Enterprises are evolving their businesses, putting pressure on data to satisfy new use case and business ecosystem through insights and learnings. Adding to this business complexity, market disruption, and demand for speed, today data governance is at front and center to make this new currency trusted, secure, and relevant. A business evolution and sustainability depend purely on how they operate and deliver to customers. Data’s definition, information, and predictions are used to penetrate new markets, control costs, drive revenues, manage risk, and help discover alternate revenues sources across the world.
Today technology like cloud has empowered users to collect, capture, store and analyze data at lightning speed and promoting instant decision making. As adoption of cloud computing continues to grow, information management stakeholders like Chief Data Officers (CDO), Manger Data Governance, Data Analyst, Data Engineer, and the spectrum have time and again raised questions about the potential risk involved in managing this data landscape explosion.
Business Executives Pain points
Lack of data assets visibility and control : Data management professional and data consumers many a time lack visibility into their own data landscape. Which data asset are available, where those assets are located and who has access to the data or whether they should have access to it. These uncertainty limits the further leverage of data impacting productivity and value.
Data Quality : Different consumers will have different requirements with respect to Data. Chief Data Officers (CDOs) purely depend on precise data access to enhance decision-making, while data analysts seek cleaner and more reliable data for their analyses. On the other hand, data stewards and data governance managers focus on aligning data assets with business requirements, overseeing data quality across the organization. Ensuring data accuracy, completeness, consistency, and reliability is of utmost concern to any organization seeking valuable insights and informed decision-making.
Regulation and compliance : There is a growing set of regulations, including the European Union’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA) and industry specific standards such as global Legal Entity Identifier (LEI) numbers in the financial industry, The Health Insurance Portability and Accountability Act (HIPAA), in health industry, and ACORD data standards in the insurance industry. Business may have concerns about oversight and control of data. With these regulations in place, businesses are rightfully concerned about maintaining oversight and control over their data. The stakes are high, as data breaches or mishandling of sensitive information can lead to severe financial penalties, reputational damage, and loss of customer trust.
Securing the data : Large enterprises that typically deploy their system on – premises, expect tight security. With a significant number of security threats and breaches in the news, businesses are concerned about the volatility. These factors contribute to concerns for protecting against unauthorized access to or exposure of sensitive data, ranging from personally identifiable information (PII) to corporate confidential information, trade secrets, or intellectual property.
Data adoption : Automating data onboarding and collaboration are crucial concerns especially for CDOs and Data Governance Managers. Accelerated data onboarding ensures timely deployment of new use cases, enhancing agility and innovation, streamlines workflows, enhancing efficiency, and reducing human errors. Meanwhile, the collaboration portal fosters seamless communication among stakeholders, aligning data governance efforts with business objectives, driving data-driven decision-making, and enabling collective insights for organizational success.
Data Governance Management : Data security, data protection, data accessibility and usability, data quality and other aspects of data governance will continue to emerge and grow as critical priorities for any business. And as more organization migrate their data assets to the cloud, the need for auditable practice for ensuring data utility will also continue to grow.
Data Maturity : Enterprises rely heavily on data to make informed decisions, gain a competitive edge, and drive business growth. Ensuring the quality, security, and effective utilization of data is no small task. One significant challenge faced by any business today is the level of data maturity they have achieved in their data governance practices. Today business face data maturity challenges due to complexities in managing vast data volumes, data silos hindering collaboration, the absence of a well-defined governance strategy, compliance and data security concerns, data quality issues affecting decision-making, and the need to foster a data-driven culture for successful governance. Overcoming these obstacles requires accurate data assessment and targeted strategies to elevate progress in data-driven decision-making.
These growing concerns clearly highlight the need to hyper focus on data assessment, cataloging of metadata, access control management, data quality, and information security as core data governance competencies that SciKIQ provides along with continuous upgrade in a transparent manner.
Enterprises need to rethink about data governance comprehensively, from data intake and ingestion to cataloguing, persistence, retention, storage management, sharing, archiving, backup, recovery, loss prevention, disposition, and removal and deletion. As more organizations migrate their data assets to the cloud, the need for auditable practices for ensuring data utility will also continue to grow.
ScikIQ’s data governance practices revolve around its distinctive set of features
The platform provides a framework that enables individuals to define, agree to, and enforce data policies. Additionally, it implements effective processes for controlling, overseeing, and stewarding data assets, covering on-premises systems, cloud storage, and data warehouse platforms. To ensure compliance with data policies, ScikIQ leverages appropriate tools and technologies, facilitating the operationalization of data policy enforcement. Through these comprehensive measures, ScikIQ demonstrates a strong commitment to maintaining a robust and efficient data governance approach.
