01 Handout 1 Overview of Big Data & Business Analytics PDF

IT2111 OVERVIEW OF BIG DATA AND BUSINESS ANALYTICS Challenges with Business Analytics...

IT2111 OVERVIEW OF BIG DATA AND BUSINESS ANALYTICS Challenges with Business Analytics Penn State University’s John Jordan described the challenges with Introduction to Business Analytics Business Analytics: there is “a greater potential for privacy invasion, greater financial exposure in fast-moving markets, greater potential for In the era of knowledge economy, getting the right information to mistaking noise for true insight, and a greater risk of spending lots of decision makers at the right time is critical to their business success, money and time chasing poorly defined problems or and one such attempt includes the growing use of business analytics opportunities.” Other challenges with developing and implementing (Min, 2017). Business analytics is one of the most talked-about topics Business Analytics include: in the field of business and information technology. And as expected, Executive Ownership – Business Analytics requires buy-in business analytics is becoming one of the most sought-after courses from senior leadership and a clear corporate strategy for in the academe. integrating predictive models IT Involvement – Technology infrastructure and tools must be Business Analytics (BA) able to handle the data and Business Analytics processes Available Production Data vs. Cleansed Modeling Data – Below are some of the definitions of business analytics: Watch for technology infrastructure that restricts available Business analytics is comprised of solutions used to build analysis data for historical modeling, and know the difference between models and simulations to create scenarios, understand realities historical data for model development and real-time data in and predict future states including data mining, predictive production analytics, applied analytics, and statistics Project Management Office (PMO) – The correct project https://www.gartner.com/it-glossary/business-analytics). management structure must be in place in order to implement Business Analytics is the study of data through statistical and predictive models and adopt an agile approach operations analysis, the formation of predictive models, End-user Involvement and Buy-In – End-users should be application of optimization techniques, and the communication of involved in adopting Business Analytics and have a stake in these results to customers, business partners, and college the predictive model executives (Galleto, 2018). Change Management – Organizations should be prepared for Business analytics refers to a broad use of various quantitative the changes that Business Analytics bring to current business techniques such as statistics, data mining, optimization tools, and and technology operations simulation supported by the query and reporting mechanism to Explainability vs. the “Perfect Lift” – Balance building precise assist decision makers in making more informed decisions within statistical models with being able to explain the model and a closed-loop framework seeking continuous process how it will produce results improvement through monitoring and learning (Min, 2017). Business Analytics Best Practices Here are the definitions of Business Intelligence (BI): Listed below are some of the most important best practices for Business Intelligence is the process of collecting information Business Analytics. from all sources to make data-driven decisions in an organization o Know the objective for using Business Analytics. Define your (Galleto, 2018). business use case and the goal ahead of time. Business Intelligence is the process of combining aspects of o Define your criteria for success and failure. reporting, monitoring, and alerting, dashboards, scorecards, and o Select your methodology and be sure you know the data and ad hoc query data exploration (Galleto, 2018). relevant internal and external factors. 01 Handout 1 *Property of STI  [email protected] Page 1 of 4  IT2111 o Validate models using your predefined success and failure then brand-new Ford Model T assembly line to measure criteria. component assembly times. 20th century – Computers played a huge role in the advancement Business Analytics Goals of business analytics with the introduction of Decision Support Gaining insights into business practices and customer behaviors. System in the 1970’s. Data warehouses also become popular Business analytics is designed to transform unstructured, during this time to help organize large amounts of data, the first nonstandardized big data originated from multiple sources into iteration of today’s server farms. meaningful information helpful for a better business decision. 21st century – Most analytics were applied to the upper levels of Improving predictability. By deriving insights into customer enterprises and corporations. Then the Internet, big data, and the behavioral patterns and market trends, business analytics can cloud came along. With the massive amounts of data being improve the organization’s ability to make demand forecast more generated every day, analytics have become accessible and accurately. affordable to businesses of all types and sizes. Business analytics Identifying risk. Risk cannot be managed without identifying it and have become so complex and advanced which it can help to then preparing for it. Business analytics can function as an early predict future trends and behavior in real-time (Hasson, 2015). warning system for detecting the signs or symptoms of potential troubles by dissecting the business patterns (e.g., shrinking Big Data and Business Analytics market share, a higher rate of customer defection, declining stock price). Business analytics focuses on financial and operational analytics of Improving the effectiveness of communication. With the query and the business while big data involved machine automation to analyze reporting mechanism of business analytics, it can only speed up data. The importance of big data is not how much data is available, the reporting procedures, but also provide user-friendly reports but what to do with those data. including “what-if” scenarios. Such reports can be a valuable communication tool among the decision makers and thus would What is Big Data? help the management team make more timely and accurate Big data is a huge volume of structured and unstructured data that business decisions. is generated everyday around the world. This data is so complex Enhancing operating efficiency. By aiding the decision maker in that it becomes almost impossible to analyze them manually or understanding the way business works and where the greatest with the use of basic data management tools and applications. business opportunities are, business analytics can decrease the There are four (4) aspects that define data: volume, variety, chances of making poor investment decisions and misallocating velocity, and veracity. the companies resources and thus would help improve the 1. Volume is about how huge the data sets are. company’s operating efficiency. 2. Variety includes how many pieces of data were gathered together from social media data, government data, financial data, banking data, all sorts of transactions combined History of Business Analytics together to make one or more profiles for the customers. History suggests that the earliest humans would use sticks and 3. Velocity is the speed of data. stones to help predict the sales trend of new inventions. 