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Introduction to Business Anlytics and Strategic Landscape

Business analytics uses statistical methods and technologies to analyze historical data and gain new insights to improve strategic decision-making. It refers to skills, technologies, and practices for continuously developing new understandings of business performance based on data analysis. Business analytics is commonly used to analyze various data sources, find patterns within datasets to predict trends and access new consumer insights, monitor key performance indicators in real-time, and support decisions with current information. It provides companies the ability to interpret large volumes of data to make informed decisions supporting organizational growth.

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Business Analytics  Presentation by  Mr. Basavaraj M. Naik M.Com, NET, KSET Full-time Guest Lecturer Department of Studies in Commerce Rani Channamma University Belagavi Post- Graduate Centre, Jamkhandi
Introduction and Strategic Landscape  Business Analytics; Meaning, Definition and its Revolution, Information Technology and Business  Analytics, Need for Business Analytics and Its Strategy, Corporate Analytics Failure, Fact Based Decision  Making, Analytical Based Decision Making, Analytical Resources, Structure of Analytical Practitioners.
Meaning of BA  Business Analytics is the process by which businesses use statistical methods and technologies for analyzing historical data in order to gain new insight and improve strategic decision-making.  Business analytics refers to the skills, technologies, practices for continuous developing new insights and understanding of business performance based on data and statistical methods.
Essentials of business analytics  Business analytics has many use cases, but when it comes to commercial organizations, BA is typically used to:  Analyze data from a variety of sources. This could be anything from cloud applications to marketing automation tools and CRM software.  Use advanced analytics and statistics to find patterns within datasets. These patterns can help you predict trends in the future and access new insights about the consumer and their behavior.  .
 Monitor KPIs (key performance indicator,) and trends as they change in real-time. This makes it easy for businesses to not only have their data in one place but to also come to conclusions quickly and accurately.  Support decisions based on the most current information. With BA providing such a vast amount of data that you can use to back up your decisions, you can be sure that you are fully informed for not one, but several different scenarios.
Information Technology and Business Analytics  Business analytics if put in simple terms, is the process of transforming data into useful information. ... This data is analyzed using information technology, statistical analysis, quantitative methods (numerical analysis of data collected through polls, questionnaires, and surveys, or by manipulating pre-existing statistical data using computational techniques.) and other computer based models.
 Business Analytics and Information Technology (BAIT) focuses on three levels of using information which are becoming more strongly intertwined and are essential components of the modern enterprise:  Information Technology - developing skills to capture, store, organize, and search your data  Data Analysis - discovering and understanding patterns of data  Decision Modeling - using data to make better decisions and formulate complex plans of action
Strategic Use of Analytics in Business: A Key Way to Optimize Business Performance  Business analytics provides companies with the ability to interpret large volumes of data so they can make informed business decisions that support organizational growth.  To sustain growth, organizations seek to hire professionals who have a background in business analytics.
Corporate Analytics Failure  According to the Gartner survey [4], key reasons for project failures were “management resistance and internal politics.”  In other words, many survey respondents (practitioners and leaders of data science and analytics groups in large organizations) seemed to blame their managers for failing to recognize the value of their services. These managers were, presumably, often the same executives who had approved large investments in high-priced analysts and technology.
Reasons Responsible for CAF  Missing Data and Poor Data Quality (Poor Data Handling) : Poor data handling can lead to incorrect and invalid results that can lead to making sub- optimal business decisions.  Duplicate reports and data sets : Many times reports and data sets are copied and create redundancy excess than the actual need) of data and reports.  Too Much Data: Visualizing too much data in tabular format gives summary of data but does not tell the user, how to interpret this data and make informed decisions.
 Not Comparing and Measuring: Missing time- based comparisons to measure and assess business performance can lead to failure as well.  Insufficient Data Quality checks: While transforming large volumes of data, insufficient data accuracy validations and standard transformation may lead to various data issues such as duplicate records, blank value, Inconsistent or incorrect formulas.
