Data Analytics Unit-1 PDF

Data analytics Introduction :Data: Data is a set of values of qualitative or quantitative variables. It is information in raw or unorganized form. It may be a fact, figure, characters, symbols etc. Data can be numbers, like the record of daily weather, or daily sales. Data can be alphanumeric...

Data analytics Introduction :Data: Data is a set of values of qualitative or quantitative variables. It is information in raw or unorganized form. It may be a fact, figure, characters, symbols etc. Data can be numbers, like the record of daily weather, or daily sales. Data can be alphanumeric, such as the names of employees and customers. Information- Meaningful or organized data is information, comes from analyzing data. · Data base: A database is a modeled collection of data that is accessible in many ways. A data model can be designed to integrate the operational data of the organization. The data model abstracts the key entities involved in an action and their relationships. Most databases today follow the relational data model and its variants. · Take the example of a sales organization. A data model for managing customer orders will involve data about customers, orders, products, and their interrelationships. The relationship between the customers and orders would be such that one customer can place many orders, but one order will be placed by one and only one customer. It is called a one-to-many relationship. The relationship between orders and products is a little more complex. One order may contain many products. And one product may be contained in many different orders. This is called a many-to-many relationship. Different types of relationships can be modeled in a database. Data Warehouse: · A data warehouse is an organized store of data from all over the organization, specially designed to help make management decisions. Data can be extracted from operational database to answer a particular set of queries. This data, combined with other data, can be rolled up to a consistent granularity and uploaded to a separate data store called the data warehouse. Therefore, the data warehouse is a simpler version of the operational data base, with the purpose of addressing reporting and decision-making needs only. Data Mining : · Data Mining is the art and science of discovering useful innovative patterns from data. There is a wide variety of patterns that can be found in the data. Evolution of Data Analytics Why Data Analytics? · Organizations today handle and store billions of rows of data, possibly with millions of combinations. Data Analytics has been hailed as the ‗Game Changer‘, because businesses could transform the raw data into something actionable, which improved their profits. One of the first applications of analytics were found in the field of marketing, sales and customer relationship management. · Once the firms had analyzed the data, they found plethora of information ranging from insights into the customer‘s needs to consumer behavior to understanding the demand for products/ services. Evolution of Analytics: 1. Analytics era 1.0: · The first era is also known as the era of ‗Business Intelligence‘. Analytics 1.0 was a time of real progress in gaining an objective, deep understanding of important business phenomena and giving managers the fact-based comprehension to go beyond intuition when making decisions. · For the first time, data about production processes, sales, customer interactions, and more were recorded, aggregated, and analyzed. Data sets were small enough in volume and static enough in velocity to be segregated in warehouses for analysis. · However, readying a data set for inclusion in a warehouse was difficult. Analysts spent much of their time preparing data for analysis. · Analytics era 2.0 : Also known as the era of ‗Big Data‘. The analytics 1.0 era lasted until the mid- 2000‘s and as analytics entered the 2.0 phase, the need for powerful new tools and the opportunity to profit by providing them quickly became apparent. Companies rushed to build new capabilities and acquire new customers. · Example: LinkedIn, created numerous data products, including People You May Know, Jobs You May Be Interested In, Groups You May Like, Companies You May Want to Follow, Network Updates, and Skills and Expertise and to do so, it built a strong infrastructure and hired smart, productive data scientists. · Innovative technologiesof many kinds had to be created, acquired, and mastered in this era. · Big data could not fit or be analyzed fast enough on a single server, so it was processed with Hadoop, an open source software framework for fast batch data processing across parallel servers. · To deal with relatively unstructured data, companies turned to a new class of databases known as NoSQL. · Much information was stored and analyzed in public or private cloud- computing environments. · Machine-learning methods (semi-automated model development and testing) were used to rapidly generate models from the fast-moving data. · The competencies/ skills thus required for Analytics 2.0 were quite different from those needed for 1.0. · The next-generation quantitative analysts were called data scientists, and they possessed both computational and analytical skills. Analytics era 3.0: · Like the first two eras of analytics, this one brings new challenges and opportunities, both for the companies that want to compete on analytics and for the vendors that supply the data and tools with which to do so. · High-performing companies will embed analytics directly into decision and operational processes, and take advantage of machine-learning and other technologiesto generate insights in the millions per second rather than an ―insight a week or month.‖ · Data architectures (i.e., Hadoop) will augment the traditional approaches removing scale barriers. Analytics truly becomes the competitive differentiator for enterprises who capitalize on the possibilities of this new era (International institute for analytics, 2015). · The pictorial representation of the evolution of Data Analytics: · The pictorial representation of the evolution of Data Analytics shows that the concept of Data Analytics started in the early 1980s. · In 1980‘s the Data Analytics is used in such a way that only reporting is used to happen. · That means what is happening with the data being obtained. · After this type of Data Analytic modeling, the Data Analytic is being moved into the second phase that is with early 1990‘s more of Analysis (Analytics) came into existence. · In this period, it focuses on ―why did it happen‖ to the data. · Then in 2000 onwards, the Monitoring of data happens. The dashboards and the scoreboards are being used for the same. · With this type of analysis, a