4. Veracity means that there is a lot of uncertainty, meaning, 1800s – The industrial age brought along business management there is all of these different data coming together but don’t as a scientific discipline. Henry Ford applied this principle to his know what to with them. 01 Handout 1 *Property of STI  [email protected] Page 2 of 4  IT2111 Why is Big Data Important? 3. Access, manage and store big data. Along with reliable Data can be taken from any sources and analyze it to find answers access, companies also need methods for integrating the that enable cost reductions, time reductions, new product data, ensuring data quality, providing data governance and development and optimized offerings, and smart decision making. storage, and preparing the data for analytics. Some data When big data is combined with high-powered analytics, the may be stored on-premises in a traditional data warehouse, following business-related tasks can be accomplished: or in the cloud. o Determining the root cause of failures, issues, and defects in 4. Analyze big data. With high-performance technologies, near-real time organizations can choose to use all their big data for o Generating coupons at the point-of-sale based on the analyses. Another approach is to determine upfront which customer’s buying habits data is relevant before analyzing it. Increasingly, big data o Recalculating entire risk portfolios in minutes feeds today’s advanced analytics endeavors such as o Detecting fraudulent behavior before it affects the artificial intelligence. organization 5. Make intelligent, data-driven decisions. Well-managed, trusted data leads to trusted analytics and trusted decisions. How Does Big Data Work? To stay competitive, businesses need to seize the full value There are five (5) key steps to taking charge of this big “data fabric” of big data and operate in a data-driven way – making that includes traditional, structured data along with unstructured and decisions based on the evidence presented by big data rather semi-structured data. than gut instinct. 1. Set a big data strategy. A big data strategy sets the stage for business success amid an abundance of data. When Providers of Big Data Services developing a strategy, it’s important to consider existing – and future – business and technology goals and initiatives. This calls for treating big data like any other valuable business How to Choose the Best Provider? asset rather than just a byproduct of applications. The market for Big Data and analytics technology is in a state of 2. Know the sources of data. fast change and rapid growth. A recent development is the o Streaming data comes from the Internet of Things emergence of a class of platforms and managed toolsets which (IoT) and other connected devices that flow into IT can be termed Big Data-as-a-Service (BDaaS). systems from wearables, smart cars, medical It’s easy to see the appeal. Instead of building a data center, devices, industrial equipment, and more. developing an analytics toolset stack, and investing in a team of o Social media data stems from interactions on trained data scientists – a costly and time-consuming project for Facebook, YouTube, Instagram, etc. This includes any enterprise. vast amounts of big data in the form of images, Key questions anyone should ask when choosing a particular videos, voice, text, and sound – useful for marketing, BDaaS provider for their business include: sales, and support functions. o Does it offer low or zero start-up costs? Is the solution o Publicly available data comes from massive scalable? Big Data projects tend to grow in size beyond the amounts of open data sources like the US initial vision – can you easily and affordably buy more storage government’s data.gov, the CIA World Factbook, or and processing resources as you need them? the European Union Open Data Portal. o Is it already use the industry? BdaaS is particularly suited to o Other big data may come from data lakes, cloud strategies which involve analysis of very large, messy, data sources, suppliers, and customers. unstructured datasets. Additionally, there will be a 01 Handout 1 *Property of STI  [email protected] Page 3 of 4  IT2111 requirement to move large amounts of data to a third-party provider which is likely to raise security and compliance REFERENCES: issues. 100 Most promising big data solution providers - 2017. o Does it offer real-time analysis and feedback? Today’s most Available from https://talentedge.in/articles/difference-big- exciting and rewarding Big Data projects provide insights data-business-analytics based on what is happening now, not just what was Big data. What it is and why it matters. Available from https://www.sas.com/en_ph/insights/big-data/what-is-big- happening last week, meaning action can be taken when it is data.html needed rather than simply learning from the past. Business analytics. Available from https://www.gartner.com/it- Here's a quick introduction to some of the most prominent BDaaS glossary/business-analytics services available today. Galletto, M. 2018. What is business analytics?. Available from o Google Cloud Dataproc. It runs Hadoop and Spark on https://www.ngdata.com/what-is-business-analytics/ Google’s Cloud Platform and integrates with the BigTable Hasson (2015). The evolution of business analytics. storage and BigQuery analytics frameworks. Available from o Amazon Web Services. AWS is the collective name for https://www.socialmediatoday.com/technology-data/2015- 02-10/evolution-business-analytics-infographic Amazon’s cloud-based business tools and services. Their Min, H. 2017. Introduction to business analytics. Available managed Hadoop service is called Amazon Elastic from MapReduce and it runs on Amazon’s S3 storage http://www.informit.com/articles/article.aspx?p=2494418 infrastructure. What is big data analytics. Available from o Microsoft Azure HDInsight. It has built on its Azure cloud https://www.ibm.com/analytics/hadoop/big-data-analytics. framework by increasingly adding in functionality and compatibility with open source technology such as Spark and Storm. o Salesforce Wave Analytics. Salesforce built up partnerships with companies including Google and Cloudera to bring Hadoop-based Big Data analytics to its cloud data services. Wave Analytics uses a UI that will be familiar to any users of its market-leading CRM software to enable dynamic visualizations. o Qubole Data Service. Qubole was founded by former Facebook data scientists who saw a need for a self-service Big Data platform for enterprise. It is designed to be operated from a UI that assumes no prior experience of using Hadoop. As Qubole is not a cloud storage provider its solution can be configured to run on Amazon, Google, or Microsoft cloud infrastructure. o IBM BigInsights on Cloud. IBM has also forged partnerships with social media companies such as Twitter, making it easier to gain insights, and developed its own cognitive, natural language processing engine, Watson, allowing data to be queried and analyzed using natural human language. 01 Handout 1 *Property of STI  [email protected] Page 4 of 4