 Interpretation of historical data and KPIs to identify trends and patterns. This allows for a big picture look of what happened in the past and what is happening currently using data aggregation and data mining techniques.  Many companies use descriptive analytics for a deeper look into the behavior of customers and how they can target marketing strategies to those customers.
 Uses statistics to forecast and assess future outcomes using statistical models and machine learning techniques. This often takes the results of descriptive analytics to create models that determine the likelihood of specific outcomes. This type is often used by sales and marketing teams to forecast opinions of specific customers based on social media data.
OPTIMISATION AND SIMULATION Optimization offer high quality analytical solutions and powerful tactical and strategic applications. Simulation has the advantages by offers practical scenarios with minimal assumptions and also offer the easy to manage parameter of uncertainty to produces a long term strategy
 Uses past performance data to recommend how to handle similar situations in the future. Not only does this type of business analytics determine outcomes, but it can also recommend the specific actions that need to occur to have the best possible result. This is often achieved using deep learning and complex networks. This type of business analytics is often used to match various options to real-time needs of a consumer.
MS-Excel  Microsoft Excel is a spread sheet application developed by Microsoft for Microsoft Windows. It features calculation, graphing tools, vlookup, pivot tables, and a macro programming language called Visual Basic for applications
SPSS  SPSS is a widely used program for statistical analysis in social science. It is also used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations, data miners.
 What is SAS?  SAS stands for Statistical Analysis Software which is used for Data Analytics. It helps you to use qualitative techniques and processes which allows you to enhance employee productivity and business profits. SAS is pronounced as SaaS.
 In SAS, data is extracted & categorized which helps you to identify and analyze data patterns. It is a software suite which allows you to perform advanced analysis, Business Intelligence, Predictive Analysis, data management to operate effectively in the competitive & changing business conditions.
 What is mean by R?  R is a programming language is widely used by data scientists and major corporations like Google, Airbnb, Facebook etc. for data analysis.  R language offers a wide range of functions for every data manipulation, statistical model, or chart which is needed by the data analyst. R offers inbuilt mechanisms for organizing data, running calculations on the given information and creating graphical representations of that data sets.
Optimization  Business analytics really help with the optimization component. Businesses can really make great use of analytics and optimize their operations. They can competitively price their products when there is supposed to be a peak or shortage. Businesses can also create sales, offers, and discounts based on business analytics.  Data Visualisation  Data visualisation is one of the most effective ways of presenting data, and business analytics are quite helpful. This visual form of data helps companies make reports and sets new goals. The visual format is a lot more easy to explore, model and analyse. 
 Fact-based decision making is a systematic process that emphasizes collection of right data, ensure quality of data, collaboratively deliberate the pros and cons of possible decisions and choose business decisions that are supported by the analysis results rather than guesswork, thumb rule.
Analytical Based Decision Making  ability to collect and analyze information, problem-solve, and make decisions.  You use analytical skills when detecting patterns, brainstorming, observing, interpreting data, and making decisions based on the multiple factors and options available to you.
Structure of Analytical Practitioners 1. Define your data vision and strategy
 2. Structure your advanced analytics organization  The second and perhaps most important component to building an advanced analytics team is the integration of the team within your company. To maximize the potential of your data science and analytics investments you need to design a team structure that supports your data.
3. Define skills and team roles  Requisite data, technology and advanced analytics skills needed to execute to your existing internal roles and skills. Then decide which skills you can develop internally and which one you must hire for externally.  To do this management needs to understand what skills are in advanced analytics and data science.
4. Recruit and assess skills  The key component to becoming a successful data driven organization is not data, but people.  Hiring the best and right resources is where value from data is derived for your organization. 5. Develop skills Accept that hiring ready-made Data Scientists is really a fading and increasingly expensive option, so you’ll have to start developing your team in-house and developing a portfolio of skills among many people.
6. Retain your analytics talent  The good news is that research shows that at a certain point, as analytics professionals mature in their role and life stage, they’ll be looking for a better work-life balance and a stable career.
 Strategic Landscape: critical uncertainties that will change the future business environment in which the industry operates, such as disruptive ( interruption ) technologies, societal shifts, demographic changes, economics, etc.