technology. Data is a major new trend in data strategy. The goal was previously to collect all data in one platform. The new strategy is to have a data-management solution capable of managing all types of data from different sources and adapting to an evolving IT infrastructure. In their Big Data warehouse, Pro data chefs need tools that give them control to support analytics and manage data ingestion, onboarding, integration and quality requirements across all forms of data sources and types easily. Basically, an agent that works like yeast is what is needed. Yeast, as it has many forms, is a tricky ingredient. Left alone, leaves are inactive, but add warm water and it comes alive, releasing gas that causes the rise of the dough. Elite bakers know how to use yeast to turn a lump of flour into incredible bread and pastries. What if you had an agent with a similar effect on your business ‘ enormous volumes of data? Only 20 percent of their data is used by most organizations. The rest is asleep, or a lump of uninteresting dough. In the same vein, the value of the data, like yeast, has an expiry date. Here’s a standard recipe for creating a high-value, Big Data warehouse:
- Appoint a Big Data warehouse lead.
- Inventory different systems and data types.
- Determine operational business and IT requirements.
- Identify data governance and analytical requirements.
- Define an enterprise architecture deployment model.
- Implement data lifecycle and multi-temperature management.
Keep watching this space for more.