clear idea of what‘s happening to the data is being understood. · Then after 2010 onwards, the Prediction with the data and the data inputs being implemented with. · That means, what will happen with the data is the main question being asked in the period after 2010. · The different methods of statistics, data mining and the optimization is being used in this period. · Now we are in the era with the more detailed data analytics and that is of nature Prescriptive. · In this period we are training our machines to be smarter and focusing on the computations to happen with less time and less efforts. · So we can conclude that we are in the period with more of AI. Data analytics What is Analytics? · Analytics is the use of tools and processes to combine and examine sets of data to identify patterns, relationships and trends. · The goal of analytics is to answer specific questions, discover new insights, and help organizations make better, data-driven decisions. Data analytics: Data analytics is the knowledge of investigating raw data with the intention of deriving solution for a specified problem analysis. · Nowadays analytics has been used by many corporate, industries and institutions for making exact decision at various levels. · The mechanism of drawing solutions during analysis of large datasets with the intention of determining hidden patterns and its relationship. · Analytics differs from mining with the mechanism of determining the new patterns, scope, techniques and its purpose. Definition of Data Analytics · Data Analytics is the process of exploring and analyzing large datasets to find hidden patterns, unseen trends, discover Correlations and valuable insights. · Data is collected and organized, then analysis is performed, and insights are generated as follows: · Data = a collection of facts. Analytics = organizing and examining data. Insights = discovering patterns in data. Data insights that: · Optimize processes to improve performance. · Uncover new markets, products or services to add new sources of revenue. · Better balance risk vs. reward to reduce loss. · Deepen the understanding of customers to increase loyalty and lifetime value. Campaign Optimization Example · A marketing team can collect data of different email campaigns and use data analytics to gain insights on which one resonates best with their customers. The marketing dashboard below provides an in-depth view of the conversion funnel for email campaigns. · The data insight in this case is that the ―Bend the Trend‖ campaign has the highest enrollment rate, which is the primary key performance indicator for this team. Why Data Analytics? · Competitive advantage. · Removes inefficiency in the system/ organization. · Provides ability to make better decisions. Ex: Problems faced by Flip kart · Forecast demand for each SKU (Stock keeping unit) · SKU forecasting predicts the demand for specific products in a company's inventory. The process analyzes data, such as past sales and consumer trends, to help businesses predict future product demand and keep optimum amounts of stock on hand without overpaying for storage space. · Predict customer cancellations and returns. · Predict customer contacts at the customer service. · Predict what a customer is likely to purchase in future? · How to optimize the delivery system? Primary Focus Areas for Analytics Understanding Customer Behavior : · Understanding customer behavior has always been a challenge for organizations. · Customer behavior analytics is the process of collecting, analyzing, and interpreting data about customers' interactions with a company to understand and predict their behavior. · The information from this process is used to improve the entire customer journey, increase sales, and optimize marketing efforts. · Understanding customer behavior is crucial for businesses to make informed decisions(can help you have confidence that you're choosing the right option.), improve customer satisfaction, and increase revenue. Understanding product usage: · Product usage is the data that represents how and when your customers are using your product. Product usage data is a crucial resource for any business, as it can help you understand your consumer better. These insights can help you make better business decisions and optimize your marketing campaigns. Increases operational efficiency: · Data analytics is a powerful tool for improving operational efficiency. By identifying inefficiencies, improving forecasting, optimizing resource allocation, and providing real-time monitoring, businesses can improve their processes and reduce costs. Business Model Innovation : · By using artificial intelligence and data analytics tools, organizations can predict customer needs, identify market trends, and create innovative strategies for success. THE APPLICATIONS OF DATA ANALYTICS  The major industries that are implementing advanced analytical technologiesinclude –  Business analytics  Retail  Healthcare  Media and Entertainment  Banking  Transportation Business Intelligence (BI): · Data analytics helps organizations make data-driven decisions by analyzing historical and current data. It involves creating reports, dashboards, and visualizations to monitor key performance indicators (KPIs) and gain insights into business operations. · BI systems gather data from various sources, transform it into meaningful insights, and present it in the form of reports, dashboards, and visualizations. The primary goal of BI is to provide historical and current data that aids in making informed business decisions. Retail: · Retailers use data analytics to understand their customer needs and buying habits to predict trends, recommend new products and boost their business. Marketing Analytics: · Marketers use data analytics to understand customer behavior, segment customers, and optimize marketing campaigns. This includes analyzing website traffic, social media engagement, email marketing performance, and more. Financial Analytics: · In finance, data analytics is used for risk assessment, fraud detection, portfolio management, and algorithmic trading. It helps financial institutions make informed decisions and manage their investments effectively. Healthcare : · Health care industries analyse patient data to provide lifesaving diagnoses and treatment options. They also deal with healthcare plans, insurance information to derive key insights. · Data analytics can improve patient care by analyzing electronic health records (EHRs), predicting disease outbreaks, identifying trends in patient outcomes, and optimizing hospital operations. Manufacturing: · Using data analytics, manufacturing sectors can discover new cost saving and revenue opportunities. They can solve complex supply chain issues, labour