Data Governance: A Comprehensive Overview

Data governance encompasses well-defined processes and protocols to manage internal and external data within an organization efficiently. It harmonizes human resources, procedural frameworks, and technological infrastructure to foster a comprehensive understanding of data, thereby improving the status of a critical enterprise asset. Data governance entails overseeing an enterprise’s data’s availability, usability, integrity, and security by established internal standards, policies, and regulations. A robust data governance framework guarantees that data remains uniform, understandable, accurate, complete, reliable, secure, and easily accessible.

This holistic approach to data governance encompasses a wide array of domains, including data architecture, modeling, storage, operational processes, security measures, interpretability, interoperability, document management, content handling, reference and master data, data warehousing, business intelligence, metadata management, and data quality assurance. It lays the groundwork for effectively standardizing, integrating, safeguarding, and storing corporate data.

The primary objectives of a well-structured data governance initiative include:

• Mitigating data security risks
• Establishing internal protocols for data utilization
• Ensuring compliance with regulatory requirements
• Enhancing both internal and external communication channels
• Amplifying the intrinsic value of data
• Furnishing a resilient foundation for the sustained growth and continuity of the organization through prudent risk management and optimization strategies.

Since data governance initiatives have far-reaching implications at enterprises’ strategic, tactical, and operational levels, they must be treated as an ongoing and iterative process. This approach ensures that data is efficiently organized and utilized within the organization’s context and aligned with other concurrent data projects.

The Significance of Data Governance

• Defining Data Governance: Data governance is the blueprint that dictates who manages data, what processes are in place, and how data is maintained. A prominent example is Walmart, which manages petabytes of data daily. Their robust governance framework ensures that data is accurate and available across thousands of stores worldwide.

• The Data Explosion: Take the healthcare industry, for instance. With the proliferation of electronic health records (EHRs), hospitals and clinics grapple with immense volumes of patient data. Effective governance is crucial not only for compliance with regulations like HIPAA but also for ensuring the integrity and security of sensitive information. 2023, the healthcare sector alone will generate over 2.5 exabytes of data.

• Compliance and Risk Management: In 2018, the General Data Protection Regulation (GDPR) emerged, transforming how businesses handle personal data. Non-compliance can lead to fines of up to €20 million or 4% of the company’s global annual turnover. This emphasizes the critical role of data governance in regulatory adherence and risk mitigation.

Critical Components of Effective Data Governance

• Clear Data Ownership: Consider a financial institution like JPMorgan Chase. They designate specific data stewards responsible for various data sets. This clarity ensures accountability and the maintenance of high-quality data. For instance, in 2023, JPMorgan Chase’s data governance team reduced data discrepancies by 30% through diligent ownership assignments.

• Data Quality Management: Amazon’s success is partially attributed to its meticulous data quality practices in the retail sector. Automated validation and constant monitoring ensure that product listings, customer reviews, and order details remain accurate and reliable. This commitment to data quality has led to a 15% increase in customer satisfaction ratings in 2023.

• Metadata Management: a global streaming giant, Netflix relies heavily on effective metadata management. This involves tagging content with information like genre, actors, and viewer preferences. It enables precise content recommendations and a seamless user experience. In 2023, this strategy contributed to a 20% increase in user engagement and a 25% decrease in content search time.

• Data Privacy and Security: Recent data breaches have highlighted the need for robust privacy and security measures. For example, Equifax, a consumer credit reporting agency, suffered a significant breach in 2017, compromising the personal information of nearly 147 million individuals. Effective data governance includes encryption, access controls, and regular security audits. In 2023, Equifax implemented advanced encryption protocols, reducing the risk of data breaches by 40%.

Challenges and Best Practices

Challenges:

Implementing effective data governance isn’t without hurdles. One common challenge is resistance from employees accustomed to existing data management practices. Additionally, keeping up with evolving regulatory landscapes poses a continuous challenge. For example, in 2023, introducing new data privacy regulations in Europe forced many multinational companies to adapt their governance frameworks.

