Leveraging Business Intelligence for Digital Insights and Growth - Essay Sample

Introduction

D3M involves the data collection process based on measurable goals, analyzing fact patterns from these insights to develop activities and strategies benefiting business in several areas. The process entails choosing what and how to teach from factual information Provost, & Fawcett, (2013). Digital insights of wealth at fingertips leverage embracing business intelligence power; informed decisions are achieved, leading to evolution, commercial growth, and increased bottom line. Right reporting tools are implemented and analyzed, measuring the data accurately to make data-driven decisions driving businesses forward. Employing a data-driven approach means strategic decisions are made through its analysis and interpretation.

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Significant points in the process involve the right data collection, accessibility, reporting and analysis, and creating culture acting on data. The collection of accurate data is of great importance since it can be shared for more significant insights. Once the data has been collected, the system joins it together for sharing, report queries, and historical analysis. Reporting data does not give all the information since it only tells what, while the study explains why it happened. Both are data-driven and very important. Right architectural and analytical tools help to make useful insights. Still, there is a need for people asking the right questions and organization culture, encouraging ideas based action. People involved should have skills to identify correct data and metrics informing of the next step.

D3M concerning Education Leadership

Application of data-driven choice production is the central focus for scholar achievement testing data and reform effort, due to federal and test-based accountability state policies Wohlstetter, Datnow, & Park, (2008). Districts and schools use RAND research to improve student success by making decisions on analyzed achievements test results and other data types. D3M policies are examined, and future research suggested in the field. Discussion is organized on an intangible framework modified from the literature that distinguishes that numerous data types (input, outcome, process, and satisfaction data) enlighten decisions. In its raw form, data cannot be effectively used. Stakes attached to that data such as economic progress appears as the same harmful practices in high-stakes testing systems Marsh, Pane, & Hamilton, (2006). In educators’ promotion, policymakers should consider allowing teachers flexibility to alter data analysis instruction.

Principles of data understanding disparate are helping teachers know their students further through; misinterpretations or misunderstanding evidenced in test scores students’ experience. However, the process has multiple steps in data forms that bring classroom setting experience to the process. The wealth of knowledge in students is brought out through data observation, project outcomes, teacher-made check data, and additional learning informing their exercise. The chore is fitting puzzle bits together in denouncing the practice and understanding the way forward.

D3M support students since teacher participating are provided with scaffolding lesson on understanding the content of science inquiry. Ongoing PD session series are included in scaffolding to focus on scheme module’s science contented regarding; means of view, synthesize, scrutinize, making meaning of projects dashboard data collected. Several technology assessments’ proposed sustained changes in teacher behaviors related to PD programs. Ongoing support and high-quality PD are essential components Wayman, (2005). Teachers address students misunderstanding through collaborative efforts and lessons designed plans providing differentiated instructions. D3M at the classroom level supports differentiated instruction acting as learner-centered teaching tool assisting teachers issue instructions fitting both individual and class learning needs. Required teacher support and resources are provided by the dashboard enabling data-driven discussions. At this point, teachers extract students’ performance information and noted struggles with some specific skills and underlying science concepts. Teachers’ collaborative conversations lead to knowledge-based creation of lesson plans and possible steps addressing changes in student learning needs based on instructions. Also, they implement instructional decisions agreed upon in their classrooms.

Once teachers implement lessons to plan, further assessment changes in student’s observations, mainly on performance, are pointed out of the result of the iterative process about formative assessment in D3M. Practice wisdom ``tacit knowledge’’ is difficult to articulate even to the experts. Presuming out how to diagnose, share, create, and bring about it is so challenging.

Limitations to the Use of 3DM

• Typically applications since data is entered manually makes them challenging to use and prone to errors. In solving complex matters, D3M involvement contributes to challenging issues due to the following aspects.
• Human involvement and preferences in the process.
• Actionable knowledge discovery in making decisions.
• Relevant aspect consolidation for decision support.
• Social factors and organization surrounding applications.
• Domain intelligence and knowledge are making data close to business needs.

Conclusion

I encourage the use of D3M aimed at the benefits of instructors and undergraduates on the continuing evolution of data and analytics. Teachers can do data incorporation into their work driven by instructions and utilizing many new tools designated to assist them in data work and students' performance improvement. Data cultivation power harness genuinely causes an understanding of social justice complex issues, which many students face to prove and help educators in that level of field development solutions for disenfranchised students. The process doesn't require significant overhaul practices at once. Lesson on planning and teaching method decisions can improve from using data benefit. The staff should use tools at their disposal following the steps correctly to create a culture benefiting students in tangible ways.

Though data use in decision-making support is observed as an establishment in collected works, it has fascinated attention in the past years due to rising interest in data science. Another study group seeks to analyze data sources like sensors and data servers, supporting decision-making in different scenarios. The journal has made me realize that education is not only based on books and research but also on planning and development. Also, there is a provision of information on policies and recommending appropriate strategies to solve education system problems through research for educational and economic planning purposes. D3M focus on data management is minimizing the education reporting burden. However, before I make any decision regarding the development of D3M, joining a strategy, or in consideration of basing a better decision on and SIF repository model, I must-have techniques for certifying quality data. To my understanding, the process applies in real life of the business, necessitating accurate results expected on the ongoing.

International literature examines the conflict between philosophy and practice in Barbadian schools. D3M applications are mostly observed in schools of children with special needs and disabilities and schools which are streamed for ability. Teachers apply D3M to learn their students and acting towards them. The process starts with learning students’ abilities, analyzing and group-related ones systematically, nurturing, and improving them by facilitating training priorities, tests, and accreditation to achieve required standards. D3M is mainly implemented in co-curricular activities in supporting trainees to enhance their talents and improve them. Students also apply D3M in their research studies, mostly in higher education levels where they collect data, analyze it, process, and report the results.

References

Marsh, J. A., Pane, J. F., & Hamilton, L. S. (2006). Making sense of data-driven decision making in education: Evidence from recent RAND research. https://www.rand.org/pubs/occasional_papers/OP170.html

Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59. https://www.liebertpub.com/doi/full/10.1089/big.2013.1508

Wayman, J. C. (2005). Involving teachers in data-driven decision making: Using computer data systems to support teacher inquiry and reflection. Journal of education for students placed at risk, 10(3), 295-308. https://www.tandfonline.com/doi/abs/10.1207/s15327671espr1003_5

Wohlstetter, P., Datnow, A., & Park, V. (2008). Creating a system for data-driven decision-making: Applying the principal-agent framework. School effectiveness and school improvement, 19(3), 239-259 https://www.tandfonline.com/doi/abs/10.1080/09243450802246376