constraints and equipment breakdowns. Banking: · Banking institutions gather and access large volumes of data to derive analytical insights and make sound financial decisions. They find out probable loan defaulters, customer churn out rate and detect frauds in transactions. Transportation and Logistics : · Logistics Companies use data analytics to develop new business models, optimize routes, improve productivity and order processing Capabilities as well as performance management. Data analytics plays a crucial role in optimizing routes, managing transportation fleets, and reducing fuel consumption in the transportation industry. Manufacturing and Quality Control: · Analytics is used to monitor manufacturing processes, identify defects, and improve product quality. Predictive maintenance is also common in this industry. Sports Analytics: · Sports teams and organizations use analytics to make decisions about player performance, game strategies, and fan engagement. This includes player statistics analysis, injury prediction, and game simulations. · Environmental Analysis: Data analytics can help monitor and analyze environmental data, such as air and water quality, climate change, and wildlife conservation efforts. · Government and Public Policy: Government agencies use data analytics to make informed policy decisions, detect fraud and waste, and optimize public services. · Education: Educational institutions use analytics to track student performance, personalize learning experiences, and improve educational outcomes. · Social Media and Sentiment Analysis: Social media platforms use data analytics to understand user sentiment, trends, and engagement. Businesses use this information for brand monitoring and reputation management. Steps involved In Data analytics 1. understand the problem · understand the business problem. Define the organizational goals and plan for a lucrative solution. 2. Data collection: · Gather the right data from various sources and other information based on your priorities. · Data analytics begins with the collection of data from various sources, including databases, websites, sensors, and more. Data can be structured (e.g., databases, spreadsheets) or unstructured (e.g., text, images, social media posts). 3. Data Cleaning: · Clean the data to remove unwanted, redundant and missing values and make it ready for analysis. 4. Data exploration and analysis: · use data visualization and business intelligence tools, data mining techniques and predictive modeling to analyses data. 5. Interpret the Results: Interpret the results to find out hidden patterns, future trends, and gain insights. Importance of Data Analytics life cycle · Data analytics life cycle defines the roadmap of how the data is generated, collected, processed, used, and analyzed to achieve business goals. · It offers a systematic way to manage data for converting it into information that can be used to fulfill organization and project goals. · The process provides the direction and methods to extract information from the data and proceed in the right direction to accomplish business goals. · Based on the newly received insights, they can decide whether to proceed with their existing Research or scrap it and redo the Complete analysis. · The data Analytics life cycle guides them throughout this process. Importance of Data Analytics life cycle Data Analytics Lifecycle : The Data analytic lifecycle is designed for Big Data problems and data science projects. Phase 1: Discovery – · The data science team learn and investigate the problem. · Develop context and understanding. · Come to know about data sources needed and available for the project. · The team formulates initial hypothesis that can be later tested with data. Phase 2: Data Preparation – · Steps to explore, preprocess, and condition data prior to modeling and analysis. · It requires the presence of an analytic sandbox, the team execute, load, and transform, to get data into the sandbox. · Data preparation tasks are likely to be performed multiple times and not in predefined order. · Several tools commonly used for this phase are – Hadoop, Alpine Miner, Open Refine, etc. Phase 3: Model Planning –Team explores data to learn about relationships between variables and subsequently, selects key variables and the most suitable models. · In this phase, data science team develop data sets for training, testing, and production purposes. · Team builds and executes models based on the work done in the model planning phase. · Several tools commonly used for this phase are – Matlab, STASTICA. Phase 4: Model Building –Team develops datasets for testing, training, and production purposes. · Team also considers whether its existing tools will suffice for running the models or if they need more robust environment for executing models. · Free or open-source tools – Rand PL/R, Octave, WEKA. · Commercial tools – Matlab , STASTICA. Phase 5: Communication Results – · After executing model team need to compare outcomes of modeling to criteria established for success and failure. · Team considers how best to articulate findings and outcomes to various team members and stakeholders, taking into account warning, assumptions. · Team should identify key findings, quantify business value, and develop narrative to summarize and convey findings to stakeholders. Phase 6: Operationalize – · The team communicates benefits of project more broadly and sets up pilot project to deploy work in controlled way before broadening the work to full enterprise of users. · This approach enables team to learn about performance and related constraints of the model in production environment on small scale , and make adjustments before full deployment. · The team delivers final reports, briefings, codes. · Free or open source tools – Octave, WEKA, SQL, MADlib. TYPES OF DATA ANALYTICS Analytics can be classified into four levels which help the organizations to become mature in terms of analytical proficiency. 