Evlotion of Big Data in Big data vs traditional Business

traditional Business and technologies

1. Big Data

2. The Evolution Of Data

3. It is important to understand the evolution of data within the enterprise in order to understand the true value of machine learning. When the internet started, the focus was on transaction response time within online transaction processing (OLTP) systems. These systems powered websites, and the focus was on improving reliability, availability and scalability (RAS). As transactions grew on e-commerce sites, we wanted to know who was using the system, from where, what they were doing, for how long and most importantly how we could offer more value to existing customers and bring in new customers. This brought a whole slew of applications around business intelligence and reporting or analytics engines. The Evolution Of Data Cont..

4. Traditionally, a platform was used to address an enterprise process workflow -- human resources (HR), finance, manufacturing, etc. They are what we categorize as enterprise resource planning (ERP), customer relationship management (CRM), human capital management (HCM), functional setup manager (FSM), information technology operations ( ITOps ), etc. The data generated by these workflows was then analyzed using analytics or business intelligence applications to make further modifications to workflow. These workflow applications were customized as the data warranted any changes in the workflow. The Evolution Of Data Cont..

5. When intelligence becomes the new platform, data from these traditional applications will be used to determine the workflow and actions of organizations. The workflow actions will be passed on to the traditional applications or directly to the people or system that will perform the actions. These new systems of intelligence will emerge and will force existing workflow applications to change to be end-user targeted. We are already seeing a trend where AI platforms are slowly becoming a playground for new intelligent applications. More importantly, because open source intelligent platforms in this area are as rich as the enterprise platforms, we are also noticing new generations of applications. These applications prescribe specific actions that can be taken in their field of expertise -- HR recruitment, personality analysis, service optimization, sales upsell, etc. The Evolution Of Data Cont..

6. Take commercial travel as an example. Early websites pointed out the lowest flight price available from point A to point B. Then came websites that compared across other websites and aggregated results. Now, we have the ability to choose a budget and have a website or app suggest destinations that fit that budget or better days for us to travel. Our decision has transformed from just getting data and then taking action to an action being recommended to us that we can then decide whether or not we want to pursue. This works best when we have clearly established our goals (in this example it is the budget). The Evolution Of Data Cont..

7. Evolution of Big Data Data Warehousing: In the 1990s, data warehousing emerged as a solution to store and analyze large volumes of structured data. Hadoop: Hadoop was introduced in 2006 by Doug Cutting and Mike Cafarella . Distributed storage medium and large data processing are provided by Hadoop, and it is an open-source framework. NoSQL Databases: In 2009, NoSQL databases were introduced, which provide a flexible way to store and retrieve unstructured data. Cloud Computing: Cloud Computing technology helps companies to store their important data in data centers that are remote, and it saves their infrastructure cost and maintenance costs.

8. Evolution of Big Data 5. Machine Learning: Machine Learning algorithms are those algorithms that work on large data, and analysis is done on a huge amount of data to get meaningful insights from it. This has led to the development of artificial intelligence (AI) applications. 6. Data Streaming: Data Streaming technology has emerged as a solution to process large volumes of data in real time. 7. Edge Computing: E dge Computing is a kind of distributed computing paradigm that allows data processing to be done at the edge or the corner of the network, closer to the source of the data.