08 Innovation through Analytics.pdf

Data vs. Information Data is raw, unorganized facts that need to be processed Data can be something simple and seemingly random and useless until it is organized When data is processed, organized, structured or presented in a given context...

Data vs. Information Data is raw, unorganized facts that need to be processed Data can be something simple and seemingly random and useless until it is organized When data is processed, organized, structured or presented in a given context so as to make it useful, it is called information CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 1  Data Analytics To benefit from the data collected, every aspect of data needs to be analyzed – The data being generated from large-scale enterprises – The data generated from an individual Data analytics: The science of analyzing raw data in order to make conclusions about that information, to enhance productivity and business gain CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 2  Data Analytics Extracted data is cleaned and categorized to analyze different behavioral patterns. The techniques and the tools used vary according to the organization or individual. Many of the techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 3  Needs for Data Analytics Four main factors which signify the need for Data Analytics: 1. Gather Hidden Insights – Hidden insights from data are gathered and then analyzed with respect to business requirements 2. Generate Reports – Reports are generated from the data and are passed on to the respective teams and individuals to deal with further actions for a high rise in business CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 4  Needs for Data Analytics 3. Perform Market Analysis – Market Analysis can be performed to understand the strengths and the weaknesses of competitors. 4. Improve Business Requirement – Analysis of data allows improving business to customer requirements and experience. CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 5  Tools in Data Analytics Many tools have emerged with various functionalities: – R programming – This tool is the leading analytics tool used for statistics and data modeling. – Python –It provides various machine learning and visualization libraries such as Scikit-learn, TensorFlow, Matplotlib, Pandas, Keras etc. – Tableau Public – This is a free software that connects to any data source such as Excel, Corporate Data Warehouse etc. It then creates visualizations, maps, dashboards etc with real-time updates on the web. CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 6  Tools in Data Analytics – QlikView – This tool offers in-memory data processing with the results delivered to the end-users quickly. It also offers data association and data visualization with data being compressed to almost 10% of its original size. – SAS – A programming language and environment for data manipulation and analytics, this tool is easily accessible and can analyze data from different sources. – OpenRefine/GoogleRefine – A data cleaning software that will clean up data for analysis. It is used for cleaning messy data, the transformation of data and parsing data from websites. CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 7  Tools in Data Analytics – Microsoft Excel – This tool is one of the most widely used tools for data analytics. Mostly used for clients’ internal data, this tool analyzes the tasks that summarize the data with a preview of pivot tables. – RapidMiner – A powerful, integrated platform that can integrate with any data source types such as Access, Excel, Microsoft SQL, Tera data, Oracle, Sybase etc. This tool is mostly used for predictive analytics, such as data mining, text analytics, machine learning. CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 8  Tools in Data Analytics – Konstanz Information Miner (KNIME) – Open-source data analytics platform, which allows you to analyze and model data. It provides a platform for reporting and integration through its modular data pipeline concept. – Apache Spark – One of the largest large-scale data processing engine. It executes applications in Hadoop clusters 100 times faster in memory and 10 times faster on disk. Popular for data pipelines and machine learning model development CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 9  Data Analyst Data Analyst is a professional who can analyze data by applying various tool and techniques and gathering the required insights Data analysts translate numbers into plain English CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 10  Data Analyst Skills required: – Statistic – Data collection – Data cleaning – Data analysis – Hidden insights discovery – Data visualization – Reports generating – Machine learning CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 11  Big Data Analytics Big Data: A concept in Software Engineering which we use when we have a large sets of machine generated data, which in most of the cases is unstructured and not easy to use with traditional RDBMS concepts. Data Analytics: More of analyzing data which could be structured or unstructured. Big Data Analytics: Complex process of examining large and varied data sets, to uncover information that can help organizations make informed business decisions. CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 12  Big Data Analytics Four types of Big Data Analytics: – Descriptive: Allows organizations to learn from past behaviors, and help them in understanding how they might influence future outcomes: What has happened? – Diagnostic: Often referred to as root cause analysis. It provides deeper analysis to answer the question: Why did this happen? CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 13  Uses of Big Data Analytics 1. Using Big Data Analytics to Boost Customer Acquisition and Retention – In the year 2015, Coca-Cola managed to strengthen its data strategy by building a digital-led loyalty program. – Coca-Cola director of data strategy was interviewed by ADMA managing editor. The interview made it clear that big data analytics is strongly behind customer retention at Coca-Cola https://www.adma.com.au/resources/how-coca-cola-uses- data-to-supercharge-its-superbrand-status CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 14  Uses of Big Data Analytics 2. Use of Big Data Analytics to Solve Advertisers Problem and Offer Marketing Insights – Netflix uses big data analytics for targeted advertising – Netflix sends subscriber suggestions of the next movie to watch based the past search and watch data. This data is used to give them insights on what interests the subscriber most. CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 15  Uses of Big Data Analytics 3. Big Data Analytics for Risk Management – UOB bank tested a risk management system that is based on big data. – The big data risk management system enables the bank to reduce the calculation time of the value at risk. It would initially take about 18 hours With the risk management system that uses big data, it only takes a few minutes. – Through this initiative, the bank will possibly be able to carry out real- time risk analysis in the near future CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 16  Uses of Big Data Analytics 4. Big Data Analytics As a Driver of Innovations and Product Development – Amazon Fresh and Whole Foods – The data-driven logistics gives Amazon the required expertise to enable creation and achievement of greater value. – Amazon whole foods is able to understand how customers buy groceries and how suppliers interact with the grocer. CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 17  Uses of Big Data Analytics 5. Use of Big Data in Supply Chain Management – PepsiCo relies on huge volumes of data for an efficient supply chain management. – The company is committed to ensuring they replenish the retailers’ shelves with appropriate volumes and types of products. The company’s clients provide reports that include their warehouse inventory and the POS inventory to the company This data is used to reconcile and forecast the production and shipment needs. CT109-3-1 Digital Thinking and Innovation Innovation through Analytics SLIDE 18