ACTIVITY 1: The PEST and SWOT Analysis. ( attached here the project charter) Need the PEST and Swot analysis.

1. Following the instructions given in this tutorial, conduct a PEST analysis for your project and then conduct a SWOT analysis using the information gathered in the PEST.
2. Highlight key areas of concern or areas that require action. These become the focus for risk management planning.

Then rank them in your personal priority order.

Project Charter.

1. Introduction/Background

Analytical solutions are at the heart of TCNC's (Technology Company) mission. In order to stay one step ahead of the competition, we can automate the data collection process and give predictive analytics.

1. Business Objective:

Ad hoc analysis, data visualization, and prediction algorithms are all part of TCNC's long-term strategy. By highlighting any weaknesses in an organization's cash flow, this project aims to improve the company's business intelligence and find ways to save money. Product demand and project completion timeframes can also be forecasted using predictive algorithms. We want to be able to offer results in near-real-time, therefore we've automated the data collection process.

1. Current Situation and Problem/Opportunity Statement

In order to construct machine algorithms, TCNC uses a variety of tools, including reporting and visualization tools, to make data easier to understand and generate predictions. We're trying to streamline the entire event reporting procedure in order to save time and resources. In addition, we will be able to better track and assess the status of other development projects as a result of this undertaking. Predictability, on the other hand, will increase reliability and allow for a more consistent organization to fulfill demand.

1. Critical Assumptions and Constraints

In the long run, the planned project will pay for itself and enhance the way decisions are made. They'll have a senior project manager overseeing the team of data scientists. Technical assistance is required from the outset, and it will be produced using existing technologies inside our company.

1. Analysis of Options and Recommendations

There are a few ways to take advantage of this chance.

1. One of the advantages of using our own software is that we may continue to develop the project over time.

2. We have the option of purchasing newer, faster software that has already been built.

Option 1 is preferable to us since it allows us to better leverage project expenditures as it develops over time. In addition, we believe that creating our own software will enable us to better meet our requirements while also reducing our expenses.

1. Preliminary Project Requirements:

1. Among its most important aspects are the following.
2. 1. Possession of all necessary tools for the completion of this task. Python, SQL, and Tableau software are just a few examples of these tools.
3. 2. Gathering requirements for proper data management and control.
4. The collection of all mathematical equations in use in order to automate their implementation.

Collecting user-requested content.

1. Budget Estimate and Financial Analysis

The total cost of this project is estimated at $500,000. This estimate is based on a 40-hour work week and new employees who must be hired. The project's expected completion date is two years from today's date. In general, software engineers and data scientists may expect to make between $60, 000 and $70, 000 a year. As a result, it may be necessary to bring in other experts.

It is anticipated that long-term gains will surpass expenses by ensuring that the organization remains competitive in all markets in which it now operates. By automating all of our data needs, we may anticipate to save $600,000 per year in the long run. $682,000 with a ROI of 101 percent is the projected net present value.

1. Schedule Estimate

In the next six months, we'd like to know which features have been decided and how they're progressing. Progress updates will be made every six months on this project's expected completion date of 1-2 years.

1. Potential Risks:

1. The following are some of the things we need to keep in mind.
2. 1. The data science team may not have enough experience to provide a good product.
3. In order to develop a useful product, user feedback is essential.
4. Creating software can take much longer than intended.

1. Exhibits

year	0	1	2	3		Total
costs	500200	250200	250200	250200
discount factor	1	0.97	0.86	0.79
Discounted Costs	500200	242700	215200	197700		1155800

Project Title: Data Automation and Predictability

Date of Authorization: Jan 1-2022

Project Start date: Jan 1-2022

Project Finished Date: Dec 1-2022

Key Schedule Milestones:

• We've set up the databases we'll need.
• Predictability models working prototype.

Budget Information:

To get this project up and running, TCNC has given $2 million in funding. At crucial milestone dates, more financing will be available.

Project Manager:

Johan Smith, , johansmit

Project Objectives:

Ad hoc analysis, data visualization, and prediction algorithms are all part of TCNC's long-term strategy. By highlighting any weaknesses in an organization's cash flow, this project aims to improve the company's business intelligence and find ways to save money. Product demand and project completion timeframes can also be forecasted using predictive algorithms. We want to be able to offer results in near-real time, therefore we've automated the data collection process.

Main Project Success Criteria:

In order to build the databases and data visualization models we'll need, we'll identify the essential tools and model types.

Approach:

• Make a list of tools and processes.
• Make a list of important people and organizations, including small and medium-sized businesses (SMEs).
• There are model databases out there.
• Identify the most important models for predicting outcomes..

Need only pest and swot analysis. Thanks