1. Descriptive Analytics ―what happened‖ 2. Diagnostic Analytics ―Why did this happen‖ 3. Predictive Analytics ―what might happen in the future‖ 4. Prescriptive Analytics ―what should we do next‖ 1. Descriptive Analytics · Descriptive Analytics : This is the simplest form of analytics, It summarizes an organization's existing data to understand what has happened in the past or is happening currently. It emphasizes "what is going on in the business‖. · Descriptive analytics determines historical data to understand the relationship between past events and the present conditions of the organization. · It is one of the most widely used analytical tools favored by marketing, finance, sales, and operations teams, as it efficiently looks into past data and provides an analysis of the changes by comparing patterns and trends. · Descriptive analytics answers the question, ―What happened? In the past‖. · It summarizes current business status in the way of narrative and innovative visualization. · Data visualization is a natural fit for communicating descriptive analysis because charts, graphs, and maps can show trends in data—as well as dips and spikes—in a clear, easily understandable way. · It highlights past trends that lead to valuable insights for business, but we do not emphasize here "why these trends happened". · We use Descriptive Analytics when we want to summarize the story of an organization's performance (mostly in the form of Dashboards). · It provides us with a comprehensive view by joining different things together to highlight hidden trends and insights. · Information extracted from descriptive analytics helps leadership to take actions to make things better, and now with the help of Big Data technologies, management sees the real–time progress of various vital business metrics. Management sees a complete picture by benchmarking company performance against the past few years and key competitors. · Below are a few examples of knowledge extracted from descriptive analytics : · More cars come for servicing during monsoon due to water problems so garage should think about hiring part–time mechanics during monsoon to cater to the temporary demand. · Men convert credit card transactions into EMI more than women; banks should target men for EMI promotion as they are more likely to opt for the promotional campaign. · Internet routers show lots of information packets drop during 4–6 PM due to high congestion, support team to provide extra bandwidth during this time slot for seamless customer experience.  · The health department observes a recurring hike in malaria disease in a particular locality every year during the rainy season; they find water bodies are open in that area which is causing mosquito breeding. For example, in an online learning course with a discussion board, descriptive analytics could determine how many students participated in the discussion, or how many times a particular student posted in the discussion forum. Essential Tools used in Descriptive Analytics : · Statistical Summary : It provides statistical descriptions for a given business metric, e.g. Mean, Median, Standard Deviation, Percentile, Interquartile range, etc. · Z–Score : Z Score tells us how far (in terms of standard deviation) is a particular value of x from its mean. · Coefficient of Variance : It is a ratio where we divide standard deviation with mean. · Interquartile Range : It is an important measure to gauge the variation in the dataset. 2. Diagnostic Analytics · Diagnostic analytics addresses the next logical question, ―Why did this happen?‖ · Diagnostic analytics provides "Why did it happen in my business". · It is a bit advanced where analysts examine data in order to find reasons for business problems or opportunities. · Ex: In a time series data of sales, diagnostic analytics would help you understand why the sales have decreased or increased for a specific year or so. · Eg: Reduction in production because of drop in quality. Below are a few examples : · A company found that employees are not completing learning certifications, analyst diagnosed that most of the employees are stuck at programming assignments, where programming interface was not supportive/ flexible, and there was no way to get hints/ help to proceed further. · There was a low hotel check–in feedback score; analysts diagnosed that front office executive enters customer details which are not required fields during check–in itself. Typing speed and system navigation is also very slow which is resulting in a longer check– in time. · The product return rate was very high during last month, and it found that out of total return items more than 60% of products were supplied by two vendors only, where the vendor provided the wrong specification about products. Essential tools used in Descriptive Analytics : · Correlation Analysis : It is a statistical measure that indicates the strength of the relationship between two variables. · 5 Why Analysis : It is a very structured approach where we try to dig into a problem and peel it layer by layer to reach the root cause of the problem. · Cause and Effect Analysis : Here, we identify all possible reasons for one problem then we pick up all the reasons as a problem one by one and try to find other causes for that problem. 3. Predictive Analytics · Predictive analytics is used to make predictions about future trends or events and answers the question, ―What might happen in the future?‖. · Predictive analytics is the heart of business analytics, it aims to help the organization by predicting probabilities of occurrence of a future event or future values of any essential business metrics. · Once organizations have a stable setup for descriptive analytics, Predictive analytics combines this historical data with advanced business protocols (policy and rules) to forecast future values of business events. · Predictive analytics allows organizations to become forward–looking, providing an appetite to consume calculated risk by anticipating customer behavior and business outcomes. · Ex: sales in the next month/ quarter, employee attrition, and product return rate, etc. Below are a few examples : · Netflix predicts the next movie customers want to watch, more than 80% of customers select their next movie from their recommendation list. In this way, Netflix earns more rental income from regular customers by suggesting them the next film or programs. · Airline companies predict competitive airfares to extraordinary and ordinary days also they indicate how much airfare should be increased as per the increased customer's traffic on their websites. · IRCTC predict the probability to confirm the seat which provides assurance to the customer about their seat confirmation, it helps to attract more customers to their portal. · Taxi services predict the demand during different time slots and change their tariff accordingly. Important Tools used in Predictive Analytics : · Regression Analysis : It establishes the mathematical relationship between input variables and output variables, which means if we can calculate the future value of output for any given input, e.g. sales forecast for next month. · Logistic Regression : It is a classification predictive analytics technique that can predict the output class for any given set of inputs. E.g. by providing customer demographics logistic regression can indicate whether the customer will default bank loan in the future or not. · Decision Tree : Most of the time, we use a decision tree as a classification technique; it tells us the output probability of the output variable for various permutations of our input variables. Although it can be used for continuous output variables also · Clustering Techniques : These techniques segregate our customers into a few logical segments so that we can create tailored offers for a different type of customers as per their needs and interests. · Random Forest : It is another very famous business analytics technique that uses a collaborative approach to solve the problem by generating a large number of predictive models. Their accuracy is generally better  4. Prescriptive Analytics · Finally, prescriptive analytics answers the question, ―What should we do next?