Introduction to big data
The term big data is relatively new but has been in use for a while now mostly without a laid down definition for the term. Big data is used to describe a very large data volume, structured and unstructured and cannot be processed by the traditional data processing software. The usefulness of data analysis and manipulation by business organizations cannot be understated. If properly analyzed, huge and positive insights can be derived from big data.
Big data concepts
Big data has a deep base which is characterized by some basic elements which are;
- Volume: the vast and mostly unstructured data sets collected via many sources including business transactions and social media info by organizations are mostly just observed and tracked and not sampled. This leaves lots of data to be analyzed.
- Velocity: it is available in real time and must be handled with speed and accuracy.
- Variety: the data stream comes in text, image, audio and video formats and could be structured or unstructured.
- Variability: the unpredictability associated with data inflow there for all to see. Data streams can be very inconsistent with periodic peaks. This is even more difficult to manage for unstructured data sets as seen with daily or seasonal data loads associated with social media.
- Veracity: because of the inconsistency of the datasets associated with it, accurate analysis of the data becomes a problem.
The major task when dealing with big data lies in the ability to transform and derive value from big data storage. This is usually done with big data analytics tools like Hadoop.
Big data architecture
Big data architecture deals with the logical, physical and structural layout of big data storage, accessibility and manageability within an IT environment. Basically, it explains the process including the core components needed, information flow and security.
The architecture is created by big data architects before a solution is physically implemented. The architects study and understand a business/organization’s process and big data needs before creating a solution for them.
Big data architecture is classified into four different layers namely;
- sources which are basically where the big data is coming from
- Messaging and storage which deals with big data storage
- the analysis which deals with the big data analytical framework
- Then it’s consumption which deals with the usage of the analyzed data.
Big data applications
The increased demand of information management specialists as a result of big data has seen huge IT companies like SAP, Oracle, IBM, and co. spending billions of dollars in the process. The increasing use of data-intensive technologies is also helping to boom the industry. They are currently over 4 billion mobile-phone subscribers in the world with over 2 billion of the accessing the internet. These create the need for useful data analysis and manipulation by top businesses and thus it thrives. Various corporate bodies and government agencies continuously rely on big data to grow, the following examples help to show this:
Government: it helps create accountability in government by allowing efficiencies in terms of cost, productivity, and innovation.
Manufacturing industries: it provides the platform for the manufacturing sector to thrive helping them unravel uncertainties in component performances and availability. It supports predictive manufacturing which in turn ensures a near-zero downtime framework.
Healthcare: big data has made an extensive impact on the healthcare sector. It has helped improve patient care by reducing waste and care variability, automated patient data reporting and improved clinical risk intervention.
Internet of things (IoT): it has helped push the innovation of IoT. Data from IoT devices are increasingly being used by many organizations to accurately target and meet their audience. IoT is also now a major means of gathering sensory data for medical and manufacturing research.
Other vital big data application can be seen in the education sector, Information technology, and many research activities.
Benefits of big data
The most enticing benefit is that it helps organizations make better and intelligent business decisions. Better and intelligent business decisions help organizations in their productivity and sales effectiveness.
The list below shows these benefits;
- Saves time: tools like Hadoop help identify new sources of data for organizations, it also help in analyzing the data in real-time which ensures speed and accuracy of business decisions.
- Saves costs: because of the positive effect that arises from the real-time analysis and implementation of decisions aided by big data tools, companies save lots of money.
- Innovations: information from big data help organizations better understand their customer needs which in turn brings innovations.
- Business growth: companies that make use of big data solutions are guaranteed to stay ahead of their competitors.
Conclusion
Many organizations are increasingly taking advantage of the power of big data to grow the business. It breeds innovation and economic competitiveness which is good for everybody. It processes and ensures accountability. As already mentioned, the bulk of the job lies in the analysis of the data. So if firms can utilize the power of big data for their good then surely business growth is guaranteed.