The Data Governance lead is responsible for the systems and rules used to ensure data is legal, well-organized, safe, accessible, and valuable. They are responsible for developing and communicating Data Governance policies.
Because Data Governance covers a wide range of responsibilities, a variety of people are involved, and the Data Governance lead manages how they organize and respond to incoming data. Data must be organized and “governed” because the massive amounts of data an organization quickly accumulates comes in a variety of formats, and in many situations must comply with certain laws and regulations.
Additionally, many advertisements for the position of Data Governance lead include being responsible for performing data analytics. Data analytics is the process of examining and interpreting raw data to answer questions and find trends.
Many of the processes and techniques of data analytics have become automated, using algorithms that transform raw data into information that can be understood by humans. That information can be used to improve customer relations and the efficiency of the organization.
There is a high probability the Data Governance lead will be creating, setting up, and implementing a Data Governance program, as a first phase of their employment. The second phase may very well include a shift to data analytics, while monitoring the new Data Governance program. It is also possible the advertiser is a brick-and-mortar business going through a digital transformation to begin doing business online.
Data Governance Lead Management Requirements and Qualifications
Advertisements for the position of a Data Governance lead are often a little fuzzy in terms of requiring a degree. Many say something similar to “A bachelor’s degree is desired,” but people with the equivalent education or related work experience are also accepted. Because there is a shortage of people with computer related degrees, employers have relaxed their standards and are willing to hire people with experience, even if they lack a degree.
This is a management position, and job requirements often include management skills, such as:
- Familiarity with the concepts and strategies of change management
- Excellent communication skills, both written and verbal
- Communication with staff at all levels within the organization (including upper management, and ability to make presentations)
- Ability to be a leader, develop relationships, provide direction and oversight, make decisions, and educate others
- Demonstrated ability to resolve complex problems/problem solving skills (examples should be cited in letter of intro and during interview
- A commitment to professional ethical standards within a diverse workplace
- An understanding of Agile and DevOps philosophies
- Promote the adoption of changes needed within the organization to support the new Data Governance program
- Demonstrated consulting skills (have advised customers/potential customers)
Data Governance Lead Technical Requirements and Qualifications
In terms of experience, employers often request a minimum of five years’ experience working with big data, Data Governance, or as a project manager. Clearly, employers want someone who can do the job.
Companies who want to hire a data lead look for experience in working with computers and understanding analytics. While managing people and communicating with staff and upper management are important, experience with data research and an understanding of Data Governance is even more important.
Technical requirements listed in Data Governance lead ads are:
- Knowledge of cutting edge Data Quality management practices
- An understanding of Data Governance practices and data protection
- An understanding of government regulatory requirements (Europe’s GDPR, California’s CCPA, and Brazil’s LGPD) and emerging trends and issues
- Ability to work in a fast-paced, constantly changing work environment and manage multiple priorities
- Ability to implement change initiatives related to Data Governance business processes and technologies
As mentioned earlier, many of the processes and techniques of data analytics have become automated. There are a variety of methods used for analyzing data, and these must be known in order to use and comprehend the automated analytics process, and its results.
However, based on their purpose and goals, there are four basic types of analytics models:
- Descriptive Analytics: Focuses on what has happened and what is currently happening to predict the future. Descriptive analytics uses both historical and current data, taken from a variety of sources, to describe the current situation and identify trends and patterns. This results in a form of Business Intelligence.
- Diagnostic Analytics: Uses historical data (often taken from an earlier descriptive analytics project) to discover the reasons for previous performances. Can be used to diagnose the reasons for both successes and failures.
- Predictive Analytics: Makes predictions by applying a variety of methods, such as forecasting, statistical modeling, and machine learning to descriptive and diagnostic analytic models. Predictive analytics is considered a form advanced analytics, and often uses machine learning or deep learning.
- Prescriptive Analytics: A form of advanced analytics using the application of testing, as well as other techniques; it offers specific solutions to produce the desired outcomes. For business, it often uses machine learning, algorithms, and business rules.
Consumer Privacy Regulations and Data Governance
Privacy regulations are evolving, and the Data Governance lead must have a good understanding the most well-known. The General Data Protection Regulation was established in Europe, in May of 2018. In the U.S., the California Consumer Privacy Act went into effect in January of 2020. In Brazil, the General Data Protection Law became effective in August of 2020. Each of these gives consumers certain protections and choices about how their data is used.
These regulations have forced significant changes in how businesses collect, share, store, and delete their data. Failure to comply with privacy regulations can result in fairly steep fines (possibly as much as four percent of the company’s global revenue). One organization was fined $180 million for data breaches that included payment information affecting nearly 400,000 people.
The CCPA is the strongest consumer-privacy regulation within the United States. (The U.S. still has no national data-privacy laws protecting the privacy of individuals.) The CCPA gives California’s residents the right to find out what data about them has been collected, and to block the sale of their personal data.
The CCPA is a broad regulation, focused on organizations that are doing business in California, and earn over half of their yearly revenues by selling the customer’s personal information, or earn over $50 million, or store the personal information of over 100,000 customers, devices, or households.
Challenges in Data Governance
A significant problem facing businesses implementing a Data Governance program is the realization that raw data is often not analysis-ready. The data may be badly organized, unstructured, or has been stored in separate databases. The data has to be cleaned and standardized before the Data Governance program can move forward.
Developing a Data Governance program might require a fair amount of manual labor, but after the data has been standardized, incoming data would be sent automatically to the appropriate location, and in the correct format.
Data silos are a slightly different problem for Data Governance programs. Data can be stored in silos and treated as though certain teams or individuals own it — and they sometimes don’t like to share.
Additionally, different departments may use entirely different systems, making standardization especially difficult. These same departments may have no real understanding of their data’s value.
Data Governance will support a framework allowing access to their data, breaking down the silos. (Some departments may try hiding their silo in a misguided attempt to protect it, and their status.)
Researching data fabric, and mentioning it during the interview, should have a positive impact. It could also be mentioned in a resume. The concept of data fabric is still fairly new. Data fabric enables easy access and allows the sharing of data within a distributed data environment. It supports a single, consistent framework for Data Management and combines architecture and technology to manage a variety of different kinds of data.
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