Data Discovery and Assessment : ScikIQ’s discovery feature effectively addresses risks associated with cloud-based data lakes, preventing ungoverned migration of data assets. This feature enables the identification of data assets within the cloud environment and facilitates tracing and recording each data asset’s origin, lineage, applied transformations, and object metadata.
Data Classification and Organization : Our platform assists in profiling and classifying sensitive data, facilitating the identification of appropriate governance policies and procedures for effective data governance implementation. By thoroughly evaluating a data asset and scanning the content of its different attributes, the platform helps categorize the data asset for subsequent organization.
Data cataloguing and metadata management : Our platform helps maintain a data catalogue that contains structural metadata, data object metadata (often this metadata describes the demographic details, such as the name of the creator, the size of the object, the number of records if it is a structured data object, or when it was last updated), and the assessment of levels of sensitivity in relation to governance directives, such as compliance with one or more data privacy regulations. Once your data assets are assessed and classified, it is crucial for organizations to document their learnings. This documentation allows communities of data consumers to have visibility into the organization’s data landscape.
Data quality management : Due to the diverse data quality requirements among various data consumers, it is essential to offer a method of documenting data quality expectations, along with providing techniques and tools to support the data validation and monitoring processes. ScikIQaids in quality management processes, encompassing the creation of controls for validation, enabling quality monitoring and reporting, supporting the triage process for assessing the severity of incidents, facilitating root cause analysis, recommending remedies for data issues, and tracking data incidents. Implementing the right processes for data quality management ensures the availability of measurably trustworthy data for analysis.
Data Access Management : ScikIQ ensures robust data security and proper access control by addressing both aspects of data access governance. Firstly, it enables the provisioning of access to available data assets through efficient data services, leveraging cloud platforms for seamless data access by consumers. Secondly, it ensures prevention of improper or unauthorized access with meticulous identity and access management. ScikIQ establishes a well-managed access structure by defining identities, groups, and roles. It seamlessly integrates identity and access management services, allowing businesses to define roles, specify access rights, and manage access keys. This guarantees that only authorized and authenticated individuals and systems can access data assets in accordance with predefined rules and protocols.
Auditing : ScikIQ is a platform that assists business in evaluating the functionality of their systems to ensure they operate as intended. By facilitating monitoring, auditing, and tracking (who did what and when and with what information) it helps security teams collect data, identify threats, and act on those threats before they result in business damage or loss. Performing regular audits is crucial for assessing the efficiency of controls, enabling swift threat mitigation, and evaluating the overall security status.
Data Governance Maturity assessment : Enterprises heavily rely on data for informed decisions and business growth. Ensuring data quality, security, and utilization poses challenges due to complexities in handling vast data volumes, silos hindering collaboration, and compliance concerns. Addressing data quality issues and fostering a data-driven culture are essential. ScikIQ’s Data Maturity Assessment aids organizations in gauging their data management and governance maturity, providing insights for internal benchmarking, competitor analysis, and industry evaluation, guiding progress towards data-driven decision-making and market leadership.
Data Privacy Management : ScikIQ’s data platform is tailored for enhanced data governance and management, offering actionable insights. Integrating ScikIQ enables business to establish internal data security controls, mitigating data breaches and ensuring compliance. While perimeter security alone is insufficient to protect sensitive data from insider breaches or exfiltration, ScikIQ promotes the adoption of additional measures like encryption at rest, in transit, data masking, and permanent deletion to safeguard exposed data effectively.
Regulatory compliance : Regulatory compliance demands auditable and measurable standards and procedures that ensure compliance with internal data policies as well as external government regulations. Migrating data means that organizations need tools to enforce, monitor, and report compliance, as well as ensure that the right people and services have access and permissions to the right data. With ScikIQ’s data governance framework, organizations can embrace the changing regulatory environment instead of simply reacting to it.
ScikIQ’s data governance platform has evolved as per today and tomorrow’s requirements from a cost centric and compliance prospect to a key element in propelling business growth and innovation. Data governance solutions is about technological advances that is a continuous, autonomous, and virtuous cycle. This is an ecosystem – a community in which data is used for greater good leading to accelerate growth ethically. Executives looking to use data as an asset and deliver positive business outcomes need to rethink governance’s role and adopt the modern and transformative approach which ScikiIQ is efficiently designed to provide. The platform has been benefiting multiple global conglomerates.
The data fabric platform is based on a no code principle for quick integration and customization as per business needs. ScikIQ ensures data is the cornerstone to any business’s resilience, elasticity, speed, and growth opportunity and not an afterthought.
Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy, Jessi AshdownData Governance: The Definitive Guide. People, Process and Tools to Operationalize Data Trustworthiness. March 2021.
Fisher, Tony. The Data Asset: How Smart Companies Govern Their Data for Business Success. Wiley, 2009. Print.