Business Analyst Terms + MS Excel + Tableau

• Data Analyst - uses technical skills to generate informative insights which helps in the decision-making processes.
• Key Skills - Data visualization, presentation skills, MS Excel, SQL, R or Python
• use data to find patterns and correlations + build models to see how data responds to his models.
• What is likely to happen in the future? + What do we need to do? 
  
  
• Business Analyst - uses knowledge + experience + insights generated by data analyst => take decisions that affect the business
• Key Skills: - Critical thinking, Problem-solving, Communication and Process improvement, understand organization’s objectives and procedures to analyze performance, identify inefficiencies and propose and implement solutions. 
• They must have at least a working knowledge of the technology involved in analysis.
• presents conclusion of the final analyzed data set.
• What has happened? + What is happening right now?

Advantage of data driven decision

• Risk assessment - Lessen some controllable risks + identify certain risks which can't be identified unless used data insights.
• From current trend can predict outcomes
• Improve both external and internal processes
• External processes - interaction of organization with vendor, customer, distributor
• Internal processes - interaction of organization with employee, internal stakeholders
• Gauge (Measure) customer satisfaction - Data plays a major role in gauging customer satisfaction.
• Record of repeat sales for e-commerce platforms
• For offline retail stores => Same-store sales is metric that companies use to evaluate the total sales in stores that have been operating for a year or more. It provides a performance comparison for the established stores of a retail chain over a given time period. If most of revenue increase comes from opening new stores => indicates that demand for product is flattening out and future revenue growth can be expected to reach a saturation point in terms of total locations.
• Eg.- same-store sales of 7% → total revenue at retail chain's existing locations increased by 7% over the given time period from previous year

Data types

• Structured Data - Easily searchable + Analyze large quantitative problem
• Unstructured Data - Not Easily searchable + Disordered in nature
• It is not like spreadsheet but may have its own internal structure.
• Eg.- Web scraping => visit websites, grab the relevant pages, and extract useful information --> way to get large amounts of data from websites to get sentimental analysis about product 
• Semi-structured Data - Not like spreadsheet but has tags to differentiate items from data
• Eg.- email => where mail is unstructured data but the name, mail address, time stamp are things using which we can sort them

Data Analytics

Extraction of insights from data using statistical techniques and technologies

• Large data (data collection) => Relevant data (data cleaning)
• Descriptive Analytics - What has happened and what is happening right now?
• Historical + Current Data
• We can know what has happened earlier and how the same metrics are performing right now.
• Diagnostic Analytics - Why did this happen?
• Built on Descriptive Analytics to discover reasons for past performance called Root Cause Analysis
• Descriptive Analytics - X% have Disease A
  Diagnostic Analytics - which symptoms point to Disease A
• Predictive Analytics - What is likely to happen in the future?
• Output of Descriptive & Diagnostic Analytics => Statistical modelling & forecasting => Predictions about future outcomes
• Prescriptive Analytics - What do we need to do?
• Testing, Machine Learning, Algorithms, Business rules, etc. => Recommend specific solutions => Delivers desired outcomes
• Eg.- Studying travel Pattern + conditions + consumer Demography => Suggests hotels to Higher Room Tariffs + Additional Services in Off-Season => Greater bookings than earlier strategy

Applications of Data-driven-decision-making in Manufacturing

• Predictive Maintenance - Analyzing the historical performance data of machine to forecast when it will fail => reducing downtime of machine, and identify the root cause of the problem => increases machine life
• To increase each machine's efficiency while operating => increasing yields and throughput and reduce the amount of energy they consume.
• Profit per hour maximization: Check total profitability of integrated supply chain right from raw materials purchasing to final sales => providing intelligence on how best to capitalize on given conditions. It optimizes the interaction of machines and processes to maximize profit generation in production and supply chains.

FMCG - Fast-Moving Consumer Goods (FMCG)/Consumer Packaged Goods (CPG)
Products sold quickly and at a relatively low cost. The FMCG industry is characterized by high-volume sales, quick inventory turnover, and various products catering to consumer needs. FMCGs have short shelf life because of high consumer demand (e.g.- soft drinks)

A/B testing - Experiment that allows you to determine which option performs best and thus is likely to lead to more conversions.

