Introducing the first part of my research about Big Data Analytics. Please feel free to provide me a feedback on both content and English grammar (since English is not my native language). I would really appreciate it!
What is Business Analytics?
Business Analytics (BA) is not a new phenomenon. It has been around for many years, but predominantly with companies operating in the technically oriented environment. Only recently it’s making its breakthrough and we can see more and more companies, especially in the financial and the telecom sector, deal with business analytics in order to support business processes and improve performance. So what does business analytics refers to? 
Business Analytics is translating data into information that is necessary for business owners to make informed decisions and investments. It is the difference between running business on a hunch or intuition versus looking at collected data and predictive analysis. It is a way of organizing and converting data into information to help answer questions about the business. It leads to better decision making by looking for patterns and trends in the data and by being able to forecast impact of decisions before they are taken.
BA can serve throughout the whole company and all C-level executives can take an advantage of it. For example Chief Marketing Officers (CMOs) can use BA to get better customer insight and enhance customer loyalty. Chief Financial Officers (CFOs) can better manage financial performance and use financial forecasts. Chief Risk Officers (CROs) can get a holistic view of risk, fraud and compliance information across the organization and take an action. Chief Operating Officers (COOs) can get better insight into supply chains and operations and enhance efficiency. 
Companies use business analytics to data-driven decision making. For being successful they need to treat their data as a corporate asset and leverage it for competitive advantage. Successful business analytics depends on data quality, skilled analysts who understand the technologies and the business and organizational commitment to data-driven decision making.
Examples of BA uses include: 
- Exploring data to find new relationships and patterns (data mining)
- Explaining why a certain result occurred (statistical analysis, quantitative analysis)
- Experimenting to test previous decisions (A/B testing, multivariate testing)
- Forecasting future results (predictive modeling, predictive analytics)
Why Is Business Analytics important?
Becoming an analytics-driven organization helps companies to extract insights from their enterprise data and help them to achieve costs reduction, revenues increase and competitiveness improvement. This is why business analytics is one of the top priorities for CIOs. An IBM study  shows that CFOs in organizations that make extensive use of analytics report growth in revenues of 36 percent or more, a 15 percent greater return on invested capital and twice the rate of growth in EBITDA (earnings before interest, taxes, depreciation and amortization).
Business Analytics helps you make better, faster decisions and automate processes. It helps you address the questions and ensure you to stay one step ahead your competition. Some of the basic questions in retail environment could be:
- How big should a store be?
- What market segments should be targeted?
- How should a certain market segment be targeted in terms of products, styles, price points, store environment, location?
- Who are our customers?
- How should space in the store be allocated to the various product groups and price points for maximum profitability?
- What mitigation strategies are effective and cost efficient – for example changes to packaging, fixtures, placement of product?
- What is the best customer loyalty program for our customers?
- What is the optimal staffing level on the sales floor?
- How many checkouts are optimal in a store?
- Would acquisition of a particular store brand improve profitability?
- Would creation of a new store brand improve profitability?
As Claire Cameron  says “Whenever a decision is being made whether that involves products or services, distribution, resource allocation, new markets, human capital, financing, or any other aspect of business, whenever future strategies are being explored, whenever there is a need to predict or optimize, whenever there is a story to be teased out, then Business Analytics can, and some would say should, play a role.”
Business Analytics versus Business Intelligence
Business Analytics and Business Intelligence (BI) are two terms heavily used when people are talking about data and what it can do for their company. Some people use these terms interchangeably, some strictly distinguish between them. Some (IBM , SAP ) consider business intelligence as a subset of BA, others   think the opposite.
There are many different opinions. For example, research Defining Business Analytics and Its Impact on Organizational Decision-Making  conducted by Computerworld says “Business intelligence (BI) is an important aspect of an organization’s strategic framework. But what is beyond BI? Some indicators point to business analytics, a progression from BI, as the next step. Business analytics is predictive as well as historical, which requires a cultural shift to the acceptance of a proactive, fact-based decision-making environment, providing organizations with new insights and better answers faster… Through this research, we can see that IT and business professionals mainly align business analytics with BI products. In fact, more than half of respondents (54%) cited BI as the category of products that first comes to mind when they think of the term “business analytics”.”
Traditional BI has been associated with providing executive dashboards and reporting to monitor the assumptions and key performance metrics saying how are we doing. The opinion that BA has a broader character and offers deeper insight is also supported in the book Competing on analytics: the new science of winning  where Thomas Davenport and Jeanne Harris say that Business Analytics can answer questions like why is this happening, what if these trends continue, what will happen (prediction) and what is the best that can happen (optimization).
In this paper, I will consider BA as an umbrella term trying to answer BI questions like What happened? When? Who? How may? as well as questions Why did it happen? Will it happen again? What will happen if we change x? What else does the data tell us that never thought to ask? that need advanced analytics. 
How does Business Analytics work?
A high-level architecture of business analytics (see Figure 1) starts with data sources representing many business systems such as point of sale system, accounting system, order processing system, online system, and so on. These systems produce data we need for analytics. Since the data is often stored in different formats, in different locations and in many cases it cannot be accessed on real-time basis, we need to set up a single repository that is able to store all this data. This repository is called data warehouse.
The Extract, Transform, and Load (ETL)  job extracts data from one or more sources on a scheduled basis, performs any required data cleansing to transform the data into a consistent format, and loads the cleansed data into the data warehouse or data mart. Data warehouse is a pool of historical data that doesn’t participate in the daily operations of the organization. Instead, this data is purposefully used for business analytics. The data in data mart usually applies to a specific area of the organization.
When we collect all necessary data, analysis is typically performed. Analytics tools range from spreadsheets with statistical functions to complex data mining and predictive modeling applications. As patterns and relationships in the data are uncovered, new questions are asked and the analytic process iterates until the business goal is met. 
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