Feedback and Big Data
How do we improve the way we do things? We measure them, observe, make a change based on our observation, measure again, and then repeat, looking to make our process, product, or idea better over time. We design our software this way as well, especially in any of the rapid and agile development methodologies. We get feedback as we make changes, adapating our efforts to improve the end product.
We should be doing this with data as well, especially as we start to drown in waves and waves of data that grow over time. This piece from Alistair Croll talks about the ways in which we can adapt feedback to our data process. The various parts of the feedback loop, from collection to storage to analysis are all challenges we face as we start to encounter Big Data.
The challenges that companies will face as they try to find competitive advantages in all their data create huge opportunities for those of us working as data professionals. Whether we learn how to better manage the storage of data, we study techniques for pattern recognition or machine learning from large data sets, or we find ways to present complex data to humans for deeper analysis, this is a good time to work with databases.
We have seen SQL Server evolve and grow, and become incredibly complex. It’s expanded into spreadsheets (PowerPivot), high speed data collection (StreamInsight), and even other platforms (Hadoop). It’s too much for any one person to master, but the complexity and breadth of the platforms gives you the chance to pick an area that interests you. You can learn a bit about the whole feedback loop for data, specialize in the area that speaks to you, and hopefully have a challenging, interesting, and successful career.
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