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Data Governance and Architecture: How Poor Data Management Can Sink Your Business and What You Can Do About It 

February 28, 2024

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Data Governance and Architecture: How Poor Data Management Can Sink Your Business and What You Can Do About It 

If you’re a business owner, especially in the IT sector, you likely understand the importance of data. According to Zippia, 24 percent of companies use big data analytics, while 97.2 percent say they’re investing in big data and AI projects.¹ However, a significant challenge has emerged – about 95 percent of these businesses encounter difficulties in managing unstructured data. 

The necessity for skilled data management cannot be overstated. Poor data handling can lead to flawed decision-making, non-compliance with regulations, and increased security risks. If you’re seeking to bolster your understanding of data governance and management, this article is for you. 

The Rise of AI and Big Data: An Urgent Call for Skilled Data Governance and Management 

Over the last recent years, the rapid expansion of AI and big data technologies has ushered in a new era of data-driven decision-making, fundamentally transforming industries and societal norms. This transformation is underpinned by an unprecedented growth in data volume.  

According to the International Data Corporation (IDC), the world’s data volume is expected to soar to 175 zettabytes by 2025.² This massive surge, fueled by the proliferation of IoT devices, social media, and various digital platforms, poses significant challenges in data storage, processing, and effective management. Integrating AI into the big data landscape amplifies these challenges.  

AI’s role in analyzing vast datasets offers invaluable insights across sectors like tech, healthcare, finance, and transportation. However, AI systems are only as good as the data they process. It raises concerns regarding data quality, algorithmic bias, and ethical decision-making, which lead to loss of monetary resources. A study by MIT Sloan Management Review revealed that poor-quality data costs businesses an average of 15 to 25 percent of their revenue.³ 

Another critical aspect of this data-centric era is privacy and security. The increase in data breaches and cyber-attacks has made data security a top priority. Regulations like the EU’s General Data Protection Regulation (GDPR) have set new benchmarks for data privacy and security, mandating organizations to protect personal data rigorously. The implications of non-compliance are severe, often summing up to €20 million or 4 percent of a company’s annual global turnover, whichever is even higher.⁴  

In response to these concerns, businesses are increasingly turning to advanced data governance. 

Related Reading: The CIO and CTO’s Role in Fostering Generative AI Proficiency 

Data Governance Framework 

At its core, data governance ensures that data is consistent, trustworthy, and doesn’t get misused. Each of the components below contributes to the overarching field of data governance and management. 

1. Data Architecture 

Data Architecture refers to the overall structure and design of data and data-related resources. It involves designing databases, data warehouses, and other storage solutions, as well as the schemas, rules, and standards that govern data collection and use. The primary goal of data architecture is to ensure that data is stored in a manner that supports its accessibility, reliability, and scalability. 

2. Data Lifecycle Management (DLM) 

This encompasses the processes of creating, using, storing, archiving, and deleting data. It ensures that data is managed effectively throughout its lifecycle, from creation to eventual disposal, per business needs and compliance requirements. 

3. Data Quality Management 

This component focuses on maintaining data that is accurate, complete, consistent, and relevant. High-quality data underpins effective decision-making and operational efficiency. It includes data validation, cleaning, deduplication, and standardization, and setting quality metrics and benchmarks. Regular audits and feedback are necessary for maintaining data integrity and quality. 

4. Data Classification and Tagging 

Classification involves categorizing data based on sensitivity, type, and compliance requirements, aiding in protecting sensitive information and ensuring legal adherence. Tagging supplements this by attaching metadata to data, enhancing searchability and manageability. Together, these processes bolster data security, aid in risk management, and improve operational efficiency by making data more accessible and easier to control. 

5. Data Ethics and Compliance 

This area addresses the moral principles of data management and adherence to legal and regulatory requirements. It covers respecting user privacy, ensuring fairness, avoiding bias in data analytics and AI, and complying with laws like GDPR, HIPAA, or CCPA. Establishing ethical guidelines and compliance frameworks is vital to protect sensitive information and individual privacy. 