Amazon strategy - Just Walk Out system & cashier-less checkout with Amazon’s Dash Cart

• Upselling - Sales technique where you sell advanced or premium version of product
• Cross-selling - Sales technique to sell complementary, noncompetitive products. 
• Eg.- if selling TV → convincing user to purchase better model than what they are looking at is upselling but suggesting sound setup to go with it is cross-selling

BCG  Matrix

High market growth rate → sectors with high potential for sales and profits due to rapid growth → offer significant potential for business expansion and development through new customer acquisition

• Cash cow - Like dairy cow which produces milk over the course of its life and need little to no maintenance. They are part of mature, slow-growing industries, have a large chunk of the market share and require minimal investment to thrive. eg.- iPhone, intel
• Star has high market share in high growth markets. It requires large capital outlays but can generate significant cash. If a successful strategy is adopted, stars can morph into cash cows. 
• Question marks has low market share in a high-growth industry. They need large amounts of cash to capture more of or sustain their position within the market. Depending on the strategy adopted by the firm, it can land in any of the other quadrants (since its future is unknown => called '?' )
• Dogs have low market shares in low-growth markets. There is no large investment requirement, and they don't generate large cash flows.

Concierge Minimum Viable Product (MVP) - way of testing product idea by manually providing the service. Instead of building a complex and automated product, you offer a personalized and high-touch service to a small group of customers. This allows you to test your product idea & learn from customer feedback to make changes.

• Zappos - Before building an online shoe store, the founder went to local shoe stores, took pictures of shoes, and posted them on a website. When someone ordered a pair, he would buy them from the store and ship them to the customer.
  
• Airbnb - Before creating a platform for renting out spare rooms, the founders offered their own apartment as a bed and breakfast for attendees of a conference in San Francisco. They learned about the demand, the pricing, and the customer experience of their service.

Steps to follow

• Find the root cause => can be external (change in market or taste of buyer, new competitor) or internal (change in material or price)
• Breakdown the problem => in the direction of root cause
• CONCLUSION :
• Price testing must be done to test customers’ views and testing different price points qualitatively (by getting feedback from customers) and quantitatively (using statistical data and demographics).
• Observing and implementing a consumer-centric approach. Treat this as a problem faced by a startup as it is a good way to take off blinders. 
• Use surveys and quantitative research, and the insights from one-to-one observation.
• Use Concierge Minimum Viable Product or A/B testing approach.
• Study the market (tastes and preferences) => Work backwards to develop product
• Make reports, KPI matrix (Key Performance Index) to display and visualize business metrics in a single table.
• Make issue tree like Mutually Exclusive and Completely Exhaustive (MECE )

 MS Excel

1. Pivot Table - Interactive way to summarize large amounts of numerical data quickly in detail. Also used to sort, reorganize, group, count, sum or average of data stored.
2. Slicers - Another way of filtering. It narrows the portion of the dataset connected to the other reports. 
• 3 ways to select items :
• single select
• multi-select
• select all option
• It should be used to filter the unneeded and hide it in the data table. 
• Create more focused reports by using slicers next to important visuals.
3. Pivot Charts - Summarize the selected rows and columns of data into excel charts. It is a visual representation of a pivot table or any other tabular data.
4. Macros - Action or set of actions that you can run as many times as you want.
•  If you have tasks in MS Excel that you do repeatedly => record a Macro to automate those tasks.
• Here, we are recording our mouse clicks and keystrokes.
• After we create a Macro, we can edit it also.
• Programming language used - Visual Basic for Applications (VBA)
5. What-If analysis - Allows us to test out various scenarios and determine a range of possible outcomes. It even enables us to see the effect of making a certain change without changing the actual data. It provides best possible outcome.
• Solver - Type of What-If analysis used when finding the “best” outcome, given a set of more than two assumptions. It is a sophisticated optimization program finding solutions to complex problems that would otherwise require high-level mathematical analysis.

Tableau

- It simplifies raw data in a manner that is simple to comprehend. It helps to create data that is understandable by experts at all levels of company. Even non-technical people can easily develop customized dashboards using Tableau.

1. Tableau Desktop - allows us to code and customizes reports.
2. Tableau Public - Helps to create workbooks that can be saved on Tableau’s public cloud => viewed and accessed by anyone.
3. Tableau Server - used to share workbooks and visualizations created in the Tableau Desktop across business verticals only. 
4. Tableau Online - similar to Tableau Server, but here data is stored on servers hosted in the cloud which are maintained by the Tableau group.
5. Tableau Reader - View workbooks and visualizations that are created using Tableau Desktop or Tableau Public.

Tools -

1. Data Extraction & Blending - Tableau has several data source features that allows you to connect to and import data from a variety of external sources. Like large data, relational databases, on-cloud data, and Excel spreadsheets, can be easily deployed by copying the file from the source and pasting it into your Tableau worksheet.
2. Enhanced data visualization functions - Variety of visualization tools like Motion Chart, Histogram, Choropleth Maps, Stacked Maps, Heat Map, Highlights Table, Bullet Chart
3. Predictive Analysis and Forecast - Tableau also has the option of using time series and forecasting. The drag and drop interface make forecasting effective and productive.