The term Big Data is being increasingly used almost everywhere on the planet – online and offline. And it is not related to computers only. It comes under a blanket term called Information Technology, which is now part of almost all other technologies and fields of studies and businesses.

Big Data is a term used to describe a collection of data that is huge in volume and yet growing exponentially with time. In short such data is so large and complex that none of the traditional data management tools are able to store it or process it efficiently.

Big Data is essentially the data that you analyze for results that you can use for predictions and other uses. When using the term Big Data, suddenly your company or organization is working with top-level Information technology to deduce different types of results using the same data that you stored intentionally or unintentionally over the years.

Big Data

What is big data analytics?

Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.

Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency. Big data has one or more of the following characteristics: high volume, high velocity or high variety. Artificial intelligence (AI), mobile, social and the Internet of Things (IoT) are driving data complexity through new forms and sources of data. For example, big data comes from sensors, devices, video/audio, networks, log files, transactional applications, web, and social media — much of it generated in real time and at a very large scale.

Analysis of big data allows analysts, researchers and business users to make better and faster decisions using data that was previously inaccessible or unusable. Businesses can use advanced analytics techniques such as text analytics, machine learning, predictive analytics, data mining, statistics and natural language processing to gain new insights from previously untapped data sources independently or together with existing enterprise data.

Types of Big Data

Big Data could be found in three forms:

  1. Structured

Any data that can be stored, accessed and processed in the form of fixed format is termed as a ‘structured’ data. Over the period of time, talent in computer science has achieved greater success in developing techniques for working with such kind of data (where the format is well known in advance) and also deriving value out of it. However, nowadays, we are foreseeing issues when a size of such data grows to a huge extent, typical sizes are being in the rage of multiple zettabytes.

  1. Unstructured

Any data with unknown form or the structure is classified as unstructured data. In addition to the size being huge, un-structured data poses multiple challenges in terms of its processing for deriving value out of it. A typical example of unstructured data is a heterogeneous data source containing a combination of simple text files, images, videos etc. Now day organizations have wealth of data available with them but unfortunately, they don’t know how to derive value out of it since this data is in its raw form or unstructured format.

  1. Semi-structured

Semi-structured data can contain both the forms of data. We can see semi-structured data as a structured in form but it is actually not defined with e.g.

Types of Big Data

Importance of big data

Companies use the big data accumulated in their systems to improve operations, provide better customer service, create personalized marketing campaigns based on specific customer preferences and, ultimately, increase profitability. Businesses that utilize big data hold a potential competitive advantage over those that don’t since they’re able to make faster and more informed business decisions, provided they use the data effectively.

For example, big data can provide companies with valuable insights into their customers that can be used to refine marketing campaigns and techniques in order to increase customer engagement and conversion rates.

Furthermore, utilizing big data enables companies to become increasingly customer-centric. Historical and real-time data can be used to assess the evolving preferences of consumers, consequently enabling businesses to update and improve their marketing strategies and become more responsive to customer desires and needs.

Big data is also used by medical researchers to identify disease risk factors and by doctors to help diagnose illnesses and conditions in individual patients. In addition, data derived from electronic health records (EHRs), social media, the web and other sources provides healthcare organizations and government agencies with up-to-the-minute information on infectious disease threats or outbreaks.

In the energy industry, big data helps oil and gas companies identify potential drilling locations and monitor pipeline operations; likewise, utilities use it to track electrical grids. Financial services firms use big data systems for risk management and real-time analysis of market data. Manufacturers and transportation companies rely on big data to manage their supply chains and optimize delivery routes. Other government uses include emergency response, crime prevention and smart city initiatives.

Examples of big data

Big data comes from myriad different sources, such as business transaction systems, customer databases, medical records, internet clickstream logs, mobile applications, social networks, scientific research repositories, machine-generated data and real-time data sensors used in internet of things (IoT) environments. The data may be left in its raw form in big data systems or preprocessed using data mining tools or data preparation software so it’s ready for particular analytics uses.

Using customer data as an example, the different branches of analytics that can be done with the information found in sets of big data include the following:

  • Comparative analysis.

This includes the examination of user behavior metrics and the observation of real-time customer engagement in order to compare one company’s products, services and brand authority with those of its competition.

  • Social media listening.

This is information about what people are saying on social media about a specific business or product that goes beyond what can be delivered in a poll or survey. This data can be used to help identify target audiences for marketing campaigns by observing the activity surrounding specific topics across various sources.

  • Marketing analysis.

This includes information that can be used to make the promotion of new products, services and initiatives more informed and innovative.

  • Customer satisfaction and sentiment analysis.

All of the information gathered can reveal how customers are feeling about a company or brand, if any potential issues may arise, how brand loyalty might be preserved and how customer service efforts might be improved.

Big data challenges

Besides the processing capacity and cost issues, designing a big data architecture is another common challenge for users. Big data systems must be tailored to an organization’s particular needs, a DIY undertaking that requires IT teams and application developers to piece together a set of tools from all the available technologies. Deploying and managing big data systems also require new skills compared to the ones possessed by database administrators (DBAs) and developers focused on relational software.

Both of those issues can be eased by using a managed cloud service, but IT managers need to keep a close eye on cloud usage to make sure costs don’t get out of hand. Also, migrating on-premises data sets and processing workloads to the cloud is often a complex process for organizations.

Big data challenges

Making the data in big data systems accessible to data scientists and other analysts is also a challenge, especially in distributed environments that include a mix of different platforms and data stores. To help analysts find relevant data, IT and analytics teams are increasingly working to build data catalogs that incorporate metadata management and data lineage functions. Data quality and data governance also need to be priorities to ensure that sets of big data are clean, consistent and used properly.

Faremco will help you:

– Continuously detect data issues in the delivery pipeline

– Dramatically increase data validation coverage

– Leverage analytics to optimize your critical data

– Improve your data quality at speed

– Provide a huge ROI