Best Practices:

• Begin with a Solid Foundation and expand to Greater Horizons: At the core of data governance lie three foundational pillars: people, processes, and technology. Establishing a solid foundation starts with assembling the right team, developing robust strategies, and integrating technology seamlessly into these processes. Without the appropriate individuals driving the initiative, constructing effective methods for the technical implementation of data governance becomes challenging. Therefore, the initial step for any organization is to identify and onboard the right talent. With the right team, you can construct the necessary processes and acquire the technology required to execute the task.

• Identify Critical Data Elements: Prioritize data elements crucial for business operations. This ensures focused governance efforts where they matter most. In 2023, a leading e-commerce company, eBay, identified critical customer behavior data, resulting in a 25% increase in targeted marketing effectiveness.

• Treat Data as a Strategic Resource: Just as companies allocate resources for R&D, data should be viewed as a strategic asset. Investment in its governance yields long-term benefits. In 2023, companies that viewed data as a strategic asset reported a 30% increase in ROI from data-driven initiatives.

• Set Policies for the Data Lifecycle: From acquisition to disposal, establish clear policies. This ensures that data is managed consistently and securely throughout its lifecycle. In 2023, companies with well-defined data lifecycle policies reduced data storage costs by 15%.

• Involve Business Users in Governance: Collaborate with business units to understand their specific data needs. This fosters a culture of data ownership and ensures alignment with organizational objectives. In 2023, companies that actively involved business users in governance reported a 20% increase in customer data accuracy.

Emerging Trends in Data Governance

Artificial Intelligence and Machine Learning are increasingly pivotal in automating governance processes. Additionally, Blockchain technology is being explored to enhance data security and transparency. With the rise of Edge Computing, managing data at the source becomes crucial. These trends underscore the dynamic nature of data governance in the digital age.

Presently, organizations are accumulating and managing increasingly vast amounts of data daily. In the aftermath of the COVID-19 pandemic, with a significant portion of the workforce operating from remote locations, employees frequently handle sensitive data outside traditional office environments. This shift to a remote work scenario can lead to reputational harm and financial liabilities without proper governance. The data governance market is anticipated to grow at a compound annual growth rate (CAGR) of 21.44% from 2021 to 2026, according to a research report from Mordor Intelligence for 2021. It is projected to reach a market value of over USD 5.28 billion by 2026.

Wrapping Up

In the data-driven landscape of 2023, effective data governance is not just a competitive advantage; it’s a prerequisite for sustainable success. By acknowledging its significance, understanding its components, and adopting best practices, businesses can harness the true power of their data. As we navigate the complexities of an interconnected world, robust data governance will be the compass that guides us toward informed, strategic decisions.

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What is Data Management?

Data management is the process of collecting, storing, organizing, maintaining, using, and disposing of data in an efficient and secure manner. It involves establishing policies and procedures for data acquisition, validation, storage, backup and recovery, access, and destruction. Effective data management is crucial to improve operations, and support growth.

Why Should You Adopt a Modern Approach to Data Management?

One of the main forces driving contemporary data management is the growth of big data. Businesses must come up with new methods to store, handle, and analyse data given its exponential increase. With its scalable and adaptable features, cloud computing offers the perfect option.

Increased Business Agility

The processing and analysis of data by organisations has been revolutionised by artificial intelligence (AI) and machine learning (ML). Businesses may use AI and ML to automate complicated data management activities, find patterns and insights, and make decisions more quickly and accurately. Businesses may confidently make data-driven decisions since ML systems can learn from past data to increase their accuracy over time enabling organizations to respond to changing business conditions more effectively..

Improved Data Analytics

Modern data management has increasingly reliant on data analytics and visualisation tools. These tools make it simpler for companies to recognise patterns, trends, and anomalies by transforming raw data into insights that can be used to gain valuable insights from their data.

Data Integration and Interoperability

A modern approach to data management supports the integration of data from multiple sources and systems. The increasing amount of data generated by various sources; data integration has become more challenging. Modern data management solutions must support data integration across multiple platforms and systems, providing a comprehensive view thus enabling organizations to more effectively leverage their data.