‖ · Prescriptive analytics solves the complex business problem as it is the most advanced form of analytics, where we have to choose the most optimal way to increase important business metrics. · perspective analytics can be applied once we have sound business knowledge from descriptive and predictive analytics. · Descriptive and predictive analytics suggest to us various ways to improve business performance while prescriptive analytics tells us the pros and cons of all alternatives and try to provide the optimal outputs by keeping minimum risk in execution. · Prescriptive analytics is not limited to predict "what will happen" and "when will it happen" but it also tries to reveal "why it will happen" and "what would be the impact on the business" Below are Examples of Prescriptive Analytics : · In 2019, there was a prediction of the cyclone on coastal areas of Gujarat (by predicting changing airspeed, varying wind direction, and mathematical relationship between low pressure in the ocean with changes in cyclone intensity) therefore Government and disaster management team had taken proactive actions in shifting citizens from coastal areas to save places, and they stopped fishermen from going to sea and arrange comfortable camps. While in a similar situation in 1999 we lost approx. 10,000 lives due to cyclone. · Banks use prescriptive analytics to identify investment options for their customers to maximize their returns and minimize risk. They balance customer's portfolio by having an optimized ratio of equity, debt, and other types of funds. · At the time of launching a new service or a product into the market, organizations have to keep various factors into the mind like the cost of the product, features of the product, geographies in which they will launch first, customer segments whom they want to attract, marketing channels for product promotion, etc. By getting analytical results from descriptive and predictive analytics, analysts apply prescriptive analytics to decide the right mix of all these factors to make a product launch successful. · In agriculture crop yield depends on various factors like rainfall, soil type, demand in the market, etc. Analysts apply prescriptive analytics and suggest the best kind of crop in different regions as per the rainfall and demand forecast in that season. Important Tools used in Prescriptive Analytics : · Linear Programming : In linear programming, we optimize the objective functions like revenue, market share, customer feedback ratings by also keeping constraints in the model like budget, no. of people deployed, etc. as linear functions. · Analytical Hierarchy Process : We apply these techniques in scenarios where we have to identify the best solution among various available options, and there is the list of criteria's to select the solution, e.g. select best cloud service providers among top 5 organizations by keeping multiple factors into consideration like budget, customer service, flexibility to upgrade, backup services, maintenance cost, etc. · Combinational Optimization : It involves identifying optimal solutions from a considerable number of finite solutions, e.g. the travelling salesman problem, vehicle routing problem, etc. WHO NEEDS DATA ANALYTICS? · Any business professional who makes decisions needs foundational data analytics knowledge. Professionals who can benefit from data analytics skills include: · Marketers, who utilize customer data, industry trends, and performance data from past campaigns to plan marketing strategies · Product managers, who analyze market, industry, and user data to improve their companies‘ products · Finance professionals , who use historical performance data and industry trends to forecast their companies‘ financial trajectories · Human resources and diversity, equity, and inclusion professionals, who gain insights into employees‘ opinions, motivations, and behaviors and pair it with industry trend data to make meaningful changes within their organizations. Importance Of Data Analytics · Data is an unorganized and raw collection of facts that has massive importance for a company. · In the modern world, every company wants to collect and analyze data to know their past mistakes. · It might help them to build a better future. Sometimes these companies find it challenging to use analytics tools. · The demand for data analysts and their related roles comes into the picture. You might understand that industries require data analytics skills. · Data Analytics always helps companies to get an insight into how to develop the business. · There are several types of tools you will require to interpret the data. · Companies use data analytics tools to understand customer behavior and increase productivity. · It might help them to store information about the latest trends in the market. · The company uses tools related to business intelligence and data management to identify the changing functions. · The main three things will give good insight, immediate action, and information system. A good insight will help you to understand the business context. · The information will help to access the organization‘s storage and information system. · You will be able to take immediate action based on valuable information. · The companies are trends to focus on experiments with analytical languages and tools to develop new ideas. Benefits of Data Analytics : Improved Decision Making : · When big data joins forces with artificial intelligence, machine learning, and data mining, companies are better equipped to make accurate predictions. · For example, predictive analytics can suggest what could happen in response to changes to the business, and prescriptive analytics can indicate how the company should react to these changes. · Additionally, enterprises can use data analytics tools to determine the success of changes and visualize the results, so decision-makers know whether to roll the changes out across the business. Increased Efficiency and Productivity : · Data analytics enables organizations to increase efficiency and productivity