Data Execution to Data Governance

"Data is the new oil" as many business leaders, technologists and likewise entrepreneurs have been talking about these days. While statements like these are very legit in nature, but one caveat that needs to come along with such prophecies is that oil in comparison is finite proportion but data is something that will continue to grow in every organization like a hot spring.

Research houses from around the globe have been conducting studies to understand the impact of such growth in data, and have concluded that structured data will be growing at 40% on a year on year basis while traditional content and unstructured data being created by users will be growing at 80% annually. The overall outlook is that data around the globe is likely to touch 40 Zeta Bytes by the end of 2020. And that gets back to the thought that data is the new oil which is in constant supply across many oil wells around the globe but needs to be governed appearing to be in epic proportions that are only applicable to consumer-driven business models managed with strong a compliance and regulatory discipline.

" Data is a precious thing that will last longer than the systems themselves. "

- Sir Timothy John Berners-Lee

While these are great numbers which will raise a twinkle in the eye for a Chief Data Officer, there needs to be a clarion call of governing such data that is mightly contributed from users like you and me. The European Union has been at the forefront of having to build a framework of data governance much to its strong and forward-looking mindset of ensuring the privacy of the user remains sacrosanct.

From the other side of the pond reaching the shores of the big apple to the silicon valley, a similar framework has been outlined to protect data privacy. India is not far behind when it comes to such measures with Personal Data Protection Bill, a policy framework which is similar to the likes of General Data Protection Rules (GDPR) and the California Consumer Privacy Act (CCPA). Although the Government of India is yet to pass the bill and turn it into law, other countries are in multiple stages of defining a clear legislative mandate when such laws come to your doorstep. Are companies ready to align? While governments are at defining the framework, not many organizations have a strong data governance model that will act as an overarching template and process that can be replicated as the best practices principles.

In order to assimilate data collections in a profound manner, there are fundamental questions that need to be answered from the executive leadership, management teams managing data and to the frontline user who is asking for data that can be stitched into the service or product. These questions are Origination, Purpose, Criticality, Accessibility and Accountability of data. While these are simple yet an impactful way of moving ahead to harness the power of data that originates and is being captured at source, accessed, managed to be profiled, cleansed to make sense, augmented to talk business, and aggregated for purposes that leads to developing strategic initiatives based on data patterns and insights. That is a lot of words and sounds like a tongue twister if I had to present to a potential leader, well these are catchwords that everyone talks about but none have actually dirtied their hands for a bespoke implementation.  

While the above may sound fancy, for the data officer its more of a nightmare to manage data from proliferated origination and siloed sources of data residency. Thus, leads to the model of having to build a strong data governance mandate that not only emphasizes the need to bring about discipline but also make it a seamless experience for data management teams that include data engineers, data scientists, and analytics groups. The governance model benefits not only the organization but also set the trend that user data is managed and augmented with the utmost respect to compliance and regulations, leading to an acceleration of acquisition, transformation and processing of complex data. 

Ability to bring in a standardization protocol within the organization by building a central repository of customer profiles, and to be worth its salt the data officer should engage with stakeholders to align on a master data governance framework consisting of policies, and practices that can be implemented across business groups. With such legitimate lines that need to be drawn using data, how is that we should be looking at to build a full-scale data governance model? Following are some thoughts that I think we should be looking towards -

Framework - Be it a photoshoot for your next social media post, your quarterly visit to a doctor, intention to raise capital for business expansion, or going after those promotions either online or offline at the nearest brand store in your vicinity. All of these use cases have a specific need, want and expectations when it comes to originating, processing and delivery of results using data. These are the outcome of the following key persona touchpoints of data within or externally to an organization -

• People - Data Owners, Customers, Custodians, and sometimes data stewards. These are technologists who would be contributing to such a persona along with data creators who mostly are end-users and consumers of the company's product, service or information.
• Process - Developing a standardized definition and reusability of a process that drives like oil on a cogwheel setup, where data quality management is maintained effectively with a strong emphasis on SDLC management when new or updated data starts to infuse within the overall scheme of master data management
• Technology - There is no fire without smoke as it goes, and in order to get the right solutions in place (with no specific reference to any kind of application) the technology stack should address the metadata quality, with regular security audits to meet masking and reference data for compliance and regulatory purposes
• Execution - When there's fire someone needs to get it doused with the right equipment. Correct? Execution is key where a clear scope and mandate is tabled and monitored as a KPI across the organization with a centralized or federated way of taking ownership of data using sustained funding and budgeting to meet potential needs and redressal

The next step is to have everyone's skin in the game to define a well-calibrated organizational structure with each group having a specific outlay of KPI's that are measured and monitored, this would go a long way when the organization works in a harmonized manner looking like a well-defined orchestra playing in tandem to the tunes of both business strategy and associated initiatives. So what could be an organizational structure that we should be looking towards? -

• Front Line Group - These are the foot soldiers of the data governance organization, responsible for understanding data models, tracking usage and maintaining sanctity. The group makes the availability of data that is accessible, with high performance and recoverable during mitigation of potential risks
• Program Management Office - These individuals in the PMO are those who drive us to reality, the purpose of having this organization is to drive engagement across stakeholders and collaborate with teams to manage and measure timely execution of data-centric initiatives across the organization
• Data Standards Office - The custodians of developing and maintaining data based on operating procedures and policies. Appropriate determination of usage and permissions that span across business and technology, the DSO will need to address data issues and perform surgical steps that ensure correctness to its name.
• Governing Council - This team sounds like an offshoot of a typical governmental setup but has a connotation of having the ultimate business authority towards data on governance aspects, initiate investment recommendations for executive management approvals on data definitions, guidelines, policies, and associated cost impact
• Steering Council - This team would need to act like a pro-bono consulting organization that's made up of leaders who set strategic direction, deploy funding and resources. The efforts of this team should also drive prioritization of financial data capabilities that could speed up the adoption of data-led initiatives across the organization

When strong orchestration of governance models across the organization is in play, it helps in building out a successive outcome and benefits that not only help to attain greater trust and confidence with customers. But also translate into higher acceptance of its products and services, where transparency, traceability of data is authentic in nature. There could also be wider accountability of data ownership with multiple teams, it encompasses the need to be agile and compliant towards regulations that help in building a strong security perimeter with data leading to reliable insights on the business proposition of the organization.

In conclusion, it is yet to be noticed on how strong and effective governance organizations are built where accountability and ownership is the epicentre of quality information being used for analytics delivery to both internal and external customers. While governments around the world work on building a more consistent policy framework, consumption of authoritative data that certifies the authenticity and accessibility are a worthwhile opportunity that companies around the globe should tap into and fill existing gaps.