Why data governance?
Data governance may not seem to be the highest priority and it used to be a nice to have, but in a context with increasing focus and importance of data and analytics, it turned into a must have for any organization that wants to enable business value from data.
Data governance is at the heart of any data related discipline or initiative. Why?
For one reason, data governance provides the foundation for all of them.
Most organizations that are planning initiatives like updating its data warehouse to enable near real-time data, creating a big data environment to support analytics, increasing digital capabilities, migrating data and analytics to the cloud, master data management, business intelligence, big data analytics, machine learning or artificial intelligence. it’s important to understand that data governance is a key component for the success of any of these initiatives.
Most organizations are struggling to address issues that have always existed:
- Increasing data volumes from more and more sources.
- The gap between business and IT, leading to often contradictory strategies.
- Difficulty in managing a siloed data ecosystem.
- Difficulty to identify and define data across sources.
- Lack of standard business and data management rules and data protection policies.
- Rising data-security concerns around providing employees with remote access to data.
- Difficulty to identify, cleanse, standardize, and curate data for sharing.
- Existence of duplicate, erroneous, inaccurate, and incomplete data.
- ·Negative impact of regulatory requirements, either data protection regulations, or industry regulations.
All these issues have one common denominator: data. And independently of how and organization is managing its data — data warehouse, data lakes, big data, etc., data governance is critical.
Most importantly it will impact on the decision processes, enabling better decision making, decisions that are not based on poor quality, or inaccurate data, because data governance will allow a structured and proactive approach to data quality, instead of a reactive approach of constantly cleaning or fixing data, resorting to numerous workarounds, without never consistently addressing the root causes of bad data.
It’s also important also to understand that the impact of data governance goes beyond the boundaries of data, it will impact every business process that uses data and that have direct impact on day-to-day activities, impacting customers, production, etc.
From day one, data is being created, compiled, collected, stored, and distributed. Data is present in all the organization’s processes, from risk or regulatory compliance to routine operations.
Data can be the most powerful asset an organization has.
Organizations are investing heavily in leveraging new technologies, artificial intelligence, machine learning the internet of things, augmented and predictive analytics, and data is at the core of each of these initiatives.
They are fully dependent on data; all are aimed at providing quality data that is essential to improving insights and driving substantiated business decisions.
Often seen as a labour-intensive, time-consuming process that can spread across long time frames, it is easy to conclude that, when most organizations are working in reactive mode, this is the worse time to engage in such initiatives.
With all the technological advances and with larger volumes of data available organizations can increase their competitivity and earning potential, but also to highlight existing operational inefficiencies and fail to rise in an increasingly competitive business environment.
This is where the capacity to know what data the organization has, where and how it is held, and the ability to protect the integrity of that data, is a critical advantage.
Organizations need to have a clear stand on safeguarding its most important asset — data. And as for any other asset this means to define the processes and procedures by which their data will be managed.
The goal of data governance is to ensure that an organization’s business objectives are accomplished, by guaranteeing that data is available as needed for business purposes, but also secure, private and in compliance with regulatory requirements.
There is no one-size-fits-all approach to data governance and specially in the present moment — when organizations are being pressed for results or to quickly adapt to cope with a rapidly changing business environment — a more pragmatic and agile approach is paramount.
Operationalizing data governance can take weeks or even months, so, it needs to scalable, sustainable and ensure short term results.
As any other asset in the organization data’s purpose is to create value, so any data strategy must be oriented towards the organization’s strategic priorities and key business objectives, with that in mind, start with a mission and objectives that focus on an initial area.
Engage the right stakeholder, those who know the impact of data in their business processes and start by establishing a data governance charter and policies and turn start implementing them in focused, objective initiatives, ensuring across the organization there’s visibility of the progress.
Make data governance a part of the day-to-day activities, creating routines and keeping them as simple as possible. One of the objectives of proper governance must be to improve efficiency and not increase bureaucracy, avoiding the pitfall of complex processes and heavy organizational structures.