Governance with Regulations

Data governance refers to the policies, procedures, and controls used to manage data quality, security, and privacy. With the increasing importance of data in business decision-making, data governance has become critical to ensure data accuracy, security, and compliance with regulatory requirements. A modern approach to data management helps organizations comply with data privacy regulations, such as GDPR and HIPAA, and reduces the risk of penalties for non-compliance.

Also read: Crucial Role that Data Fabric Plays in Data Management Today

What should be considered for a successful approach to modern data management?

Having a successful approach to modern data management requires several key elements, including:

Effective data governance aids for data management to be successful, it is essential to implement clear policies and procedures for data gathering, validation, storage, backup, recovery, access, and deletion. Making sure that data is always correct, comprehensive, and consistent is essential for making well-informed decisions and running a successful organisation, so it is important to have a data quality management system in place.

The key to ensuring that data management operations are successful, efficient, and complement the overall business plan is having devoted, well-trained data management staff. To meet the organization’s changing demands, data management should be a process that is continually evaluated and improved.

Data management can present several challenges, including:

Data Volume: The exponential growth of data, also known as big data, can present a challenge for organizations that need to store, process, and analyze large amounts of data.

Data Variety: The increasing variety of data types and formats can make it difficult for organizations to manage and integrate data from multiple sources.

Data Quality: Ensuring the accuracy, completeness, and consistency of data can be challenging, especially as data is sourced from multiple systems and platforms.

Data Security: Protecting sensitive data from unauthorized access and ensuring compliance with data privacy regulations, such as GDPR and HIPAA, can be challenging.

Data Integration: Integrating data from multiple sources and systems can be difficult, especially when data formats are incompatible or data is stored in silos.

Data Governance: Implementing effective data governance policies and procedures that ensure consistent and compliant data management practices can be challenging.

Data Management Teams: Finding and retaining skilled data management professionals can be difficult, especially as the demand for these skills continues to grow.

Budget and Resources: Securing sufficient budget and resources to implement and maintain a robust data management program can be a challenge.

These challenges highlight the importance of adopting a comprehensive, well-planned, and well-executed data management strategy that takes into account the unique needs and requirements of each organization.

How Do You Create a Modern Data Architecture?

To create modern data architecture, you can follow these steps:

Step 1:  Start by defining the business requirements and determining what information the organisation needs to gather, store, and analyse in order to achieve its objectives. List the numerous data sources, including social media, transactional systems, logs, and third-party APIs. decide what modifications are required to accommodate the new design, and evaluate the existing data infrastructure.

Step 2:  Choose the best technology for data storage, processing, and analysis based on your needs and the sources of your data. Data lakes, data warehouses, and cloud services may all fall under this category. Then Design the data architecture in accordance with the needs, data sources, and technologies you have chosen. Creating data models, data pipelines, and data access patterns may be part of this. finally Implement the data architecture, then test it to see if it functions as expected and satisfies the criteria.

Step 3: Maintain regular monitoring of the data architecture and make changes as necessary to make sure it continues to fulfil the demands of the enterprise. Keep in mind that a modern data architecture needs to be scalable, versatile, and secure to suit the business’s continuously changing needs.

Conclusion

By leveraging the latest technologies and tools, and by having dedicated and well-trained data management teams in place, organizations can ensure that they have the right data at the right time to support their business needs. By adopting a modern approach to data management, organizations can increase operational efficiency, improve customer understanding, and gain a competitive advantage in their respective markets.

In conclusion, modern data management is essential for business success as it enables organizations to effectively collect, store, and analyze data to support informed decision making and drive business growth. The volume, variety, and velocity of data continues to increase, making it more important than ever for organizations to adopt modern data management practices that support effective data governance, security, and privacy.

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What is Data Governance? A McKinsey study points to 15 to 25 percent growth and increase in EBITDA in companies that use data-driven B2B sales-growth engines.

While this is good news, what is of greater importance is to know that data collection is only Step #1. Managing and organizing data is essential to reap the expected benefits, and the larger the volume of data, the greater the need for data organization and management.

