Building an Effective Data Analytics Strategy with Governance That Delivers Value

Apr 19, 2022
Data Analytics | 6 min READ
Today's success of digital-enabled businesses largely hinges on modern data management and analytics. Without data, there would be no digital transformation to keep up with the competition and no new insights about up-and-coming sources of revenue. But again, for data to power the initiatives at an enterprise level, it must be relevant, usable, secure, accurate, and readily available. In that sense, adopting data governance is a critical first step for all organizations that are accountable for their IT assets.
Ravi Gunturu
Ravi Gunturu

AVP, Chief Architect

Data & Analytics


What Does Data Governance Mean?
Data governance serves as an umbrella that covers all data management policies and procedures. In terms of definition, it is the principled methodology we follow to organize and structure data within a company, starting from acquisition to disposal. With it, we define the ownership of data and the policies that drive data retention and security within an organization.
The Need for Designing Data Governance That Delivers Value
Ineffective data governance services plague most organizations across the globe as the higher management fails to recognize the value-adding potential of this approach. Any company that doesn't implement proper data governance & analytics policies faces issues with compromised analytics and a lack of reliable information. As a result, they largely miss out on opportunities that optimize operations and open new revenue streams. With that in hindsight, here are some of the major benefits offered by a data governance approach for value:
Stay Ahead
Visit our Data Analytics page
It helps make more streamlined decisions
Users within the organization need access to data to provide service to the customers and further improve the product. Data governance helps deliver it.
Greater cost controls
We need data to manage our resources more effectively. We can eliminate duplicate data caused by information silos and prevent over-purchasing with data governance.
Increased compliance
Regulations have gotten stringent of late, making way for increasingly robust data governance models that help avoid risks associated with non-compliance.
Greater Trust
Data governance ensures that both external and internal data policies are compliant and auditable. This furthers the trust developed with customers and stakeholders to ensure that their sensitive information is protected.
Intersecting Data Analytics and Data Governance
The era of big data hasn't been easy to accept for most, and companies haven't had much of an option other than taking a reactionary approach to data management. As a result, innumerable businesses are reeling under data exhaustion.
To alleviate problems such as this, we must find a way to make the best of seemingly disparate data governance and data analytics software and tools. We say this because while data governance serves as the foundation for a larger data management program, it's only through an analytics platform that an organization can boost their governance efforts.
Analytics-driven data governance
Data governance enables organizations to leverage their data as an enterprise asset. Therefore, it is crucial to understand similar data assets across an enterprise, especially when its key components include business glossaries, data dictionaries, and metadata management. With the data governance paradigm, we assign data owners and stewards to bring accountability into the picture and thus ensure data quality.
When we introduce analytics into the scene, machine learning algorithms can help improve the quality of data across the enterprise and improve themselves as it goes forward. For example, with data governance and AI leveraged together, organizations can ensure increased data utilization for analysis and make the data more reliable. Moreover, with analytics, organizations can proactively pinpoint areas where they may violate certain regulations, like the General Data Protection Regulation (GDPR).
Data governance enhancing analytics
Data governance has an equally relevant role to play in enhancing analytics within an organization. It is meant to encourage collaboration and improve data understanding across the enterprise. To that end, it creates educated users who will then turn to analytics to solve business issues more efficiently. Moreover, metadata management, an important aspect of data governance, is key to predictive analytics for deriving business insights for growth.
Building an Effective Data Analytics Strategy with Governance That Delivers Value
Data Governance in The Cloud
Cloud analytics and computing are witnessing unprecedented adoption at the enterprise level. This raises various concerns related to data governance. For instance:
  • Visibility and control - The Google Cloud Platform (GCP) has the ability to help enterprises with their data governance needs. It offers businesses various analytical tools for data assessment, access control management, and metadata cataloging. These major tools include Data Catalog, BigQuery, Data Loss Prevention, and Cloud IAM.
  • Data security - There's still a huge misconception about the security of data stored in the public cloud, and some businesses still prefer on-premises solutions instead of adopting the cloud. The Google Cloud Platform is a safe bet, and it protects its users' data against breaches and unwarranted exposure.
  • Compliance - Enterprises need to ensure whether their cloud vendor is adhering to the set regulations and standards for data governance. To that end, the Google Cloud Platform recognizes and adheres to HIPAA, PCI DSS, CCPA, and GDPR, among other regulations.
5 Tips on Driving Data Governance Excellence
  1. Reimagine the organizational designVisibility and control : Digital literacy is a key component for a company that wishes to excel in data governance. The IT department can't be burdened with the sole responsibility of cloud data analytics and governance, and the entire organization must come together to pinpoint priority data assets and define policies necessary to foster maximum value creation with accelerated advanced analytics use-case delivery.
  2. Getting the C-suite approval : Any initiative or project is bound to falter down the line if it doesn’t have unwavering support from the leadership chairs. Hence, it’s the responsibility of the Chief Data Officer (CDO) to make the C-level management understand the needs of the company at the ground level and how data governance alleviates the core issues of the business. It shall also help to demonstrate various tangible and measurable ways to track progress in terms of value creation and the time data scientists spend positioning priority use cases.
  3. Integrate primary transformation activities with data governanceFor companies working on projects involving rigorous data transformation activities, data governance shows the most value when linked together. For instance, data governance will be a major focal point if the company is already looking to digitize its workflows or modernize its resource-planning pipelines. Furthermore, it helps rally the senior management behind the program and the rest of the organization.
  4. Adopt a narrowed-down approach : Many organizations make the mistake of interpreting analytics data as a whole while implementing data governance. Adopting a broader scope creates the risk of spending time on low-priority data assets. As a best practice, it's important first to identify our data domains and allocate them to data stewards. Next, before running straight to execution, we must prioritize them on the basis of ongoing transformational efforts and anticipated potential value.
  5. Gain from a leaner and agile-inspired data governance process : Data governance programs are known to show great variance across different industries. For instance, banks rely on highly sophisticated models to comply with regulations like BCBS 239, while the regulatory pressure may be greatly reduced in different industries. Given that, it’s highly recommended to adopt data governance and analytics on a needs-based approach.
For any organization, adopting data governance is an essential first step to capturing value through various digitally transformative opportunities. It helps them manage costs, remain compliant with the regulations, and make their decision-making processes more streamlined. When leveraged along with data analytics, data governance can ensure better data quality and hence, its increased utilization across the enterprise. By following the above best practices for implementing data governance, we can realize quantifiable returns within a short period
Was this article helpful?