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Data Governance for the Digital Age —Part 1: A Paradigm Shift


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The World Economic Forum estimated that by 2025, the world will produce 463 exabytes each day – That is 463 followed by 18 zeroes!

The proliferation of data means organizations must ingest a larger volume of data from a wider range of sources and use them in more innovative ways to stay competitive. This trend is driving an industry of big data solutions from data quality and storage tools to advanced analytics engines and visualization software. Statista projects the market for big data solutions to increase from $42B in 2018 to $103B in 2027.

Proliferation of data and data tech creates a challenge to the traditional hierarchical data governance model where IT oversees data and technology in general. In this model, all data and tech requests must go through one department. With an increasing number of data and technology requests, this bottleneck creates inefficiencies, and engenders data fragmentation that creates compliance risks, data security risks, and other risks.

To address this challenge, organizations have been updating their data governance framework. Instead of reinforcing a centralized role (IT), businesses are aspiring to achieve a more decentralized organizational structure when it comes to data and technology. This structure can support democratization of data, allowing different teams the access and control of the data they rely on. This type of governance model not only frees up the IT bottleneck, but it also empowers members of your organization to drive data and tech dependent innovations.

data_governance_part1_IT_model_W800px.pngForbes describes data democratization as a state where “everybody has access to data and there are no gatekeepers that create a bottleneck at the gateway to the data.” The article continues, “The goal is to have anybody use data at any time to make decisions with no barriers to access or understanding.” Removing friction from data utilization accelerates insights and improves responsiveness, attributes that are critical in challenging times like the Covid-19 pandemic.

Hierarchal Model: The historic positioning of the hierarchical model added to its deep roots in across industries will result in many firms arguing for its ongoing usage. There is consistent messaging and cohesive control over data quality, software, and costs when all data flows through IT. Moreover, a simple top-down model may genuinely be the best approach for smaller organizations. In addition, unwinding this model may be cost enough in time and resources to be viewed as unrealistic – discouraging pursuit of a newer, more flexible model.

A large and complex organization is likely to have such divergent software and data warehouses that integration and upgrades across the board often have negative impact to other systems, requiring years-long investigations before implementing what should be straight-forward enhancements. This is the reason a recent survey found that one in three companies still have Windows XP running on their computers.

This lack of flexibility and the wide distance between data and its end users result in increased risk to the organization. Using outdated software exposes firms to cyber-attacks and data breaches. The long journey for data to becoming actionable information and strategy sometimes grinds decision making to a halt.

Decentralized Model: A decentralized model, on the other hand, allows for maximum flexibility. While it requires a change in deeply held philosophical approaches to business structures and information flow, its benefits are significant. It allows for rapid resolution to end-user requirements, timeliness in responding to industry changes, rapid scalability and operationally driven data definitions which support quick fixes to issues and more nimble and long-lasting data accuracy.

However, having non-IT members managing the organization’s data increases the risk of data quality issues. Decentralizing data governance requires an effective change management approach. Driving the ability to empower staff with information and respond to needs as they arise is a path that must be clearly and decisively chosen. The appetite must begin with the decision-makers to delegate and support dissemination. To go down that road, there are three critical changes your organization needs to manage to address the risk of losing control of your data governance.

The following parts of this series will examine these three areas of change management:

Part 2: Organizational Structure, Evolving Roles and Responsibilities: Data roles and responsibilities will have to evolve and expand across the organization

Part 3: Collaborative Data-Driven Culture: There needs to be a strong collaborative data-driven culture

Part 4: Technology for Democratized and Collaborative Data Governance: Technology needs to be updated with the focus on supporting the decentralized data governance model

About BaseCap Analytics: BaseCap’s Data Quality Manager is a data quality control platform that supports a collaborative data governance. Its intuitive user interface allows non-technical individuals to be data stewards. With no user limit and customizable permission levels, an organization can leverage the Platform to address all data issues across all its teams.

Contact us to see how the Data Quality Manager will become a critical component your organization’s data governance program.

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