And, there is more. Data management and organization are just the tip of the iceberg. Businesses need to set in place policies and processes around making the data available and usable while ensuring its integrity and security. This is where data governance plays a crucial role. It ensures data consistency and trustworthiness while protecting data from being breached and misused. They need data governance.

What is Data Governance?

Data Governance refers to the corporate view on a variety of aspects related to data engineering services, including:

● The collection processes

● Roles and responsibilities of the employees accessing the data

● Policies and standards

● Metrics to measure data usage and ensure its effectiveness and efficiency in achieving goals

A data governance program is designed by bringing together a steering committee consisting of the executives, IT,, and data management teams. They create the policies and standards that govern data collection and usage processes, while data stewards implement and enforce the procedures.

Challenges to Data Governance

Data governance is mandatory and guided by regulatory requirements to protect data privacy and security. However, having an effective and efficiency data governance practice is a complex process. It requires a common understanding of key data entities among the different stakeholders, requiring common data definitions and formats.

Second and key challenge is in determining the business value, which can make it difficult to get the required approvals, budgetary allocation, and support of the key stakeholders.

To demonstrate the business value of data governance on an ongoing basis, quantifiable metrics need to be identified, established, and communicated to the rest of the organization.

One of the advantages of cloud-based data access and modern technologies is that data is now available to the business users for self-service BI and analytics. This has added to the complexity of data governance challenges of ensuring data is not misused or breach data privacy and security requirements. Streaming data in real-time analytics is another compilation where accuracy, privacy, and security are not impacted.

Big data analytics solutions with the amalgamation of structured, semi-structured, and unstructured data adds another layer of complexity to governance, which traditionally dealt only with structured data.

Siloed data, lack of resources, poor quality of data are among some of the other challenges that make data governance complex.

Must read: Data Governance and Security of Cloud Data Warehouse

3-Step Guide to Improve Data Governance

To ensure effective governance and overcome the challenges, businesses need a structured approach. This includes:

1. Identifying Distinct Use Cases: Understand the benefits, costs, and risks of governance to be able to make a business case and allocate the necessary resources. By identifying the use cases, engaging the stakeholders becomes easier and ensures a more comprehensive data governance framework that addresses meaningful issues.

2. Quantifying Value: Assign a quantifiable value to the key performance indicators and monitor the KPIs helps to:

– Assess the effectiveness of data governance framework

– Strengthen the need to align processes with the frameworks where it proves to be effective

– Identify areas of improvement to improve it further

3. Improve Scalable Data Capabilities: Clearly outline the capabilities users require to improve the value and usage of data based on their specific needs. Empower the users with the required technology and processes such as intuitive and searchable catalog that helps discover data assets, enhanced data security, accurate date, and understand data origin, classification, content, and use to:

● Collaborate across the functions for sharing data assets

● Improve internal and external regulatory compliance

You might be interested in: Why Data Fabric is the key to next-gen Data Management

Benefits of Data Governance

An effective data governance strategy provides many benefits to an organization, including:

● Data Consistency: Provides a consistent view of data with common terminology to different business units while enabling flexibles use based on need

● Data Quality: Provides access to accurate, complete, and consistent data

● Data Mapping: Makes it easy to location all data associated with key entities for faster access.

● Single Version of Truth: Often data siloes fragment the view of data, impacting business outcomes. Data governance enables unifying data for providing a holistic view of business operations to improve decision-making.

● Improved Compliance: Meet the requirements of regulations and standards such as the US HIPAA (Health Insurance Portability and Accountability Act), the EU General Data Protection Regulation (GDPR), and industry standards such as PCI DSS (Payment Card Industry Data Security Standards).

Indium to Help Build Data Governance Framework

Indium Software is a digital engineering expert with specialized expertise in data science services, data engineering services, and data lifecycle management services. We help businesses establish a data governance framework by understanding their business needs, identifying their data sources, and creating a centralized data repository to improve data management, organization, and stewardship while ensuring compliance, privacy, and security.

