Data governance is all about increasing the understanding of data across a business and encouraging team collaboration to get the most from their data assets. Analytics play a key role in helping organizations maximize the value of their governance strategy. Here’s how you can become the master of your data governance strategy. 

A quick intro to data governance  

Data governance essentially refers to the people, processes, and technology an organization utilises to manage its use of data. 

A solid data governance strategy is key to all organizations that work with data. With the overall goal of enabling an organization to achieve its financial objectives, data governance encompasses a range of processes and policies that ensure the effective and efficient use of information. By establishing the procedures and responsibilities that ensure the quality and security of the data used across an organization, it defines exactly how data can be leveraged to drive business benefits from consistent, common processes and responsibilities. 

Data governance covers the wide scope of teams, processes and technologies needed to manage and protect an organization’s data assets— all with the aim of ensuring that corporate data is clear to understand, complete, trustworthy, and privacy-compliant. 

Boiled down to the essentials, it’s about establishing methods, and an organization with clear responsibilities and processes to standardise, integrate, protect and store corporate data. As a continuous and ongoing business project, the key goals involve establishing internal rules for data use, improving internal and external communication, increasing the value of data, reducing costs and minimising risks by implementing compliance requirements. 

What are the advantages of analytics-enabled data governance? 

With analytics-driven data governance, companies will improve data quality and reliability across the board. Engaged users who trust and understand data are far more likely to turn to analytics for business intelligence and insight. This not only boosts understanding of data across the business but encourages teams to strive to get the most from their data assets. Data-savvy users are also better placed to uncover unseen issues and potential opportunities. 

What are the 3 essential pillars of data governance to manage your analytics? 

Analytics-enabled data governance can be distilled down to three key dimensions: data quality, privacy compliance and democratization (making data and insights accessible to a wider range of roles within a business). They act together to form a virtuous circle, where strong governance shores up the analytics, which in turn drives teams, customers, brands, consumer trust and the entire business. A lack of effective governance has the inverse effect and puts the entire system at risk. 

 
1. Data quality: The importance of solid data collection processes to maximize data availability and actionability.

Data coherence: Data processing and usage aligned across the entire organization guarantees data of the highest possible quality. Data that is coherent is data that is the same across the network — data on the server and all the clients is synchronized. Look for data coherence with our RAPS approach that touches the below four areas: 

• Reliability: Look for data tolerance and continuous operation to support the data availability demands necessary in mission-critical business environments. 
• Availability: Maintained throughout the enterprise and across all departments and profiles, coherence provides real-time availability of data that can support the organization’s continuity of operations and disaster recovery requirements.
• Performance: With real-time data processing, organizations can considerably increase the performance and scalability of calculations. This enables them to leverage valuable data-driven insights in context and at speed. 
• Scalability: Coherence enables organizations to scale in a linear and dynamic way to improve the use of resources, offering a straightforward approach to increasing the effective capacity of shared data sources. 

Capacity management:

This is the broad term describing the key monitoring, administration and planning actions taken to make sure a company’s infrastructure has adequate resources to handle current and future data-processing requirements. This includes the optimum tools, as well as personnel resources, time and budget, designed to maximize the quality of the data. Data minimization also has a valuable role to play in reducing any pressure on data processing capacity.  

Effective capacity management can also allow more effective purchasing to handle future growth and more accurately anticipate needs. By constantly monitoring infrastructure and processing, it’s possible to avoid any potential issues such as bottlenecks or potential equipment failure.  

Reliability/data checkpoints (verification/correction):

To leverage the full value of your data, it’s vital to be able to act and align collected data with your team’s and company’s objectives. To do this effectively, you need to ensure that the data is as reliable as possible. 

Through practical everyday data governance, you can maintain quality control and data reliability over time. This includes practices such as effective incident management, regular correction, validation and verification. It’s therefore essential to employ adequate tools AND processes, including: 

• Regular quality control 
• Crawling checks 
• Alerts 
• Managing 
• Retro degradation tests 

Strategic alignment: To make sure that your data collection is at the service of your strategy and in line with your business objectives/KPIs, it is vital to adopt the best tools available. 

Effective strategic alignment will ensure that an organization’s structure, use of resources and internal culture entirely supports its strategy. Contributing to improved performance by optimizing the operation of processes/systems, and the activities of teams and departments, it not only encompasses technical and functional activities, but also issues relating to human resource management.  