by automating and streamlining processes, maximizing resource allocation, and minimizing manual labor. · Additionally, data analytics assists businesses in identifying areas where productivity can be increased, such as waste reduction, better inventory control, and supply chain optimization. More effective marketing : · By using data analytics, companies can pinpoint precisely what customers are looking for. · Data enables businesses to do in-depth analyses of client trends, which companies can then utilize to develop successful, focused, and targeted marketing. Enhanced Customer Experience : · By giving organizations useful insights into customer behavior, preferences, and needs, data analytics enables businesses to identify areas where they can improve their customer experience–such as lowering wait times, enhancing customer service, or streamlining user interfaces. Improved Risk Management : · Data analytics can, for instance, assist companies in identifying potential fraud, online threats, or operational risks. Businesses can also take preventative action to mitigate potential risks by monitoring data in real-time. By utilizing data analytics to enhance risk management, they can lessen the possibility of monetary losses, reputational damage, and other negative outcomes. Competitive Advantage : · Analyzing data from various sources allows businesses to understand market trends, consumer behavior, and competitor activities. Businesses can use this information to improve their strategies, spot new opportunities, and set themselves apart from the competition. · Data analytics can, for instance, aid companies in identifying underserved market segments, anticipating client needs, and enhancing product offerings. Simply put, businesses can increase their market share, spur revenue growth, and fortify their brand by utilizing data analytics to gain a competitive advantage. · Data analytics is a potent tool that can assist companies in enhancing their operations and achieving better business results. Different Applications of Data Analytics in Business · Business Analytics provides an in-depth knowledge of the organization‘s data. This in turn helps in understanding the present circumstances as well as in predicting future events and trends. 1. FINANCE ◦ Business Analytics assists financial managers in managing their finances optimally and then taking relevant measures. Implementing business analytics in various sectors of finance(such as investment banking and budgeting) can prove to be highly fruitful for the finance industry. ◦ It helps in building future strategies for a new product by observing similar products and methodologies. ◦ In addition to this, business analytics can also be used to predict future loan defaulters. 2. HUMAN RESOURCES MANAGEMENT (HRM) ◦ Human Resource Management is the process or practice of managing, hiring, organizing, training, and directing people in an organization in a strategic manner. Human Resources (or HR) professionals use business analytics in several ways. ◦ It helps them in analyzing large amounts of data to understand employees‘ needs and grievances and therefore assist them accordingly. ◦ Business analytics can be used by HR in determining the right candidates, the expected salaries as well as the trending retention rates in the industries. ◦ Moreover, HR professionals can leverage business analytics to forecast the trajectory of the organization and thus efficiently design appropriate training and development programs for trainees or employees. 3. PRODUCTION AND INVENTORY MANAGEMENT · Management is a key element in every organization. It aims to enhance the profits and productivity of an organization all the while trying to reduce overall costs. · Business Analytics serves as a great tool for management and manufacturing. It is involved in every phase of product development. It supports analyzing the inventory measures and designing business solutions that are most suitable for products. · It can help determine the costs and gauge the expected sales of products. This way the organizations can adapt to the latest styles and opportunities in the industry. 4. CUSTOMER RELATIONSHIP MANAGEMENT (CRM) ◦ Customer Relationship Management or CRM is the process of building and managing the organization‘s relationships as well as interactions with customers. ◦ Business analytics can be used in customer relationship management to understand the customer base better and therefore, implement corresponding strategies. This helps significantly drive sales and amplifies the organization‘s profits. ◦ Customers‘ purchasing patterns, needs, buying behaviors, issues, feedback, and all the other indicators can be obtained and analyzed through business analytics methodologies. These indicators can then be used to foster long-lasting and loyal relationships between clients and the organization. WHAT IS DATA ANALYTICS IN BUSINESS? · Data analytics is the practice of examining data to answer questions, identify trends, and extract insights. · When data analytics is used in business, it‘s often called business analytics. · You can use tools, frameworks, and software to analyze data, such as Microsoft Excel and Power BI, Google Charts, Data Wrapper, Infogram, Tableau, and Zoho Analytics. · These can help you examine data from different angles and create visualizations that illuminate the story you‘re trying to tell. Importance Of Business Analytics: · Business analytics is a methodology or tool to make a sound commercial decision. Hence it impacts functioning of the whole organization. Therefore, business analytics can help improve profitability of the business, increase market share and revenue and provide better return to a shareholder. · Business analytics combines available data with various well thought models to improve business decisions. · Converts available data into valuable information. · This information can be presented in any required format, comfortable to the decision maker. · For starters, business analytics is the tool your company needs to make accurate decisions. · These decisions are likely to impact your entire organization as they help you to improve profitability, increase market share, and provide a greater return to potential shareholders. Essentially, the four main ways business analytics is important : · Improves performance by giving your business a clear picture of what is and isn‘t working. · Provides faster and more accurate decisions. · Minimizes risks as it helps a business make the right choices regarding consumer behaviour, trends, and performance. · Inspires change and innovation by answering questions about the consumer.  