Big Data has established an authentic tech niche of processes, personnel and passive technology to anchor the fast exploding new field. Big companies such as Amazon and Wal-Mart, Government institutions and social media platforms such as Facebook are utilizing Big Data to run and achieve strategic objectives. Apart from this, Big Data has also opened the huge potential for small and medium enterprises that can identify them. In this post, we set out to establish what is big data technology and everything you need to know about it.
What is Big Data and how does it work?
Big data is simply data, but in huge quantities. To understand what big data concept is, it is important to go back to the basic definition of the term data. According to the Oxford Dictionary, data comprise of quantities, characters, and symbols that computers operate on. It can be stored on drives and transmitted as electrical signals and recorded on a different medium. Now, Big Data is in huge quantities and complex such that traditional data management systems and old tools can neither handle nor process efficiently. It is advisable to look at big data as 3Vs (volume, variety, and velocity) expanded to include value, veracity, visualization, and variability. So complex is the concept of big data that the current data on the globe will double in 2 years and shorter periods thereafter.
Making sense of big data
Though big data concept can be traced back to mid-20th century, it was only recently that its complexity became more publicized. Despite this, businesses were still using the common analytics such as the spreadsheet to examine and uncover anticipated trends manually. However, the last 2 decades have seen the volumes grow so much that the traditional models of accessing, processing and analytics are no longer effective. With businesses relying on data in all areas of operations to draw decisions that define their operations, it has become critical to relook at the entire concept of Big Data.
The days of keeping all your data in Microsoft office documents or small files on the computer have passed away. It is time to sail on the sea of dunes in small caravans. The 7 terabyte share in the year 2000 will look minute when compared to the modern 900 terabyte marketing database. You must start looking at the importance of big data and exploit all available opportunities. To achieve this, there is need to make sense of the big data, appreciate areas where big data is used and key technologies used in big data.
The main uses of Big Data and key benefits
If you can process big data fast and appropriately, the following are the associated benefits.
- Cost reduction: When you use Big Data technologies like Hadoop or Cloud-based analytics, it comes with very many costs related advantages. You will also have more ways of doing business.
- Capability to make better decisions faster: Using the speed of Big Data, it is possible to analyze data faster and make the right decisions faster.
- Ability to design new services and products: One of the main advantages of big data and its impact is the ability to design new products and services. Because you can access larger databases, analyze clients’ needs and satisfaction, more companies can define new products to meet clients’ needs.
- Enhanced customer service: The traditional customer service models are fading fast and getting replaced by modern technologies modeled on Big Data technologies.
- Early risk identification in businesses: The impact of big data on business is particularly evident in risk identification and mitigation. Because of fast data accumulation, latest technologies that utilize artificial intelligence can help business to identify trends and point when risks are imminent.
Creating staging areas for new data: Prior to classifying data and establishing what should be moved to the warehouse, Big Data technologies help to organize the data and offload what is not used regularly.
Main categories of big data and how they are created
A closer look at big data real world examples reveals that Big Data is available in three key categories.
(1) Structured Big Data
This is any Data that can be stored, accessed, or processed as fixed formats can be referred as structured data. Computing experts have advanced so much in crafting technologies that work with structured data. However, growth in size hampers the structured data efficiency.
(2) Unstructured data
This is a group of Big Data that does not have a specific structure. Besides size, unstructured data brings a lot of challenges in processing and deriving value. A good example of structured data is a heterogeneous data source with videos, images, documents, and other types of files.
(3) Semi-structured Big Data
This is the type of data that contains both Structured and unstructured content. For example, you could consider semi-structured data as a structured form but lacks the common table format common in relational databases. One example of semi-structured data is data in an XML file.
Implications of Big Data today
Big data has promised transformation to both businesses and society. By the close of the first decade of the 21st Century, huge breakthroughs were achieved in enhancing greater storage capacity and computing power that yielded euphoria about anticipated potential. Even up to now, the impact of big data on society and businesses is still being pointed as an imminent revolution. It is not uncommon to hear companies pointing they will strive to collect, store, interpret, and capitalize on the latest vast quantities of new data.
Why the sluggishness by entities in taking full advantage of Big Data? Apart from the complexity of understanding what is big data technology, there is much more to overcome the integrative capabilities. For example, integrating internal and external data to get useful analytics might require full restructuring. It is this complex. The business culture must be redefined and big data impact on consumers needs to be carefully acknowledged.
The corporate teams must have both creative capacity and data science to exploit the new space of Big Data fully. Here, they have to be proactive and understand the new forms of challenges associated with big data. Depending on the types of big data technologies that a company may prefer, every corporate expert has to rethink the notion of security. How secure is the big data? What are the risks involved in Big Data?
Big Data analytics
With Big Data, you have to accept that things have to change. In particular, the analytics must be different. Depending on the big data architecture design, the analytics model lets the user do things that could only be dreamt off previously. For example, appropriate analytics can help to generate timely insights and arrive at decisions and draw answers to hard-to solve problems that point to new opportunities.
As businesses try to understand what is big data analytics, they must focus on picking the right technologies. Notably, no single technology can be considered ideal for Big Data analytics. Though some advanced analytics technologies can work in many data types, it is prudent to establish what is perfect for your business niche. The main types of Big Data analytics include;
- Data Mining: This helps in Big Data examination to determine patterns for further analysis and to solve complex business puzzles.
- Data management: This type of Big Data analytics technology works better when the data is of the highest possible quality. You need to establish the important data, especially the one that informs repetitive tasks.
- Hadoop: This is an open software framework with the capability of storing a lot of data and run multiple applications. Unlike most of other technologies, Hadoop has enabled more businesses to appreciate the impact of big data on information technology because it is free.
- In-memory analytics: When data is analyzed from the system memory as opposed to the computer hard drive, it is possible to draw more helpful insights that are more pronounced within shorter timeframes. This technology helps to clear data prep as well as analytical processing latencies for faster evaluation of new scenarios.
- Predictive analytics: This is one of the major Big Data analytics technologies that apply advanced methods such as machine learning and statistical algorithms to point at the expected future outcomes. It is also one of the main technologies applied in artificial intelligence to leverage customer confidence.
Main users of Big Data
After appreciating the difference between the Big Data and traditional data, the next step is looking at ways of using it to drive a competitive edge for the business. Already, there are companies and institutions applying Big Data and using the impact of Big Data analytics to design products & services and hasten their march to the next level. Here are some of the big data real world examples and users.
- In the travel industry, Big Data and Big Data analytics are used to help keep customers happy. Casinos and resorts are using big data to get a new lease of life by understanding what clients want and how to deliver it.
- Healthcare niche is adopting the Big Data and applying it to manage clients’ records, insurance files, and carry progressive analytics faster and more efficiently.
- In the fast unfolding retail sector, stores adopting e-commerce operational model have to adopt the Big Data to handle multiple marketing targets, process clients’ requests, and source for products. Top e-commerce stores such as Amazon and eBay have developed their own Big Data technologies to perfect their internet of things for greater efficiency.
- Many of the government agencies who have faced challenges of handling cumulative data are now shifting towards Big Data technologies and analytics for policy formulation, decision making, and enhance efficiency with the limited available resources.