Skip to main content

What is Big Data Analytics-Introduction


Big Data Analytics-Introduction
What is Big data Analytics?
Big data refer to a term that describes the volume of data like data processing application software which has both structured and unstructured to deal with challenges like capture, search, storage, analysis, data curation, sharing, visualization, transfer, querying, updating and information privacy that inundates a business on a day-to-day basis. It is not important the amount of data, but it what companies do with these data. It can be analyzed for insisted that is going to lead better decision and strategy.
The main components of big data as follows.

  • Techniques for analyzing data, such as  machine learning, A/B testing, and natural language processing
  • Big data technologies, like databases, business intelligence, and cloud computing.
  • Visualization, such as graphs, charts, and any displays of the data.
Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate. Big data examination is the way toward looking at extensive and changed detail indexes like Big data  to reveal concealed examples, obscure relationships, showcase patterns, client inclinations and other valuable data that can help associations settle on more-educated business choices.

For more information on Analytics and Big Data, please visit http://olgaazina.blogspot.com/2017/04/status-and-future-of-big-data-part-3.html

Comments

Post a Comment

Popular posts from this blog

What is Big Data Analytics -Conclusion

Big Data Analytics -Conclusion Big Data improves transportation and energy consumption in the city, making our favorite websites and social networks more effective and even preventing suicide. Companies collect more data than they know what to do. Big Data is everywhere; The volume of data produced, stored and mined is incredible. Today, companies use data collection and analysis to develop more coherent business strategies. Factory use the data obtained from the use of the actual products to improve and develop new products and create innovative offers after-sales services. This will continue to be a new area for all industries. Data has become a competitive advantage and a necessary part of product development. Businesses succeed in great time data not only because they have more or better data but because they have good teams that set clear goals and define success that seems to ask the right questions. Big Data creates new growth opportunities and new business cate...

Significant travel and leisure company uses analytics to drive customer acquisition

​  Leisure company uses analytics to drive customer acquisition Today’s technology allows businesses to collect ever-growing piles of customer and prospect transaction, demographic, and preference data. But more information does not mean more insights. Analytics is a powerful tool that aims to help organizations to find   opportunities to improve marketing, sales, and customer service . I read about topic in business analytics is that a great travel and entertainment company were searching for ways to raise its acquisition of co-branded credit card customers and the old strategy of daily email was leading to low yields and high opt-outs. They used an analytics techniques to identify the most likely customers to accept deals, using a mix of both internal and external data. Also, the company created machine learning algorithms to predict deals and offers acceptance rates by segment, going on to prioritize media usage, frequency, messages,   and timing. The aim wh...

Success in Data Monetization strategy for a Business.  

Data Monetization strategy in Business   D ata Monetization is a way of process of actively generating value from a company’s data inventory. Data monetization strategy actively looks to extract potential value through three essential factors: Aggregating and Analyzing                                          Organization look to drive incremental revenue by aggregating multiple data sources and conducting deep analyses through data science. Therefor, The resulting models are then used to drive changes in the decision-making process for operational, sales, and marketing. While the Ownership of value is retained and protected, but the cost of value generation is the highest of the three models. Crowdsourced Data Insights Crowdsourced data insig...