What Are the Three Distinct Characteristics That Discriminate Big Data From Traditional Data?

“What are the three different characteristics that distinguish big data from traditional data science?” This is a question asked by many IT executives who have recently heard the term “big data” and are searching for answers. More, business executives are realising the potential of big data to improve their businesses and help them grow. In an effort to define this term, it is useful to first look at what data actually is. It is an actual volume of data, that can be analysed over time and used to make informed decisions about important issues. There is also some room for interpretation and new models of the data can emerge.

what are the three distinct characteristics that distinguish big data from traditional data

Traditional data has always been traditionally collected by asking customers for permission to collect specific information about them. The problem with this approach is that there is no way of uniquely identifying the customer – in fact, there is no unique identifier or name that the customer provides to make it easy to associate with them in the context of the entire organisation. This makes it difficult to make informed decisions on what to do with this customer data, as well as whether to sell it or to hold on to it. A large amount of time and money can be wasted attempting to adopt the right decision depending on which perspective the data actually falls within.

Traditional databases used to be constructed using traditional engineering approaches where all records were linked together using logical relationships. This approach made it very difficult to efficiently identify which records belonged to which customer and which information had any value to the company. As new technologies developed, it became more practical to construct the database with more logic and a greater ability to uniquely identify records. The traditional database still needed to keep up with the volume of data collected – which was often the case when processing huge volumes of data by traditional systems.

Big data is designed to address these challenges faced by traditional systems. Firstly, it contains an enormous amount of information. It is not possible for a single, traditional storage room to contain it all. Second, it requires a significant upfront investment. Traditional databases require a significant memory write-back on each visit to the database, meaning the business will have to invest in storing the information on other devices, or else incur significant cost penalties for re-delegating data storage.

This is a critical issue for business, because the time required to implement a data management strategy will be greater than the time spent storing and accessing the data. This will also affect the level of return on investment made by the initial implementation. Lastly, it creates a challenge for software developers to build a system that can quickly and reliably process this data. In order to address these issues, developers need to focus on several factors when designing a solution. They need to consider what data is unique to their business, the nature of queries that they will conduct, the storage methods used and the unique needs of each customer.

Big data centers typically consist of servers, and storage. Data center houses servers that have unique workload requirements and power requirements. In contrast, traditional data centers house servers that are standardized and have low power requirements. The latter allows data centers to remain flexible and reduce IT staff costs. While some IT staff may be required to oversee the operations of a traditional data center, others may be willing to spend time supervising the deployment and management of the new, highly-efficient data center.

While managing big data presents significant challenges, developers can greatly reduce operational expenses by implementing time and attendance tracking into their data management solutions. Time and attendance data will allow business managers to effectively measure employee involvement and suggest the most efficient strategies for meeting organizational objectives. Not only does this help businesses track their activities, it also helps them understand how well employees are utilizing their time. Tracking time and attendance data also helps businesses understand their employee turnover and help them design incentive programs to retain employees.

While it’s clear that big data is transforming business processes, many IT departments haven’t yet fully embraced this paradigm. However, as this transformation occurs, IT departments should work with business managers to determine their immediate goals and how they will meet those goals. Until then, IT departments must stay on top of the emerging trends and apply their knowledge and skills to help businesses understand, utilize, and leverage big data. IT professionals must take advantage of opportunities such as the one currently being offered by the collaboration between enterprise service companies and telcos. This effort will increase IT expertise, drive greater revenue, improve employee engagement, and lead to more cost-effective solutions. In addition to these unique opportunities, IT professionals will continue to work on strategies to address the three distinct characteristics of big data: flexibility, scalability, and data specialization.