Big Data is useful only when we can do something with it; otherwise, it's simply a pile of garbage. However, the effort required to dig is sometimes like trying to a find needle in a haystack. A meaningful pattern emerges only with a lot of analysis. Analytics put to work, tries to analyze the data with every piece of machinery available, brains included. These machineries are nothing but tools accompanied by computing power to explore the data. This article attempts to give a brief overview about the techniques used with big data analytics.
Prior to analysis, the data is collected from different sources. You must arrange it in a manner so that an analyst can do their work and deliver some tangible data products useful for the business process of the organization. The collected data can be in various states, such as unstructured raw data, semi-structured data, structured data, and so forth. These are the raw materials of big data analytics. Then, the complex process of exploring starts to unravel hidden patterns, correlations, and insights. Analysts take the help of any and every available tools and technology in the process of analysis and try to get some value out of it. Therefore, what data analytics means is the process of examining a large set of data (with one or more characteristics that refers to it as big data) and uncover some meaningful information.
The analyst initially needs to make sure that the data have some value before employing rigorous endeavours and resources to analyze the data. Sometimes, simple visualization and statistics are what you need to get some results. The basic techniques are as follows:
- Basic monitoring: Monitoring a large volume of data in real time is also one of the ways to gain some insight. For example, simply by monitoring the meteorological data compiled over years, we can gain quite a bit of insight into the types of climate conditions of a geographical region. Also, the real time information of wind, humidity, pressure, temperature, and so on, can throw light upon the type of an upcoming storm. If we connect every dot, there can be a number of parameters with huge information. Today, if we can tap the trend of all the tweets in the social media, we can get easily get an idea of the masses and what they are thinking. The political analyst often does that and what they do is just monitor the streaming data.
- Slicing and dicing: This common technique refers to segmenting a large block of data into smaller data sets so that it become easy to view and comprehend. Segmentation is done repetitively until a more manageable size is obtained. Specific queries are fired to gain some insight or do some computation, create a graphical representation or apply statistical formula on the smaller data sets. This helps ascertain a certain perspective for the analyst sitting in the sea of data. One can only have queries when a perspective is definite. Therefore, the technique helps in building a query space when working with large volume of data.
- Anomaly detection: Anomaly, here, refers to the sudden change of events that occurs in an environment which can trigger different effects. For example, a sudden fall in the Sensex can have numerous causes, such as abrupt socio-political changes, war or natural calamity, or many other things. But, if we can detect the anomaly, it gives a valuable insight to understand and analyze the situation. A simple set of statistics or observation may help in solving the problem as well.
As should be obvious, analysis is not always straightforward or simple. In fact, in many cases it depends upon the complexity of the data, and the type of information we want to extract determines the type of analytics we want to involve in the process. Advanced analytics employs algorithms for complex analysis on varied formats of data, such as using machine learning, neural networks, sophisticated statistical models, text analytics, and advanced data mining techniques to get some meaningful pattern out of the volume of data.
- Text analytics: Text analytics is the process where meaningful information is derived from a collection of unstructured data. Dealing with unstructured data is a huge part of big data analytics; therefore, specific techniques are employed to analyze and extract information and finally transform it into structured information. The structured information then is used to conveniently analyze further. The techniques employed with text analytics are derived from computational linguistics, statistics, and other computer science disciplines.
- Predictive modeling: Predictive modeling uses data mining solutions and probability to predict outcomes. The technique is applied to both structured and unstructured data to forecast the result. For example, a predictive system may predict the number of consumers of a product shifting to another product based upon some behavioral attributes available or predict change in the mindset of the people by observing the tweeting trend in the social media, which can have a decisive sociopolitical outcome in a political campaign.
- Using statistical, data mining algorithms: There are numerous other advanced techniques of forecasting using statistics and data mining solutions. There are techniques such as cluster analysis, micro segmentation, affinity analysis, and the like.
This article, of course, only scratches the surface of the topic, yet perhaps gives a taste of what it is to be called big data analytics. The trend of the use of big data by organizations is gaining momentum rapidly for all the good as well as bad reasons. The result undoubtedly is open to use and misuse and we cannot stop it. New tools and technologies are created to aid in the process of big data analysis. Perhaps, awareness is the only respite.
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