You Began Writing Your Business Analytics Implementat 507860

You Began Writing Your Business Analytics Implementation Plan Inmodule

You began writing your business analytics implementation plan in Module 3. In addition, you already have gained information about the various technological solutions discussed in the previous modules. In this assignment, you will now address ways to implement the plan along with any associated costs, as this will complete the proposal for management to make their decision. Description of LASA In this assignment, you will amend your existing business analytics implementation plan developed in Module 3. You will amend the existing proposal to discuss the importance of managing information systems, describe the techniques and tools used to manage the data, and explain how utilizing technology can help the organization.

Scenario You have been hired as a business analyst for a well-known design firm. Currently, they utilize technology for their day-to-day operations but not to analyze data that could help with making business decisions. Your task is to convince management that the usage of business analytics would be a great benefit to the business and it would help the business to make well-informed decisions and thus action plans that would align with the business’s strategic planning. The firm currently has technology in place but does not have any connected systems. The databases are all independent of each other but they do utilize a client/server environment.

The firm currently has one location but is looking to add a second location in another part of the state but is unsure about whether it would be beneficial to the firm. The firm liked your implementation plan but have questions about how they will manage the data and how data-driven decision making can help the organization versus just being an additional expense for the organization (cost of new equipment or resources).

Instructions

Using the Argosy University online library resources and the Internet, research business analytics implementation plans, especially methods of developing a rationale in support of implementation. Select at least 6 scholarly sources for use in this assignment. Amend your existing proposal addressing the importance of Management Information Systems and managing the data for the organization.

Objectives of proposals: Revise the previous proposal based upon the comments from your instructor. Explain the importance of MIS in relation to data-driven decisions. Describe the techniques and tools that can be utilized to manage the data. Include at least 2 effective techniques and 3 effective tools. Explain how the techniques and tools can be utilized to present data to management and other organizational decision makers.

Be sure to include at least 3 innovative examples that follow current best practices for managing data. Explain to management how the data can add value to the business in day-to-day operations as well as long-term strategic planning. Use examples to further demonstrate how value is added to an existing organization.

Write the paper from the perspective that it will be presented to the firm’s management team as you are trying to persuade them to utilize business analytics for data-driven decision making. The paper should contain: Cover Page (update date) Table of Contents (auto-generated by Microsoft Word and updated) Introduction Implementation Plan (5–6 pages of content revised as per instructor feedback) Management Information Systems Section: (5–6 pages of new content) Importance of MIS Techniques and Tools Utilized Along with examples Added Value to Organization Conclusion References

Utilize at least 6 scholarly sources in support of your recommendations.

Make sure you write in a clear, concise, and organized manner; demonstrate ethical scholarship in appropriate and accurate representation and attribution of sources; display accurate spelling, grammar, and punctuation. Submit a 10–12-page report in Word format. Apply APA standards to citation of sources.

Paper For Above instruction

In today’s rapidly evolving business environment, effective management of information systems (MIS) and data-driven decision making are vital components of organizational success. For a design firm contemplating expansion, integrating business analytics into their existing technological infrastructure can significantly enhance strategic planning and operational efficiency. This paper revises an initial implementation plan, emphasizing the importance of MIS, exploring techniques and tools for data management, and illustrating how technology can add value at both operational and strategic levels.

Importance of Management Information Systems in Data-Driven Decisions

Management Information Systems serve as critical frameworks that collect, process, and distribute information necessary for managerial decision-making. According to Laudon and Laudon (2018), MIS provides managers with easy access to timely, relevant, and accurate information, enabling informed decisions that align with organizational goals. For a firm in the expansion phase, MIS facilitates the integration of disparate data sources and supports predictive analytics to assess potential benefits of opening a second location.

Data-driven decision-making, underpinned by robust MIS, allows organizations to base their strategies on empirical evidence rather than intuition. It reduces uncertainty and enhances operational efficiency, customer satisfaction, and competitive advantage (Chen et al., 2012). For instance, analyzing customer data can help tailor marketing efforts, optimize resource allocation, and forecast demand, which is essential during expansion phases.