6. Data Security and Privacy 

This involves protecting data from unauthorized access, breaches, and theft. It encompasses encryption, access controls, and other security measures. Ensuring data privacy involves adhering to relevant laws and regulations regarding the handling of personal and sensitive data. 

7. Data Stewardship 

This refers to the responsibility and accountability for data management within an organization. Data stewards are tasked with ensuring the data meets quality and governance standards, and they act as liaisons between IT and business functions. 

8. Change Management 

Data governance often requires changes in processes, culture, and technology. Effective change management ensures smooth transitions and the adoption of new practices. 

Data Governance Best Practices 

Implementing data governance in your organization is a crucial step towards managing your data assets effectively. Here’s how you can personally ensure best practices in data governance: 

1. Understand Your Data 

The foundation of any data governance framework is a thorough understanding of the data. Begin by identifying and mapping all data assets within your organization. This includes structured data in databases and unstructured data such as documents and emails. Utilizing data mapping tools can significantly streamline this process. 

2. Create a Data Structure 

Once all data is identified, the next step is organizing it systematically. Define clear data categories and sensitivity levels. Implement a unified data catalog that serves as a central reference point, facilitating easy access and maintaining consistency in data handling across the organization. 

3. Build Your Data Governance Framework 

Establish a framework tailored to your organization’s needs. This should be a clear guide on policies, procedures, and standards for managing data, understandable by everyone involved. 

Related reading: Data-Driven Decision Making: Harnessing ServiceNow and On-Demand Staffing Insights 

4. Assign Data Roles and Responsibilities 

Identify who in your team will take on roles like data owners, stewards, and custodians. Clearly define these roles to ensure everyone knows their responsibilities regarding data management. 

5. Prioritize Data Privacy and Security 

Embedding privacy and security into the fabric of your data governance policies is non-negotiable. Ensure compliance with data protection regulations and implement robust security measures to safeguard data against breaches and unauthorized access. This includes regular data quality audits, data cleaning, and validation processes. 

6. Promote a Culture of Data Literacy 

Employees should be trained to understand the importance of data governance, how to use data effectively, and the implications of non-compliance. A data-literate workforce is better equipped to make informed decisions based on accurate and reliable data. 

7. Garner Organizational Support 

For data governance to be effective, it’s essential to have buy-in from all levels of the organization. Communicate the value and importance of data governance to stakeholders and establish a network of data stewards across various departments to champion governance initiatives. 

ELEVATE YOUR BUSINESS WITH COMPREHENSIVE SOLUTIONS FOR EVERY NEED 

In today’s rapidly evolving digital landscape, the right approach to data governance is crucial for business success. At Raso360, we specialize in transforming data challenges into growth opportunities. Our range of services, including Onshore Consultancy, Offshore Delivery, and Direct Hire Placements, are meticulously crafted to meet the unique needs of your business. Our Onshore Consultancy offers tailored, local expertise, ensuring data governance strategies that are not only effective but resonate with your business culture and objectives. 

Explore the benefits of our Offshore Delivery for cost-effective, scalable solutions that leverage global expertise to bring excellence to your operations. If you’re looking to enhance their teams, our Direct Hire Placements provide access to top-tier talent in data governance, perfectly aligned with your company’s vision. Seize the opportunities of the digital age – connect with us today, and let’s help you elevate your business’s data governance to new heights. 

References 

1 McCain, Abby. “26 Stunning Big Data Statistics [2023]: Market Size, Trends, and Facts” Zippia, 16 Jan. 2023, www.zippia.com/advice/big-data-statistics/ 

2 Patrizio, Andy. “IDC: Expect 175 zettabytes of data worldwide by 2025” Network World, 3 Dec. 2018, www.networkworld.com/article/966746/idc-expect-175-zettabytes-of-data-worldwide-by-2025.html/ 

3 Redman, Thomas. “Seizing Opportunity in Data Quality” MIT Sloan Management Review, 27 Nov. 2017, www.sloanreview.mit.edu/article/seizing-opportunity-in-data-quality/ 

4 “What are the GDPR fines for non-compliance?” GDPR, 18 April 2018, www.gdprregister.eu/gdpr/gdpr-fines/ 

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