Benefits of Business Analytics · Apart from having applications in various arenas, following are the benefits of Business Analytics and its impact on business – · Accurately transferring information · Consequent improvement in efficiency · Help portray Future Challenges · Make Strategic decisions · As a perfect blend of data science and analytics · Reduction in Costs · Improved Decisions · Share information with a larger audience · Ease in Sharing information with stakeholders  Business analytics: · Business analytics is a set of statistical and operations research techniques, artificial intelligence, information technology and management strategies used for framing a business problem, collecting data, and analyzing the data to create value to organizations. · Business Analytics can be broken into 3 components: 1. Business Context 2. Technology 3. Data Science Business Context : · Business analytics projects start with the business context and ability of the organization to ask the right questions. · Another good example of business context driving analytics is the ‗did you forget feature‘ used by the Indian online grocery store bigbasket.com (Abraham et al., 2016). Many customers have the tendency to forget items they intended to buy. The customers may buy the forgotten items from a nearby store where they live, resulting in reduction in basket size in the future for online grocery stores such as bigbasket.com. · Alternatively, the customer may place another order for forgotten items, but this time, the size of the basket is likely to be small and results in unnecessary logistics cost. Thus, the ability to predict the items that a customer may have forgotten to order can have a significant impact on the profits of online grocers such as bigbasket.com. · Another problem that online grocery customers face while ordering the items is the time taken to place an order. Unlike customers of Amazon or Flipkart, online grocery customers order several items each time; the number of items in an order may cross 100. Searching for all the items that a customer would like to order is a time- consuming exercise, especially when they order using smart phones. Thus, big basket created a ‗smart basket‘ which is a basket consisting of items that a customer is likely to buy (recommended basket) reducing the time required to place the order. · The above examples( ‗did you forget‘ and smart basket feature at bigbasket.com) manifest the importance of business context in business analytics, that is, the ability to ask the right questions is an important success criteria for analytics projects. Technology: · To find out whether a customer has forgotten to place an order for an item, we need data. In both the cases, the point of sale data has to be captured consisting of past purchases made by the customer. Information Technology (IT) is used for data capture, data storage, data preparation, data analysis, and data share. Today most data are unstructured data; data that is not in the form of a matrix (rows and columns) is called unstructured data. Images, texts, voice, video, click stream are few examples of unstructured data. To analyse data, one may need to use software such as R, Python, SAS, SPSS, Tableau, etc. for example, in the case of Target, technology can be used to personalize coupons that can be sent to individual customers. Data Science : · Data Science is the most important component of analytics, it consists of statistical and operations research techniques, machine learning and deep learning algorithms. · There are several techniques available for solving classification problems such as logistic regression, classification trees, random forest, adaptive boosting, neural networks, and so on. The objective of the data science component is to identify the technique that is best based on a measure of accuracy. Web Analytics? What is Web Analytics? · Web analytics is the gathering, synthesizing, and analysis of website data with the goal of improving the website user experience. · Web Analytics is the methodological study of online/ offline patterns and trends. It is a technique that you can employ to collect, measure, report, and analyze your website data. It is normally carried out to analyze the performance of a website and optimize its web usage. · We use web analytics to track key metrics and analyze visitors‘ activity and traffic flow. · It is a tactical approach to collect data and generate reports. · Web analytics enables a business to retain customers, attract more visitors and increase the dollar volume each customer spends. Analytics can help in the following ways: · Determine the likelihood that a given customer will repurchase a product after purchasing it in the past. · Personalize the site to customers who visit it repeatedly. · Monitor the amount of money individual customers or specific groups of customers spend. · Observe the geographic regions from which the most and the least customers visit the site and purchase specific products. · Predict which products customers are most and least likely to buy in the future. Web analytics process: · Web Analytics is an ongoing process that helps in attracting more traffic to a site and thereby, increasing the Return on Investment. The web analytics process involves the following steps: 1. Setting goals: · The first step in the web analytics process is for businesses to determine goals and the end results they are trying to achieve. These goals can include increased sales, customer satisfaction and brand awareness. 2. Collecting data: · The second step in web analytics is the collection and storage of data. Businesses can collect data directly from a website or web analytics tool, such as Google Analytics. The data mainly comes from Hypertext Transfer Protocol requests. For example, a user's Internet Protocol address is typically associated with many factors, including geographic location and click through rates. 3. Processing data: · The next stage of the web analytics funnel involves businesses processing the collected data into actionable information. 4. Identifying key performance indicators ( KPIs): · In web analytics, a KPI is a quantifiable measure to monitor and analyze user behavior on a website. Examples user sessions and on-site search queries. 5. Developing a strategy : · This stage involves implementing insights to formulate strategies that align with an organization's goals. For example, search queries conducted on-site can help an organization develop a content strategy based on what users are searching for on its website. 