Analytics help enterprises get valuable business information and insights by analyzing a mixture of structured, semi-structured and unstructured data.
So what exactly is Analytics? In simple terms, Analytics is a process of scrutinizing huge amounts of data sets that contain a variety of data types such as big data, in order to uncover some specific and hidden patterns, peculiar market trends, unknown correlations, customer preferences and other useful business information. These findings can then lead to more effective marketing, better customer service, new revenue opportunities, improved operational efficiency, competitive advantages over rival organizations and many other business benefits.
The primary objective of having an efficient function of Big Data Analytics in any organization is it to make better informed business decisions, in turn enabling data scientists and also predictive modelers, along with other analytics professionals analyze large amount of invaluable data.
This data might be the type of transaction data or any other form of data that may not have been tapped using traditional business intelligence (BI) programs within the organization. The various types of Big Data might range from clickstream data, social media content, Web server logs, Social network activity reports, any form of communication such as texts from customer emails and also survey responses, not barring machine data captured by sensors connected to the internet and even phone call detail records.
Systems Plus Offerings – Delivering Value
Systems Plus specializes in Big Data Analytics. It provides outstanding Big Data consulting solutions that are customized to help solve some of the most complex business issues faced by organizations.
Systems Plus not only institutionalize data based decision making for our clients by assisting them in analytical problem solving; bringing predictability to their business drivers and helping them internalize the processes by setting up right systems.
It is the data scientists, database experts, ex-investment bankers and consultants which makes us strong on all aspects of data analytics i.e. business understanding, statistics knowledge and experience with using the right analytical tools.
Big data can be usually be analyzed with using the software tools, commonly used as part of advanced analytics disciplines such as the predictive analytics, text analytics, data mining and statistical analysis. Also in addition, the mainstream BI software and data visualization tools can also play a crucial role in the analysis process.
However given the semi-structured and unstructured data that is procured, may not fit well in the traditional data warehouses based on relational databases. More so, the processing demands posed by the sets of big data that need to be updated often or even continually may not be handled well by the data warehouses. Examples such as, the real-time data on the performance of mobile applications or of oil and gas pipeline makes this problem evident. This results into several organizations that collect, process and analyze big data have now turned to newer class of technologies such as Hadoop and other related tools such as Spark, Hive, YARN, MapReduce and Pig as well as NoSQL databases. The advantages of these technologies being, that they are able to form the core of an open source software framework which inherently support the processing of massive and diverse data sets at the same time across clustered systems.