 
2. Data compliance, security and privacy - Regulatory compliance, data security and privacy protection are interrelated and critical functions of any data governance regime  

Auditing the data: Compliance with data protection regulations is top of the agenda for companies in 2021. With business decisions increasingly driven by analytics, it’s critical that teams have full confidence in the privacy-compliance of their organization’s data. This is where data audits come into play:  

• What data are you collecting? 
  A data protection impact assessment (DPIA) is a privacy-related impact assessment whose objective is to identify and analyze how data privacy might be affected by certain actions or activities. Under the GDPR, data protection impact assessments are mandatory in certain cases, such as when profiling activities are carried out using personal data. 

Executing this data mapping/auditing exercise with your data privacy officer allows you to form a precise definition of personal data, and for what purposes the data is used. It also allows you to nurture your organization’s data privacy culture and ensure teams have adopted a privacy-driven approach. 

• How critical/relevant/granular is the data? 
  To understand the need for consent, it’s important to understand how the data is collected and managed. The higher the relevance and granularity of the data, the easier it is to ensure privacy-compliant governance. 

Privacy control capacity: Another important aspect of regulatory compliance is ensuring that you have the appropriate capacity to measure privacy control and impact of the procedures you put in place. This involves having:  

The available tools and resources to ensure adequate data privacy control and that the data is effectively protected and managed over the long term 
The capacity to handle evolving regulatory changes 
The ability to display that you can provide these resources — and that a project can be delayed or abandoned if not — as well as the ability to respond to requests for deletion of data 

Adequate internal awareness/training: To make sure that regulatory compliance, data security and privacy protection are at the heart of your data governance strategy, it’s key to build and propagate an organization-wide culture of privacy. 

It’s obviously vital to stay up to date on and adapt to ongoing privacy regulations to avoid the financial cost and brand impact of fines for a data breach. However, when cultivating an internal privacy culture, the goal is more than just achieving legal compliance. It’s equally important to place a focus on how personal data supports other business objectives. To do this, you need to look at privacy and data governance in terms of customer expectations, organizational ethics and strategic initiatives, as well as regulatory obligations. 

Implementing privacy culture across the organization needs to come from the top down. Privacy champions can also act as representatives in areas impacted by data use and help promote overall privacy awareness.  

 
3. Data access/democratization: Companies must determine who needs what data and ensure its accessibility to anyone with legitimate need.

• Team access to actionable data: The first step when approaching democratization is ensuring that specific teams are given access to data to be able to use it effectively. This includes providing specific access based on teams’ maturity to extract the maximum value from the entirety of available data.
• Simplicity of access: In the drive towards full democratization, it’s also essential to keep access to relevant data standardized, fluid and simple across the organization. Having a single point of data collection that can then address a range of different profiles and business needs limits the analysis time, overall usage of resources and risk of error. 
• Avoid loss of control over data: Hand in hand with maximizing data usage, it’s key to maintain full control over your data and avoid possible leaks. Data leakage can come from factors such as generic email addresses and teams working in silos. Using a range of data collection tools compounds the issue with the vastly increased likelihood of human error compared to using a single provider that can address all company profiles and needs. Using several tools superimposed as a single suite also carries the risk that the data will be badly inadequate to real business requirements.  
• Adaptability to needs: It’s important to make sure the data flow is adapted to the real needs of the organization as well as operational requirements of each department. When the data is fully available, it not only keeps teams informed, but allows them to explore ways to drive value and create new possibilities. And the more the different business units in your company are consuming data, the more they will optimize its use. 

Data governance comes down to managing flows, both data and business needs. You can’t run effective data governance with a static approach. The needs and levels of governance and those implementing it are constantly changing, so you need to remain nimble.  

Analytics-led data governance can unlock the full value of your data by helping you: 

• Increase user trust in your brand 
• Boost employee productivity and collaboration 
• Provide faster access to critical business information 
• Cut costs from reduced data storage 
• Reduce risk of data breaches and mishandling of sensitive data (improved regulatory compliance) 
•  Harness emerging technologies in business intelligence 

Implementing state-of-the-art data governance is the ultimate way for organizations of all sizes to increase efficiency, save money and reduce risk.