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However, while data and advanced analytics are inseparable, the quality of data determines the quality of analytics as well. Raw data can be incomplete, inaccurate, or filled with errors. Data could be in multiple formats, and structured and unstructured. To ensure that the insights gained from the data are meaningful and help deliver the desired outcomes, the data needs to be processed to make it usable for further analysis. This process is called data preparation and it is an essential step before running advanced analytics.

What is Data Preparation?

Data preparation or data wrangling, as it is also called, is a complex process that encompasses several steps including:

• Finding relevant data
• Collecting it from different systems, both internal and external
• Combining data from different sources
• Structuring
• Imputation of missing data
• Removal of Outliers

Further, this data needs to be processed, profiled, cleansed, validated, and transformed to ensure the accuracy and consistency of BI and analytics results.

Enriching and optimizing data can by integrating internal and external data or from across systems enhance its usefulness and provide greater value and insights. Data preparation also enables curating data sets for further analysis.

Making Data Preparation Efficient

While data preparation is an integral part of advanced analytics, data science is a complex field and often businesses find that this takes up a large portion of their time. It requires the involvement of specialists along with specific tools and technologies to achieve the desired results. The ideal is for businesses to be able to catch errors and correct them quickly, create top-quality data fast for timely decision making.

This requires automation and machine learning to accelerate the preparation process and ensure scalability, future-proofing, and accelerated use of data and collaboration. Automation plays a crucial part in ensuring speed and efficiency, but even to train algorithms, data preparation remains a crucial step.

Some of the best practices in data preparation include:

• Data Governance: Data governance, not a part of the preparation, provides the framework based on which businesses can lay down their advanced analytics goals, define processes and establish the standards for data preparation.
• Ensure Reliability of Source: Establish the reliability of the source and the relevance of data by defining the data needed for the task, identifying the sources, and ensuring the span of time you will need it.
• Start Small: Begging by creating a random sample for validating your data preparation rules before taking on larger volumes.
• Try Different Cleansing Strategies: Find out which strategy works for your stated need by trying them on the small data set before making it operational.
• Cleansing is Iterative: When you run the data preparation processes on your entire data set after clearing the proof-of-concept, there could still be some exceptions. This requires you to constantly finetune your data preparation process to improve the quality of your analytics
• Automate and Augment Data Processes: Creating AI and machine learning-based tools to help with collecting relevant data, scanning it, and transforming it based on organizational goals for repeatable tasks can speed up your data preparation process.
• Enable Self-Service: Normally, data science teams are required for data preparation and this can be a bottleneck as different functions need different data sets. Relying solely on one set of data scientists can delay getting the data for analytics. By enabling self-service using AI-based data preparation tools can allow business users to get the data sets they need for their analytics faster and in real-time.
• Create Collaborative Workflows: Enabling sharing can improve the reusability of authenticated data pipelines and accelerate data preparation.

Indium Approach

Indium Software has a large team of data scientists and data engineers who can help businesses with their data preparation. For instance, one of our customers in the US, a real estate and infrastructure

consulting services provider, helps their customers make informed decisions that can improve the cost-efficiency of their infrastructure projects. They wanted to create a solution that could detect the different types of wires that were present in the thousands of images that they and needed them to be accurately annotated before using them for wire detection models.

The challenges included acquiring good quality annotated data for training the model, ensuring the quality of data with consistency and accuracy, and controlling the cost of labeling data.

Data Annotation was a key part of the engagement and was a precursor to the supervised ML training.

• Labelme software was used to annotate the wires in the photos.
• Two different types of wires – transmission (red) and communication (green) were tagged.
• A total of 3000+ documents were annotated and converted to VOC and COCO formats, which can then be directly consumed by the AI models.

Indium’s streamlined process approach significantly reduced the effort taken to identify the different types

of wires by 40% and reduced the time taken for the entire data pre-processing activity by 45%. A high level of accuracy was achieved by employing effective quality control mechanisms, thereby minimizing human errors.

Indium can also help with automation and enabling self-service to free the IT teams of our clients. Our cross-domain experts understand the different needs of different industries as well as cross-pollinate ideas for developing innovative approaches to solve complex problems.