Techniques and Tools for Data Management

Effective data management is foundational for successful business analytics. This involves techniques such as data cleansing, integration, and governance. Two notable techniques include:

1. Data Governance: Establishing policies and standards to ensure data quality, consistency, and security (Khatri & Brown, 2010). Implementing data governance frameworks helps prevent inaccuracies, redundancies, and compliances issues, especially when integrating data from multiple sources.
2. Data Warehousing: Creating centralized repositories for storing integrated data from various operational systems (Inmon, 2005). A data warehouse enables efficient querying and analysis of large datasets necessary for strategic decision-making.

Three effective tools that facilitate data management include:

1. ETL (Extract, Transform, Load) Tools: Software solutions like Talend or Informatica automate data extraction from sources, transformation for consistency, and loading into warehouses.
2. Business Intelligence Platforms: Tools such as Tableau, Power BI, or QlikView allow visualization and analysis, making data insights accessible to decision-makers.
3. Master Data Management (MDM) Software: Ensures consistency and accuracy in key organizational data, critical for maintaining reliable analytics.

Utilizing Techniques and Tools to Present Data

Presenting data effectively to management requires visualization and reporting tools that translate raw data into actionable insights. For instance:

• Dashboards: Interactive dashboards created via Power BI or Tableau enable managers to monitor key performance indicators (KPIs) in real-time, facilitating prompt responses to operational issues.
• Automated Reports: Scheduled reports that summarize performance metrics help management track progress towards strategic goals, reducing manual reporting efforts.
• Data Storytelling: Using visual elements combined with narrative explanations enhances understanding, making complex data accessible to all decision-makers.

Innovative Examples of Data Management Best Practices

Modern data management incorporates techniques like predictive analytics, machine learning, and cloud computing. Examples include:

1. Predictive Analytics: Utilizing historical data to forecast future trends, such as customer demand or sales patterns, supporting proactive decision-making.
2. Machine Learning Algorithms: Implementing algorithms that automatically identify patterns and anomalies, helping prevent operational risks or uncover new opportunities.
3. Cloud-Based Data Lakes: Leveraging cloud platforms like AWS or Azure to store and process vast amounts of structured and unstructured data, ensuring scalability and cost-effectiveness.

Applying these practices allows the firm to optimize resource allocation, enhance customer targeting, and support long-term growth strategies.

Adding Value Through Business Analytics

The integration of business analytics transforms data into a strategic asset. For the design firm, data can improve daily operations by streamlining workflows, optimizing staffing, and managing project timelines more efficiently. Strategically, analytics support market expansion decisions by analyzing geographic customer data, competitor landscapes, and financial implications of adding a second location.

For example, predictive models can estimate customer demand in the new region, enabling informed investment decisions. Value is created through increased operational efficiency, improved customer satisfaction, and data-driven innovation, fostering a competitive advantage in the marketplace (McKinsey & Company, 2020).

Conclusion

Implementing a comprehensive business analytics plan anchored by robust MIS and advanced data management techniques offers significant benefits for organizations contemplating expansion. By leveraging effective tools and innovative practices, the firm can convert raw data into strategic insights, supporting day-to-day operations and long-term growth. This transformative approach aligns with current best practices, ensuring the organization remains competitive and adaptable in a digital economy.

References

• Chen, H., Chiang, R., & Storey, V. (2012). Business intelligence and analytics: From big data to insights. MIS Quarterly, 36(4), 1165-1188.
• Inmon, W. (2005). Building the Data Warehouse. John Wiley & Sons.
• Khatri, V., & Brown, C. V. (2010). Designing data governance. Communications of the ACM, 53(1), 148-152.
• Laudon, K.C., & Laudon, J.P. (2018). Management Information Systems: Managing the Digital Firm. Pearson.
• McKinsey & Company. (2020). The next normal: How business analytics is reshaping industries. McKinsey Insights.
• QlikView. (2023). Business intelligence solutions. Qlik Technologies.
• Talend. (2023). Data integration and management solutions. Talend.
• Wang, R., & Wood, L. (2020). Cloud computing and data lakes: Opportunities and challenges. Journal of Cloud Computing, 9(1), 1-15.
• Yam, S., & Lang, C. (2019). Data-driven decision making in organizations. International Journal of Data Analysis, 8(2), 45-60.
• Inmon, W. H., & Linstrom, M. (2015). Data Warehouse Design Strategies. Elsevier.