Data Management For Data Science
The right data management tools are key to making effective use of Big Data, turning its volume into a resource rather than a daunting mountain of unsorted bits and bytes. With the right data management techniques, tools, Big Data can be divided relatively easily into manageable chunks.
Data analytics empower firms to dissect and study sets of data that matter most to them and their business goals.
Data capture is what’s going on when firms get people to sign up to various things, asking for details at all opportunities and collecting consumer insight based on the accumulated data. However, none of it will make much sense unless the useful data sets have been directly identified.
Data management constitutes the systematic approach to dicing and slicing massive piles of data into logical, digestible portions.
The key step to effective data management is defining and adopting a comprehensive, systematic process for using Big Data. A major component of this is to centralize all information collected into a single place, rather than using disparate systems. Data quality software integration, together with data visualization tools, can help achieve this primary step: putting data into the right context, and in this way making it meaningful.
Once this is achieved, it becomes far easier to get the right data into the right hands. XMG analyst Jacky Garrido has commented in a ZDnet interview that enterprises must isolate the right sorts of data in order “to avoid getting buried under the humongous amount of information they generate through various outlets.” Garrido compares Big Data to an ocean wave; companies must either ride on top of or be consumed by it.”
Analytics for Big Data can involve any number of procedures, some deriving from the pure traditional sciences of uinivariate, bivariate, and multivariate analysis. Of course, univariate analysis refers to the study of single-variables, bivariate to the study of two, and multivariate to the application of univariate and bivariate procedures, plus other procedures, to multiple variables.
Stream Processing (aka, Real Time Analytic Processing – RTAP)
Remember how we said one key characteristic of Big Data is velocity? Yes, well that being the case, the Data Scientist is not so much interested in looking at a traditional “data set” as he or she is in studying “data streams.” The Data Scientist must mine and analyze actionable data in real time using architectures that are capable of processing streams of data as they occur. This is a major area of Big Data and Data Science where the best and most robust tools are still in the making. All in all, current database paradigms are not ideal for stream processing, although the algorithms already exist.
As Mike Driscoll notes: ” … Calculating an average over a group of data can be done in a traditional batch process, but far more efficient algorithms exist for calculating a moving average of the data as it arrives, incrementally, unit by unit. If you want to take a repository of data and perform almost any statistical analysis, that can be accomplished with open source products like R or commercial products like SAS. But if you want to create a set of streaming statistics, to which you incrementally add or remove a chunk of data as a moving average, the libraries either don’t exist or are immature.”