To know more about how Indium can help you with your data preparation needs to facilitate advanced analytics in your business, contact us now. To know more, click here: https://www.indiumsoftware.com/advanced-analytics/

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Security Concerns of Data Warehouse on Cloud

The agile and scalable data warehouse can be created within minutes, with real-time data to provide significant insights regarding new product development, detecting frauds, improving customer loyalty or optimal pricing. By taking it to the cloud, you can accelerate and simplify data warehouse development. While data management and integration are two critical aspects of cloud data warehousing, data governance and security are the two other aspects that need as much attention.

Your data warehouse is a wealth of organizational information right from financial to those related to customers and employees, credit card details and organizational trade secrets. This makes it vulnerable to cyberattacks from malicious outsiders and insiders. While cloud service providers may have security provisions, research shows that it is not completely safe and it is important for you to have data governance and security to protect your sensitive data on cloud as well.

Cloud data warehouse is vulnerable to four types of attacks:

• DDoS or Distributed Denial of Service where the servers are overwhelmed and unable to service genuine user request
• Data breaches due to unauthorized access
• Data loss due to deletion by accident, with malicious intent, or due to physical destruction of the infrastructure
• Visitors accessing your services through insecure points
• Timely alerts in case of potential threats

Data Governance for Greater Security

Insecure data not only poses a threat to data but also has the potential to tarnish your reputation, for customers to lose trust, compromises the safety of your employees and your business as well as a drop in revenues.

According to a Ponemon survey of more than 3,000 people in 507 companies, the data breach lifecycle in 2019 was longer at 279 days than that of 266 days in 2018. The malicious attacks lifecycle was longer by 12.5% at 314 days. While a lifecycle of more than 200 days cost companies $4.56 million, those shorter than that cost $3.34 million for a breach. The most common and costly causes of the breaches were malicious attacks at a per capita cost of $166.

This makes data governance and data security critical to protect data as well as ensure its integrity, availability and usability. Regulatory compliance is another reason why you need data governance for your cloud data warehouse. While protecting stakeholder interests, it also enables you to standardize your processes and procedures, improves your efficacy at a lower cost and enhances the quality of workflows and decision making.

Meeting Your Data Governance Needs

Indium Software, with expertise in Big Data technologies, understands that the key requirements of a good data governance are the clear definition of:

• Processes
• Roles
• Policies
• Standards
• Metrics

This enables establishing access rights, responsibilities and accountability to protect sensitive data and prevent it from falling into the wrong hands. It also ensures that the data is available consistently across the business with common terminology but is flexible enough to let individual business units retain it as per their needs. Most importantly, Indium ensures that all functions have access to a single version of truth that can provide them with deeper insights to develop strategies for their operations while aligning it with the organizational goals.

Indium’s endeavors to implement data governance includes ensuring greater data accuracy and completeness along with better data integration by creating a clear data map for data related to all key entities.

Indium also ensures that your data governance implementation complies with the guidelines provided by regulatory bodies such as the US HIPAA (Health Insurance Portability and Accountability Act) and the EU General Data Protection Regulation (GDPR) as well as industry requirements such as PCI DSS (Payment Card Industry Data Security Standards).

While having the policies that will govern the security and usage of your enterprise data is important, there is no silver bullet to protect your data. It will depend on your organizational needs and should be configured accordingly using the appropriate tools. It should be an integral part and an extension of your overall IT governance strategy for a smooth and seamless implementation.

Indium’s aim for implementing data governance, in short, is to establish a code of conduct and best practices for improved data management meeting legal, security, and compliance needs best suited to your organization and ensuring growth.

About Indium

Indium, a more-than-two-decades-old software company, has wide and deep experience in end-to-end delivery of Big Data solutions as well as in other cutting edge technologies such as AI, Blockchain, data analytics, application development and in the traditional platforms.

Our teams have cross-domain experience as well as expertise in the technology offerings. We offer high level services including consulting, implementation, on-going maintenance & managed services. To implement data governance and security in your cloud data warehouse, contact us now.

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