What is Big Data analytics?
As technology continues to grow, the amount of data the devices are able to collect and store also keeps increasing. Similarly, the amount of collectible information also increases due to the increasing ease of collecting information in various sectors of any economy. This means that for any scientific research that relies on data analytics, they now have to deal with massive data sets, often complex and too large for traditional systems and applications to handle. These large and complex data sets are referred to as ‘big data.’
The science behind the extraction of information from these large and complex data sets by examining the data patterns, as well as correlations within the data is known as big data analytics. The information gathered from this process is often used in making business decisions regarding market trends as well as product advertisements. The bigger the organization, the larger the sets of data that they need to comb through. This means that multinational companies have to invest quite a large chunk of their budget towards data analytics as the influence and effectiveness of these processes continue to be acknowledged across all industries.
Characteristics of Big Data
Big data has four main characteristics that are used to determine and classify any data into the big data class or otherwise. These four characteristics include:
- Velocity and
Variety is a feature that refers to the heterogeneous nature of the data. This means that for any data to be considered as big data, it needs to be collected from different sources and these sources need to provide different kinds of data. Some of the examples of data that could be available within the database include photos, emails, texts, audios and so forth. Therefore, for any data to be considered as big data, it must have a variety of data sources. This heterogeneous nature of the data presents the analysts of the said data with a challenge in extracting the data, examining the data and analyzing the data.
Volume is the main characteristic that is used to classify any set of data as big data. The very name of ‘big data’ alludes to this fact and, therefore, any data must be large in size before it can even be considered to be big data. Big volumes of data mean that they need more resources to ensure effective examination and analysis of the data.
Velocity is the third characteristic used to classify data as big data. Velocity refers to the speed at which the said data is generated and flows from the sources of the data. If the data is massive and flows from the sources continuously, then the data can be considered as big data if it also has the other three features. Examples of sources of big data include sensors, mobile devices, social media networks, and business processes.
The fourth characteristic of big data is variability. It refers to the inconsistency that the data may show. This means that big data keeps behaving differently, hence it presents a challenge to its effective management and handling.
Challenges of Big Data
Big data presents a number of challenges to people interested in finding useful information from the data. These challenges include:
- Inconsistency in the data: This means that the data keeps changing in terms of generation and needs. The data analysts therefore need to keep themselves abreast of all the current trends in the sources of data in order to continuously come up with effective ways to handle the data.
- The big amounts of data continuously being generated: This means that the storage devices need to be large enough for the data. Also, the storage devices need to be fast enough to store all the data as it is generated.
- New data types arising out of new technology: New types of data from new and modern sources, such as social media data, are generating new forms of data such as tweets that may not conform to the traditional data types and analyzing these new forms of data needs newer methods to be created specifically for the sets of data.
Uses of Big Data
Big data has a variety of uses across a multitude of sectors and industries. However, large organizations are the primary users of big data and it is these firms that invest a lot into the systems of capturing the big data as well as its analysis in order to continue their market leadership in their subsequent industries. The three main benefits of big data include:
- Improving customer service by these firms: This is possible because the firms are able to obtain feedback on their services and products rather quickly through the new and efficient forms of data analysis.
- Ease of Identifying risk: It is possible to identify risks much earlier and take preventive measures.
- Increased efficiency: firms benefit from big data by changing their operations based on the useful information obtained from big data analytics.
Big Data Concepts
As has been identified above, there are several concepts and ideas that underlie the idea of big data. The characteristic of the data is obviously a key issue in determining and classifying the data as big data. Secondly, the challenges arising from the very nature of big data is also a key concept. Issues such as Variability of the data, the Velocity of the data, the Volume of the data as well as the Variety or heterogeneous nature of the data should be considered. All these characteristics are essential in the description, classification and analysis of the big data.
Big Data Analytics Assignments Help
For scholars of information technology and computer science courses, big data analytics assignments can prove tricky because of the relatively new concepts involved in the field as well as the need to always keep abreast of changing trends in technology and market demands. Therefore, it may require them to access a computer science help desk via online sources where they can readily access experts in the field to offer much-needed guidance in the field of big data.