Necessity is the mother of invention - where Data Governance came from?

Year after year, the amount of data is increasing. The times when all the information collected in the company was obtained from only a few sources and stored in a structured form are irretrievably gone. There is more data, and the pace at which it piles up is faster than ever before, which is perfectly illustrated in the graphic below: 

Caption: Data growth across platforms, on average per minute in 2020.

Big Data technology seems to be the response to the problem of the increasing amounts of data from various sources, often stored in an unstructured form. However, the mere storage and processing of large data sets does not solve the problem highlighted during the internal organizational analysis – low quality and lack of confidence in their reliability.    

Data becomes useless if it does not carry information.  Data Governance originates from Data Quality. The dimensions of data quality, without which we cannot really talk about Data Governance, have been determined based on frequently observed problems.  Below are examples of data quality dimensions. 

What is Data Governance? 

Data Governance is a much broader concept than Data Quality itself. We don't measure corporate maturity in managing data-related processes just by how a company handles poor data quality.  

We are all familiar with two dimensions in any organization: the dimension of business processes and the dimension of systems. Often at business meetings there are sentences beginning with "According to our process..." or "In system X there are...". The implementation of the Data Governance practice introduces a new dimension in the organization – the data dimension. 

Data Governance is a new dimension of data in the organization, it is a number of changes that can have a positive impact on business goals in the long term: 

• Data accountability – identify data producers and users of data so that they can jointly identify flows, improve quality, and increase business value from data. 
• Data standards – developing standards for data together with previously appointed data owners (key data users) to use one unified business language and analyze aggregated data from different systems in the same format with as little manual effort as possible. 
• New roles – the establishment of dedicated roles, including Data Stewards, to properly manage data in the organization and effectively use Data Governance processes. 
• Ensuring security – when entering, storing and processing data and exercising control over accesses. 
• Building  awareness – increasing awareness of the importance of data in the enterprise affects the "data-driven mindset", which leads to strategic decisions based on data and analytics. 

A company's maturity in the area of data management is measured by Data Governance metrics. These metrics will provide the necessary contribution to monitor the implementation status and development of Data Governance in the enterprise. 

• The adaptation rate will measure the extent to which the organization addressed responsibility for data and whether it effectively implemented structural changes, 
• The success rate will measure the progress of work in the documentation of rules, standards and processes related to Data Governance, 

The performance factor will measure the extent to which the organization is able to manage the operational work related to the handling of daily Data Governance tasks.

Data Governance and Data Management 

We often encounter the use of these two terms interchangeably. Meanwhile, according to the DAMA  Data Management Body of Knowledge, Data Management is a broader concept than Data Governance. 

Signature: Relationship between Data Management and Data Governance

Deloitte Data Governance Framework

To properly implement the data dimension and then effectively manage their data, companies need to take care of several inseparable elements. Many years of experience in the area of Data Governance allowed Deloitte to develop a structured, repeatedly proven approach for the effective implementation and development of practice – Deloitte Data Governance Framework. The framework allows you to define all necessary elements needed to address the implementation of a data management practice. 

Each time we implement the Data Governance practice, we adapt our approach to the needs of the organization – taking into account the specificity of the industry, size of the company, ambitions and goals in the area of data management as well as  the level of technological advancement. In order to develop a tailor-made approach to the implementation of Data Governance, it is also necessary to understand business needs and problems occurring when working with data.

Deloitte Data Governance Framework structures the implementation of data management practice around six main areas:

• Data Governance Strategy 

As in any initiative in an organization, the implementation and maintenance of the practice requires a well-thought-out and sustainable strategy. It is necessary to properly "arrange all the blocks" so that each action has a specific goal. An extremely important element of the strategy is to understand business needs so that the Data Governance framework can support them. For this reason, it is important to remember that Data Governance is a business practice. An extremely important element is to design an appropriate communication plan adapted to each level of activity (strategic, tactical, operational). This will help achieve one of the  most important goals of the framework– build awareness that data in the company is one of the most important assets. In the course of the development of the framework, we should constantly review whether our strategy is still valid and continuously adapt it to the current business conditions and needs. 

• Data Governance Policies and Principles 

Policies and principles are a set of guidelines that ensure consistent data management and its proper. They are also a kind of guide that allows you to maintain and enforce Data Management standards and effectively use Data Governance processes.  It is a kind of Conduct for Data Governance.  Cyclical review of policies should verify that we staying on the chosen course chosen when creating the strategy to achieve our goals. 

• Organization and roles of Data Governance 

The implementation of a new practice is always associated with the creation and establishment of an appropriate organizational model adapted to the needs of the organization. In the case of Data Governance, it is necessary to appoint new roles such as Data Steward, Chief Data Officer, Data Architect or Data Quality Analyst (the number of new roles depends on the organization).  In addition to the roles located strictly in the Data Governance office, business roles involved in the Data Governance practice should also be identified in the organization: Data Owners, Data Users, Data Producers.  These are selected people from business teams who use or produce data in their daily work. The entire organizational model should be properly described and include regular meetings so that stakeholders at every level in the company can have a direct impact on designing the strategy and defining priorities for the development of Data Governance practice.

• Data Governance Processes 

Processes are at the heart of Data Governance practice. They enable efficient management and monitoring of the practice, allowing the effective use of data in the organization in order to achieve business goals. Properly designed and efficient processes allow employees to reduce the time for preparation and manual correction of data, while increasing the time for data analysis and conclusion-drawing/decision-making. 

• Tools and technologies supporting Data Governance

There are a number of Data Governance tools on the market – supporting both processes and people in creating a data catalog, managing data standards and documenting Data Lineage, in order to understand the flow of the data in the organization and identify areas of data redundancy. The preference of tools supporting Data Governance should always be based on an analysis of the needs and should be adapted to the organization's goals and ambitions in the area of data management. 

• Metrics of Data Governance and Data Quality 

Measuring progress in achieving Data Governance goals allows you to determine how effectively processes work and how successfully is the Data Governance strategy implemented. The second area to measure in is Data Quality. Constant monitoring of the quality of key data attributes that have a direct impact on business results is essential for the success of Data Governance.  Metrics identify the most important actions to be taken – data cleaning initiatives, system changes, changes in business processes or changes in the Data Governance strategy.

Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited (“DTTL”), its global network of member firms, and their related entities (collectively, the “Deloitte organization”). DTTL (also referred to as “Deloitte Global”) and each of its member firms and related entities are legally separate and independent entities, which cannot obligate or bind each other in respect of third parties. DTTL and each DTTL member firm and related entity is liable only for its own acts and omissions, and not those of each other. DTTL does not provide services to clients. Please see www.deloitte.com/about to learn more.
This communication contains general information only, and none of Deloitte Touche Tohmatsu Limited („DTTL“), its global network of member firms or their related entities (collectively, the “Deloitte organization”) is, by means of this communication, rendering professional advice or services. Before making any decision or taking any action that may affect your finances or your business, you should consult a qualified professional adviser. No representations, warranties or undertakings (express or implied) are given as to the accuracy or completeness of the information in this communication, and none of DTTL, its member firms, related entities, employees or agents shall be liable or responsible for any loss or damage whatsoever arising directly or indirectly in connection with any person relying on this communication. DTTL and each of its member firms, and their related entities, are legally separate and independent entities.