6. Experimenting and testing: · Businesses need to experiment with different strategies in order to find the one that yields the best results. · For example, A/ Btesting is a simple strategy to help learn how an audience responds to different content. The process involves creating two or more versions of content and then displaying it to different audience segments to reveal which version of the content performs better. Off-site web analytics · The term off-site webanalytics refers to the practice of monitoring visitor activity outside of an organization's website to measure potential audience. Off-site web analytics provides an industry wide analysis that gives insight into how a business is performing in comparison to competitors. On-site web analytics · On-site webanalytics refers to a narrower focus that uses analytics to track the activity of visitors to a specific site to see how the site is performing. The data gathered is usually more relevant to a site's owner and can include details on site engagement, such as what content is most popular. Two technologicalapproaches to on-site web analytics include log file analysis and page tagging. Text analytics · Text Analytics is the process of converting unstructured text data into meaningful data for analysis, to measure customer opinions, product reviews, feedback, to provide search facility, sentimental analysis and entity modeling to support fact based decision making. · Text analytics is the quantitative data that you can obtain by analyzing patterns in multiple samples of text. It is presented in charts, tables, or graphs. · Text analytics helps you determine if there‘s a particular trend or pattern from the results of analyzing thousands of pieces of feedback. Meanwhile, you can use text analysis to determine whether a customer‘s feedback is positive or negative · Text Analytics determines key words, topics, category, semantics, tags from the millions of text data available in an organization in different files and formats. · The term Text Analytics is roughly synonymous with text mining. · Text analytics software solutions provide tools, servers, analytic algorithm based applications, data mining and extraction tools for converting unstructured data in to meaningful data for analysis. · The outputs, which are extracted entities, facts, relationships are generally stored in a relational, XML, and other data warehousing applications for analysis by other tools such as business intelligence tools or big data analytics or predictive analytics tools. Text analytics in business : · Every business strives to provide the best to their customers. To achieve this, they are depending on text analytics to study and understand patterns, drifts in behavior through the positive and negative feedback provided, buying trends, opinions of consumers, blogs etc. · And modify the approachability to satisfy needs which can make a greater impact on business. · By implementing text-based analytics, a business can bridge the gap to unlock the very needs and demands of the customers. · Text analytics focuses on quantitative insights that give the essence of ‗why‘ a particular problem arises and ‗what‘ the reasons are and upon understanding, ‗how‘ can a business overcome it in the most effective way. · Various tools like HANA, Python, R, Microsoft excel etc can be used to achieve important tasks of Text analytics as discussed below. Important Tasks in Text Analytics: · Information Extraction: It involves extracting the relevant information from large volumes of textual data. It centres on extracting attributes and entities. This information can be used for further analysis. · Information Retrieval: Information Retrieval (IR) alludes to extricating relevant and related examples dependent on a particular arrangement of words or expressions. In this content mining strategy, IR frameworks utilize various calculations to track and screen client practices and find applicable information as needs are. Google and Yahoo web indexes are the two most famous IR frameworks. · Clustering: It looks to recognize characteristic constructions in text based data and sort them into relevant subgroups or 'bunches' for additional examination. A critical test in the grouping interaction is to frame significant groups from the unlabelled text-based information without having any earlier data on them. · Summarization: This content mining strategy helps to create a summary of a large volume of text in a way that the meaning and intent of the original document is preserved. · Categorization : This technique is used to classify text (review, paragraph, document) into a relevant category. The text could be the reviews provided by different users for a product and the reviews could be classified as positive or negative. Similarly, a mail can be classified into a spam or non spam email.  Skills for Business Analytics · Business analytics refers to the process of extracting insights from data to make informed decisions regarding a business question or challenge. · Here are five skills you can develop to improve your understanding of business analytics. 1. Data Literacy · One of the fundamental skills to build before diving into business analytics is data literacy. At its most basic, data literacy means you‘re familiar with the language of data, including different types, sources, and analytical tools and techniques. · Being data literate also means you‘re comfortable working with data in various ways—from evaluating it to manipulating it and gaining insights. 2. Data Collection · The first step in leveraging analytics to drive business decisions is to collect a data sample from which conclusions can be drawn. · In some cases, a dataset already exists, and it‘s up to the business analyst to pull relevant information. For example, if you‘re interested in discovering a retail store‘s most profitable products, you might start by pulling historical sales data for transactions that took place over a specific period. 3. Statistical Analysis · Several statistical methods can be helpful when it comes to analysis, including: · Hypothesis testing , which is a statistical means of testing an assumption. · Linear regression analysis, which can be used to evaluate the relationship between two variables. · Multiple regression analysis, which is used to evaluate the relationship between three or more variables. · Through these forms of analysis, you can draw insights and conclusions that answer your business question. 4. Communication · While insights derived from reliable data are key to making informed business decisions, it‘s likely that other stakeholders need to be involved in the decision-making process. For this reason, effectively communicating your findings is essential. Without strong communication skills, the value of your analyses can go unrealized. 5. Data Visualization · Data visualization goes hand in hand with strong communication, as it allows you to present findings in an easily digestible format for those who may not be as data literate as you are.