Data Warehouse and Big Data Analytic
Why Data Warehouse is Useful?
There are hundreds of reasons why a data warehouse is useful to your organization, it is suggested that the following list be a good starting point:
- Real-time issues — current systems are not enabled to integrate disparate sources of data and keep historical records of those integrations, in near real-time.
- Scalability issues — tons of historical data needs to be gathered in to an easily accessible place, common formats, common keys, and common access methods. It is needed to ensure that the system is scalable over the next 3 to 5 years.
- Self-Service BI — users can “visualize” and construct their own reports, along with it’s highly integrated historical facts from all the different sources in organization.
- Data Mining is becoming (or already is) the heart-and-soul of better decision making in BI. The mining engine is only as smart as the domain of information that is provided for it, along with the model that is designed. Statistics say: available projection (with some accuracy) for 1/2 as much time as provided history. The same goes for Data Mining initiatives, and the better interconnected the data set is, the better Data Mining confidence ratings will be.
Importance of Big Data Analytics
Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers.
- Cost reduction. Big data technologies and cloud-based analytics bring significant cost advantages when it comes to storing large amounts of data — plus they can identify more efficient ways of doing business.
- Faster, better decision making. With the speed and in-memory analytics, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately — and make decisions based on what they have learned.
- New products and services. With the ability to gauge customer needs and satisfaction through analytics comes the power to give customers what they want. With big data analytics, more companies are creating new products to meet customers’ needs.
Benefits of Big Data Analytics
Big Data Analytics enables enterprises to analyze their data in full context quickly, and some offer real-time analysis. With high-performance data mining, predictive analytics, text mining, forecasting, and optimization, enterprises that utilize Big Data Analytics are able to drive innovation and make the best business decisions. Companies that take advantage of all that Big Data Analytics solutions have to offer are better positioned to optimize machine learning and address their Big Data needs in groundbreaking ways.
Specifically, Big Data Analytics enables enterprises to narrow their Big Data to the most relevant information and analyze it to inform critical business decisions. This proactive approach to business is transformative because it gives analysts and decision makers the power to move ahead with the best knowledge and insights available, often in real time. This means that companies can improve their customer retention, develop better products, and gain a competitive advantage by taking rapid action to respond to market changes, indications of critical customer shifts, and other metrics that impact business. Enterprises utilizing Big Data Analytics with fidelity also have the ability to boost sales and marketing results, discover new revenue opportunities, improve customer service, optimize operational efficiency, reduce risk, and drive other business results.
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Driven by specialized analytics systems and software, big data analytics can point the way to various business benefits, including new revenue opportunities, more effective marketing, better customer service, improved operational efficiency and competitive advantages over rivals.
Big data analytics applications enable data scientists, predictive modelers, statisticians and other analytics professionals to analyze growing volumes of structured transaction data, plus other forms of data that are often left untapped by conventional business intelligence (BI) and analytics programs. That encompasses a mix of semi-structured and unstructured data — for example, internet clickstream data, web server logs, social media content, text from customer emails and survey responses, mobile-phone call-detail records and machine data captured by sensors connected to the internet of things.
On a broad scale, data analytics technologies and techniques provide a means of analyzing data sets and drawing conclusions about them to help organizations make informed business decisions. BI queries answer basic questions about business operations and performance. Big data analytics is a form of advanced analytics, which involves complex applications with elements such as predictive models, statistical algorithms and what-if analyses powered by high-performance analytics systems.
Ahmad Fawwaz Zuhdi
18215036
Information Systems and Technology
School of Electrical Engineering and Informatics
Institut Teknologi Bandung
References:
[1] http://danlinstedt.com/allposts/datavaultcat/why-when-data-warehousing-is-it-relevant/
[2] https://www.sas.com/en_id/insights/analytics/big-data-analytics.html
[3] https://www.ngdata.com/what-is-big-data-analytics/
[4] https://searchbusinessanalytics.techtarget.com/definition/big-data-analytics
