Question

Identify and briefly discuss what big data analyses (e.g., Tableau) would provide insights into the following Company concerns given the public health crisis.

a. Projecting revenue in the next year based on the multi-stage reopening plans of retail outlets selling the Company's products. b. Estimating production costs based on supply chain disruptions and governments' social distancing mandates.

c. Controlling selling and general expenses based on evolving regulations requiring contract tracing of office and line workers.

Answer 1

What is big data analytics:

Big data analytics describes the process of uncovering trends, patterns, and correlations in large amounts of raw data to help make data-informed decisions.

These processes use familiar statistical analysis techniques—like clustering and regression—and apply them to more extensive datasets with the help of newer tools.

With the explosion of data, early innovation projects like Hadoop, Spark, and NoSQL databases were created for the storage and processing of big data.

This field continues to evolve as data engineers look for ways to integrate the vast amounts of complex information created by sensors, networks, transactions, smart devices, web usage, and more.

Even now, big data analytics methods are being used with emerging technologies, like machine learning, to discover and scale more complex insights.

How big data analytics works:

1. Collect data: Data collection looks different for every organization. With today’s technology, organizations can gather both structured and unstructured data from a variety of sources — from cloud storage to mobile applications to in-store IoT sensors and beyond. Some data will be stored in data warehouses where business intelligence tools and solutions can access it easily.
   
2. Process data: Once data is collected and stored, it must be organized properly to get accurate results on analytical queries, especially when it’s large and unstructured. Available data is growing exponentially, making data processing a challenge for organizations.
   Stream processing looks at small batches of data at once, shortening the delay time between collection and analysis for quicker decision-making. Stream processing is more complex and often more expensive.
   
3. Clean data: Data big or small requires scrubbing to improve data quality and get stronger results; all data must be formatted correctly, and any duplicative or irrelevant data must be eliminated or accounted for. Dirty data can obscure and mislead, creating flawed insights.
   
4. Analyse data: Getting big data into a usable state takes time. Once it’s ready, advanced analytics processes can turn big data into big insights. Some of these big data analysis methods include:
   a) Data Mining
   b) Predictive analysis
   c) Deep learning

Big data tools and technology :

• Hadoop
• NoSQL databases
• MapReduce
• YARN (Yet Another Resource Negotiator)
• Spark
• Tableau

The Big benefits of big data analytics :

• Cost savings
• Product development
• Market Insights.

The big challenges of big data:

• Making data accessible
• Maintaining quality data
• Keeping data secure
• Finding the right tools and platforms.
  

a) Forecasting business revenue and expenses during the startup stage is really more art than science. Many entrepreneurs complain that building forecasts with any degree of accuracy takes a lot of time--time that could be spent selling rather than planning. But few investors will put money in your business if you're unable to provide a set of thoughtful forecasts. More important, proper financial forecasts will help you develop operational and staffing plans that will help make your business a success.

Here's some detail on how to go about building financial forecasts when you're just getting your business off the ground and don't have the luxury of experience.

1. Start with expenses, not revenues.
2. Forecast revenues using both a conservative case and an aggressive case.
3. Check the key ratios to make sure your projections are sound.