How to Make the Best Use of Data Being Collected by Your Business

What is Big Data?

In the field of information technology, big data does refer to a collection of data sets that are so complex and large that it becomes quite difficult if not out rightly impossible to process using traditional data processing applications or on-hand database management tools.

It is important to note that data is only valuable if it is used by the enterprise to make decisions. The key is therefore to understand the data as it is; completely unstructured, and using a scalable platform for its analysis, processing, and action so as to unlock its value. In this regard, businesses that wish to remain competitive must continuously learn how to use technology to provide meaning to the swathes of data that they collect.

Collecting Big Data

Online customers usually generate a lot of data trails which when effectively combined with social media footprints can be effectively used by businesses to generate leads and close sales. It is worth mentioning at this point that information tends to lose its value pretty fast and unless it is used efficiently, much of its value will be wasted. It is because of this that effective decision making should always be made using current, accurate real time data. To keep up with the demands of an ever changing client base as well as conditions, it is important to come up with a structured solution such as a real time management and utilization solution that ensures data is received and utilized.

It is important to note though that it not actually the size or quantity of data that’s revolutionary per se, but it is the simple fact that businesses can do something positive with this data. This particular revolution lies in the improved computational and statistical methods being employed. This has seen the growth and development of big algorithms which can solve problems much faster than would otherwise be achieved using conventional computational methods. Fresh methods of linking datasets has played a major part in the generation of new insights whereas new, creative and interesting approaches to visualizing data has proved to be a key cog to the process of creating, disseminating and using this knowledge.

Appreciating the uses of Big Data in Businesses

In fields such as marketing, businesses such as Amazon and Netflix and other small enterprises are constantly utilizing big data in the form of recommendation engines’ to help in making purchase suggestions which are normally based on previous interest of a client as compared to hundreds of millions of others. Credit card companies do a lot of data mining to accurately evaluate the overall risk of default by looking at simple purchasing habits, for example associating clients who purchase anti-scuff pads for their newly purchased furniture to being less prone to missing their payment schedules. In the public sphere, the allocation of police resources is usually made after using big data to predict when and where certain crimes are likely to occur; or using the highly technical genomic analysis to significantly speed up the breeding of crops such as corn, rice or wheat for drought resistance.

There are also tools which have been developed to help in the analysis of social media texts using statistics software and other statistical methods and give some meaning to the billions of text being sent at any given time. finally, just to show how far reaching the effects of big data are, Google recently analysed a wide array of clusters of given search terms by region in the US to accurately outbreaks of flu faster than would have been possible if they were to rely on the use of individual hospital admission records.

In the scientific world, scientists across many disciplines, research areas and industries rely on multivariate data with its powerful collection of processing routines to effectively and efficiently solve some of the most difficult problems in data analysis. An area worth mentioning is spectroscopy, a branch of analysis that uses the interaction of energy with a given sample so as to perform an accurate analysis which can provide reliable information on the chemical composition and subsequent conformation of the said samples.

How to Interpret and Analyse Big Data

Big data analytics is without doubt an area of rapidly growing diversity; it is multifaceted and more emergent. Even though this growth has historically been driven by the web, rapid growth is currently being witnessed in all major vertical industry segments, from Agriculture, Chemicals, Food & beverages, Industrial Manufacturing, Oil and gas , Pulp and Paper to Pharmaceuticals & Biotech and anything therein between. In general, the diversity to be found within big data analytics is present in four main segments; these are MapReduce, Scalable database, big data appliance and real time processing.

Through the use of Apache Hadoop, users can build their own parallel computing servers with dedicated storage in the form of SSD (Solid State Drive) or small disk arrays for performance in what are referred to as shared nothing architectures. This makes it possible to process unstructured data in warehouse like environments.

The use of scalable data has also become quite popular. There is a rapidly growing and vibrant community surrounding NoSQL, a non-relational, open source and horizontally scalable collection of selected database structures which efficiently address the user need for a database designed for streaming media and high traffic websites. Users can also do real time analytics on several or multiple data streams using StreamSQL.

To help users wade through this maze of data and ensure they use big data effectively, it is recommended that one gets in touch with a specialist enterprise in this highly technical field such as Camo.


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