ANALYTICS SOLUTIONS
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This document discusses how analytics and data science can help businesses make data-driven decisions to power growth. It explains that while data science deals with identifying important questions, analytics focuses on answering those questions. The document then outlines three levels of analytics maturity - descriptive, predictive, and prescriptive analytics - and provides examples of solutions and tools used at each level to analyze past data, predict future trends, and prescribe optimal outcomes. Businesses can work with Grazitti to advance through these maturity levels and drive growth with data-driven insights.
ANALYTICS SOLUTIONS
- 1. ANALYTICS SOLUTIONS Power Business Growth With Data-Driven Decision-Making
- 2. Q. Analytics & data science…is there a difference? ● Yes. Although we use the terms interchangeably, there is a difference. While data science deals with questions that should be asked, analytics focuses more on answering these questions. Q. So, how does data science help me? ● With data science, you can solve the most challenging business questions, extract actionable insights from data, and power business growth with data-driven decision-making. Q. Okay, so how do we, at Grazitti, help businesses make data-driven decisions? ● The complexity of the questions that you’re able to answer depends on the level of analytics maturity you’re at. Let’s find out how we help businesses through these levels of maturity to drive growth. Questions You May Have
- 3. ● Descriptive ● Predictive ● Prescriptive Level of Analytics Maturity
- 4. ● Understand past and current events better by describing or summarizing existing data. ● Solutions: ▪ Data Mining ▪ ETL ▪ Data Warehousing ▪ Data Visualization Descriptive Analytics
- 5. ● Transform raw data into actionable information. ● Example: Exploring large databases and improving market segmentation to launch highly- personalized campaigns. Data Mining
- 6. ● Extract, Transform, and Load the data you require, from multiple sources. ETL
- 7. ● Build a central repository of integrated data ● Example: Building a data warehouse with customer information from a company’s point-of- sale systems, website, mailing lists, and comment cards Data Warehousing
- 8. ● View important data in a visually appealing format. Data Visualization
- 10. ● Predict possible outcomes and discover trends. ● Solutions: ▪ Statistical Analysis ▪ Extrapolation Forecasting ▪ Predictive Modeling Predictive Analytics
- 11. Statistical Analysis ● Learn what could happen in the future ● Example: Summarizing data, such as by making a pie chart
- 12. Extrapolation Forecasting ● Learn what could happen if current trends continue Image Source: https://xkcd.com/605/
- 13. Predictive Modeling ● Learn what will happen if you were to change factor X, Y, or Z
- 15. ● Prescribe the best achievable outcome ● Solutions: ▪ Optimization Analysis ▪ Machine Learning Prescriptive Analytics
- 16. Optimization Analysis ● Learn more about the best course of action you can take ● Example: Find out how many products of each type you need to manufacture from the number of parts you have, in order to maximize profits and make effective use of your inventory
- 17. Machine Learning ● Learn from this outcome and find out how to address it. Examples: ● Virtual Personal Assistants such as Siri, Alexa, Google Now ● GPS Traffic Predictions ● Video Surveillance
- 19. Thank You To learn more, please visit : https://www.grazitti.com/services/data-science/