Posts Tagged Big Data

What Is Smart Data?

Data that is being collected at an exponential rate is collectively known as big data. In its raw form, data is meaningless. So, to gain maximum benefit, it needs to be processed and turned into actionable information. As per an article by Forbes, keeping the rate of data growth in mind, approximately 1.7 megabytes of new information will be created every second for each human being on the planet by the year 2020.

Smart data is formatted digital information that can be acted upon before being sent for further data consolidation & analytics. It is a result of clear focus on purpose, insights, actions & resulting outcomes. Thus, it refers to the data that is decisive, valuable and actionable in real time. Smart data facilitates analysis and interpretation of data for better decision making and optimized data driven functions. The term smart data is often associated with IoT (Internet of Things).

Application Of Smart Data

  • Retail – Smart data can help in understanding customer requirements as well as help in localized promotions.
  • Telecom – It assists in bandwidth allocation.
  • Manufacturing – Can be used for proactive maintenance.
  • Healthcare – Monitor patient’s health reports to ensure complete safety & care.
  • Transportation– In driverless cars and also in detecting unsafe driving.
  • Financial Services – It can help in detecting & preventing financial frauds.

Difference Between Big Data & Smart Data?

The primary difference between big data & smart data is that the latter is actionable and adds more value to the data. Big data refers to the humungous amount of data that is collected whereas smart data is much more useful and applicable. So even though there is abundance of data in case of big data, it is usually unintelligent and needs to be churned up before it can be used for analysis. Smart data on the other hand is accurate & agile that can give better insights.

Collecting Smart Data

It is common for organizations to collect everything and store it in their data warehouse with an aim to use it for decision making. However, since the data is collected without any specific purpose in mind it often lacks quality. To curb this problem, an organization needs to be smart enough in its way to use the resources to collect data that is relevant to their business. Smart data is gaining popularity due to its relation with the coming algorithm economy. So, collecting only important and useful data does not

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Benefits of Hadoop

Hadoop is an open source distributed processing framework that is commonly used to process and store big data. Not only does it store but also helps in running applications on clusters of commodity hardware.

Software solutions like Hadoop have become a necessity for businesses that store & use big data due to its ability to store & process massive amounts of data. A classic feature of Hadoop is that it can handle both structured & unstructured data thus providing enhanced flexibility to users.

Reasons To Implement Hadoop

  • Massive Storage – With big data increasing rapidly, there was a need for a software solution that could handle large volumes of data. Hadoop has solved that problem with its ability to hold enormous amount of data. The data is often split into blocks and then stored in clusters.
  • Scalability – You wish to have the ability to handle more data? No problem, with Hadoop you can do that easily and that too without any hassle. With little administration Hadoop allows you to add more nodes easily.
  • Flexibility – Hadoop enables you to store as much data as you want whether it is structured or unstructured, formatted or encoded or in any other form. So apart from text you can store images, videos, etc. Also, with Hadoop you are not required to process data before storing it, thus making it relevant as well as simple to use.
  • Computing – Its computing model helps in processing large amounts of data easily. The arithmetic is simple; more the nodes, more the processing power that you have.
  • Fault Tolerance – Hadoop is fault tolerant i.e. it protects the data in case of any hardware failure. When a node gets lost or goes down, Hadoop framework redirects the task/work to another node to avoid any disruption. Also Hadoop keeps a duplicate copy of data which is then stored in different clusters.
  • More Real Time – It allows you to store information into a cluster and analyze it in real time. It provides a standard approach to numerous API’s for big data analytics.
  • Cost Effective – It uses commodity hardware to store data which leads to reduction in cost per terabyte of storage.

When Not To Use Hadoop?

  • When you wish to modify data
  • There are lots of small files
  • There is low latency data access

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Privacy Concerns Surrounding Big Data

30th Oct 2017

A lot of information and data is there on the web, and even more inundates into the network each day. For organizations collecting data from individuals, it is of vital importance that proper security measures should be in place to prevent misuse of information. International Data Corporation (IDC) has predicted that that by 2020 more than 1.5 billion people would be affected by data invasion, which is indeed worrisome.

For individual victims, data breach can result into loss of confidential information. Whereas for companies, data breach or loss of valuable customer information can erode business image and result into loss of market share as well as customers, owing to mistrust and lack of confidence.

Here we have listed certain privacy concerns surrounding big data that you need to be aware of.

Privacy Breaches – Organizations collect a large volume of data each day. This data is generally analyzed by businesses to provide customized services to their clients or to have better understanding of customer segments. The results can be catastrophic if this data, which may include personal or financial information of customers is compromised. There have been instances in past where hackers invaded the data sources of large firms and exploited it, which thereby brought in a lot of embarrassment to the company.

Favoritism /Discrimination – Big data analytics can be used for discriminating or favoring a particular person which is ethically wrong. It impedes the true objective of big data.

Defeat The Purpose Of Data Masking – Data Masking refers to creating a proxy version of an individual’s data. It is to protect the actual data by decoding or hiding it with random characters. However, if this is not done properly, the actual purpose of data masking is defeated altogether as privacy can easily be encroached.

Unauthorized Use Of Information –  Big data analytics is a common phenomenon today, which on one hand has helped quite a few industries, and on the other has increased the potential for large- scale thefts and unauthorized use of sensitive data. It is important to safeguard vital information by taking proper security measures.

Loss Of Control Over Private Information – Once there is a data breach, your private information does not remain private anymore. If the information is floated over, there are chances that there would be absolutely no way to retain its spread over the web.

Unwanted Data Inferences – Data collected from various sources is contemplated to make data inferences. The documented information from different sets of data is co-related to discover hidden patterns which may reveal certain behavioral inferences of individuals involved in the data study.

Today, we cannot boycott technology, even if we do it does not guarantee us that our private information shall stay secure and protected. The only way is be aware of the potential risks we are exposed to, so that necessary preventive steps can be taken well in time.


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