What does ‘Big Data’ mean?
Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily available hardware.
In recent years, there has been a boom in Big Data because of the growth of social, mobile, cloud, and multi-media computing. We now have unprecedented amounts of data, and it is up to organizations to harness the data in order to extract useful, actionable insights. But, because traditional systems cannot store, process, and analyze massive amounts of unstructured data, organizations are turning to Big Data management solutions to turn unstructured data into the actionable data needed for gaining key insights into their business and customers.
Benefits of using “BIG DATA ANALYTICS’
1. Identifying the root causes of failures and issues in real time.
2. Fully understanding the potential of data-driven marketing.
3. Generating customer offers based on their buying habits.
4. Improving customer engagement and customer loyalty.
5. Reevaluating risk portfolios quickly.
6. Personalizing the customer experience.
7. Adding value to online and offline customer interactions.
BIG DATA CHALLENGES
1. Dealing with data growth.
2. Generating insights in a timely manner.
3. Recruiting & retaining big data talent.
4. Integrating disparate data sources.
5. Validating data.
6. Securing big data.
7. Organizational resistance.
The 10 Vs of Big Data
BIG DATA APPLICATIONS
-Big Data Applications in Healthcare
-Big Data Applications in Manufacturing
-Big Data Applications in Media & Entertainment
-Big Data Applications in IoT (Internet of Things)
-Big Data Applications in Government
USES OF BIG DATA
I) Understanding & targeting customers.
II) Understanding & optimizing business process.
III) Performance optimization & performance evaluation.
IV) Improving healthcare & public health.
V) Improving sports performance.
VI) Improving science & research.
VII) Optimizing device & machine performance.
VIII) Improving security & law enforcement.
IX) Improving & optimizing cities & countries.
X) Financial trading.
Here is a list of top 10 big data tools that are used by successful analytics developer
• Wolfram Alpha