Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits.
The primary goal of big data analytics is to help companies make more informed business decisions by enabling data scientists predictive modelers and other analytics professionals to analyze large volumes of transaction data, as well as other forms of data that may be untapped by conventional business intelligence programs. That could include Web server logs and Internet stream data, social media content and social network activity reports, text from customer emails and survey responses, mobile-phone call detail records and machine data captured by sensors connected to the Internet of things.
Big data can be analyzed with the software tools commonly used as part of advanced analytics disciplines such as predictive analytics, Data mining, Text analytics and Statistical Analysis.
Potential pitfalls that can trip up organizations on big data analytics initiatives include a lack of internal analytics skills and the high cost of hiring experienced analytics professionals. The amount of information that's typically involved, and its variety, can also cause data management headaches, including data quality and consistency issues. In addition, integrating Hadoop systems and data warehouses can be a challenge, although various vendors now offer software connectors between Hadoop and relational databases, as well as other data integration tools with big data capabilities.
SAP has whole range of solution for taking care of entire analytics scope.
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How Big Data has evolved? This lecture speaks about the journey Big Data has made over the years.
This lecture give detailed account of Data sources. Very engagaing and interesting lecture. Please watch out.
How Big Data has influenced or changed everyday life of a common man.
Lecture explains a case study related to benefits of big data when it is appled to human beings. How Big data makes one to lead enhanced an happy life.
This lecture give bigger picture of SAP Solutions, tools and other techiques This is the first stepping stone in understanding SAP's various offeringings.
This lecture give bigger picture of SAP platform, tools and other techiques for exploiting hidden value behind Big Data. This is the first stepping stone in understanding SAP's various offeringings.
For any analytical environment collection of data is most important activity. It is required to have effective tools and techniques. This lecture deals with SAP Data acquisition tools.
Very interesting lecture. You will come to know various Big Data applications by SAP currently available in the market. This is explained in a practical way.
SAP is really not just to create a powerful real-time platform, but also to operationalize Big Data solutions. RDS is a solution which combines SAP software, services, and also best practices
It is mandatory to get an overview of various predicative models to strengthen our understanding. This lecture deals precisely with that.
This lecture talks about data flow between in the entire modelling process.
This lecture provided high level steps in the Modeling process.
When evaluating predictive tools, modelers should consider several functional areas to ensure a tool meets their needs. This lecture discusses the same.
Selection of appropriate tool is key for an effective and useful model development. This lecture talks about the same.
EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task. This lecture gives an overview of Exploratory Data Analysis.
Lecture talks about types of Models and process of developing the models.
Every process system need time to time updates and maintenence. This lecture talks about the need of update and how that can be done.
R Language is backbone any preditive models and extensively used in analytics. Let us see an overview of R.
The HANA Predictive Analysis Library (PAL) is a set of predictive algorithms in the HANA Application Function Library (AFL). Lecture gives and overview.
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