Abstract: Data in today’s world is much more complex than ever before. With the technological advancements businesses are able to easily gather data both at the organizational level as well as from the external data sources. The accumulated data is huge and diverse with structured, unstructured components or data generated by Internet-of-Things (IOT). Businesses are in dire need to analyze these sets of data to derive a better value to the organizations. With analytics becoming central to all the business strategies, this paper presents a review of the challenges which the organizations have to take into account while dealing with these complex data residing in the data stores. Apart from the volumes and complexity of data, IOT brings in new challenges in the form of security to the BigData systems as a whole and data in particular. This paper also reviews the conceptual studies which have attributed to the growth of Bigdata technologies to provide business analytics by ensuring security to the data. Keywords: BigData, BigData Analytics, Challenges, Internet-of-Things, Security BigData Analytical Challenges with IOT Lalitha Balla 1* , Chavva Ravi Kishore Reddy 2 , A V L N Sujith 3 1 Professor, Department of Computer Science and Engineering, JNTUAC, India. 2 Assistant Professor, Department of Computer Science and Engineering, VLITS, Vadlamudi, India. 3 Assistant Professor, Department of Computer Science and Engineering, JNTUP, India. *Corresponding Author E-mail: lalitha_balla@yahoo.co.in I. IntroductIon BigData is being generated by almost all the components around us, but to derive a meaningful value from this data we need have processing power and analytical capabilities. Organizations have to carefully manage their business model around the key processes as shown in Fig 1. Fig. 1: BigData Processes Insights from BigData will help the organizations to make better decisions, provide better customer service and get better operational effciency and new sources of revenue. The importance of BigData is realized only it is able solve the business-related tasks such as failure detections in real- time, performing risk-management and detecting fraudulent behaviors before they can affect the organization [3]. A. Data Acquisition Data acquisition is the process of gathering data from different data sources. It is commonly governed by the three V’s- volume, variety and velocity. The core of data acquisition comes down to acquiring data from distributed information sources to store them in a BigData-capable data storage. To achieve the organizations have to keep in mind of the protocols that help in information gathering from these distributed sources and technologies that provide the persistent storage. The nature and source of this data is very diverse and complex. Tools and methods are used deal with such data for improved performance of the BigData stores.The main goal of a data acquisition strategy employed at an organization must understand the needs of the system and take the right decision on which tool is best to ensure the acquisition. B. Data Cleaning Data cleaning is the process of preparing the data for analysis by dealing with corrupt or inaccurate records. The process is mainly in data stores which usually have the inaccurate, incomplete or irrelevant data. A typical data cleaning process involves removing of errors or validating and correcting values of records. Raw data which is usually present in several formats is transformed by the data cleaning process. High quality data need to have high accuracy, completeness, uniformity and consistent behavior across the system. Data preparation i.e. Data acquisition, data cleaning and data management constitute major part of the work for a data scientist. Understanding these steps improves the effciency of the data acquisition activities there by streamlining the business activities which improves the productivity and revenue. C. Data Analytics BigData analytics deals about using advanced analytical algorithms and methods against very large and complex data sets. It is about discovering patterns and other useful information and knowledge that comes with analyzing the data. The important analytical techniques include text analysis, data mining, machine learning, natural language processing and International Journal of Distributed and Cloud Computing Volume 5 Issue 1, June 2017 ISSN.: 2321-6840