Overcoming 4 Key Data Challenges in Higher Ed Institutions. Môn Kỹ năng viết và thuyết trình bằng tiếng Anh | Đại học Trường Đại học Phenika.

lOMoAR cPSD| 59416725
Overcoming 4 data-quality challenges in higher education
Administrators and faculty are then left sifting through inconsistencies that
make it harder to generate accurate reports. As a result of these inefficiencies,
quality issues can impact funding, compliance and institutional outcomes.
Organizations should address these challenges by understanding the most
common pitfalls and exploring ways to overcome them.
Maintaining high-quality data is essential for colleges and universities, but
many face persistent challenges that keep them from utilizing information
effectively. Below are some of the most common issues they experience.
Higher education organizations are increasingly leveraging multiple cloud solutions
to manage their data, often leading to fragmentation across different systems. Reports
show that 43 percent of organizations cite an increasingly distributed and remote user
base as a key driver of this shift.
While cloud-based tools provide flexibility, they also create challenges in integrating
and standardizing insights. Therefore, it can be hard to get a clear view of institutional success.
2. Inaccurate or Outdated Information
Accuracy is critical for compliance, funding and tracking student success, yet many
struggle with outdated or duplicate records. Errors arise when manually entering
information across multiple platforms or when reporting structures vary across
departments. This challenge puts institutions at risk of making inaccurate decisions,
impacting several aspects of operation.
3. Lack of Data Governance
Organizations face challenges in ensuring security, accessibility and consistency.
Many colleges and universities lack clear policies on how they should collect, store
and use data. This leads to compliance risks and vulnerabilities, compromising data integrity and security.
4. Inability to Use Insights Effectively
Many universities struggle to translate figures into insights. Studies show that higher
education institutions lack resources when leveraging data for strategic planning.
This gap stems from several issues, such as a lack of integrated analytics tools and lOMoAR cPSD| 59416725
limited expertise. Institutions then miss valuable opportunities to improve student
outcomes and enhance operational efficiency. 1. Basic Information •
Title of the article: Overcoming 4 data-quality challenges in higher education •
Author / Publication / Date: The author is not mentioned in the excerpt. The
publication source and date are also not provided, so these would need to be
looked up to complete the citation accurately. •
Type of source: Likely a professional blog post or educational industry
article, possibly from a website that focuses on education technology or institutional research. 2. Main Content • Main topic or issue:
The article discusses the major challenges higher education institutions face
regarding data quality and how these issues hinder effective decision-making and reporting. •
Author’s main argument or purpose:
The author aims to highlight common data-quality pitfalls in higher education
and suggest that institutions need to address these to enhance outcomes, ensure
compliance, and improve operations. •
Two or three key points:
1. Fragmented data systems due to increased use of cloud tools make it
difficult to integrate and standardize information.
2. Inaccurate or outdated data, such as duplicate records, often result
from manual entry and inconsistent reporting practices.
3. Lack of data governance and strategy leads to compliance risks and
makes it harder to use data effectively for strategic planning. 3. Understanding and Analysis • Unfamiliar concepts: lOMoAR cPSD| 59416725
The term "data governance" might not be immediately clear. I understood it
through context as referring to the policies and procedures organizations use to
manage data—ensuring it’s accurate, secure, and used correctly. •
Is the argument convincing?
Yes, it is. The article clearly outlines the consequences of poor data quality and
supports its points with real-world issues, like funding impacts and compliance
risks, making the argument practical and relatable. •
Evidence, data, or examples:
A compelling data point is that 43 percent of organizations cite remote,
distributed users as a driver for cloud-based systems, supporting the point
about fragmented data environments. 4. Personal Response •
Interesting or surprising element: I
found it surprising how something as simple as inconsistent data entry
can ripple out to affect funding and institutional decisions. • Agree or disagree: I
agree with the article. In an era where decisions are increasingly data-
driven, not having strong data practices can really undermine an institution’s
ability to serve students effectively. •
Connection to experience or studies:
This connects to my studies or work where I’ve seen how hard it is to pull
meaningful insights when data is stored across unconnected systems, or when
people input information differently depending on the department. 5. Sharing with Classmates •
Summary in 3–4 sentences:
The article explores four major data-quality challenges faced by colleges and
universities. These include fragmented systems, inaccurate or outdated data,
lack of data governance, and difficulty turning data into insights. These issues
can negatively affect compliance, funding, and student outcomes. The article
encourages institutions to tackle these problems to operate more efficiently and strategically. •
Question for classmates:
Have you ever experienced or noticed the effects of poor data quality in your
own school or workplace? What happened, and how was it handled?