Question

Discuss an organization can use data governance and IT governance. Explain why and how they are different. Please submit a typed response. Thank you

Answer 1

Data Governance and IT Governance

The source of some of the confusion is that data and IT governance have very similar and interdependent goals. Both processes aim to optimize the organization’s assets to generate greater business value for the organization.

Since IT and data governance are so inextricably connected and vital to an organization’s operations.

IT Governance

IT governance ensures that the organization’s IT investments support the business objectives, manage the risks, and meet compliance regulations. Organizations wouldn’t be able to operate, optimize or even generate revenue without IT. In short: no IT, no data, and no business. But good IT operations require dedicated leadership to ensure that tech investments are maximized. Stakeholders involved in the success of IT governance include the board of directors, executives in finance, operations, marketing, sales, HR, vendors and, of course, the chief information officer (CIO) as well as other IT management. The key individual who’s responsible for aligning IT governance to the organization’s business goals is the CIO.

To accomplish their goals, CIOs will often use existing data governance frameworks, created by industry experts. These frameworks also provide implementation guides, case studies and assessments. Here are some frameworks you may have heard of 1. COBIT 5 2. ITIL 3. FAIR 4. ValIT®, 5. ISO 38500

When it comes to frameworks, you’ll have to decide which one works with your company culture and often times, organizations will find that a hybrid approach works the best. And with proper IT governance, the chance for data governance success increases. Execution and management of systems, applications, IT support and their management of data within a company will impact data governance.

Data Governance

Data governance refers to the management of data in order to improve business outcomes and fuel business growth.

So far, with the exception of asset type, data governance very similar to IT governance. Data governance involves processes and controls to ensure that information at the data level—raw alphanumeric characters that the organization is gathering and inputting—is true and accurate, and unique (not redundant). It involves data cleansing ( or data scrubbing) to strip out corrupted, inaccurate, or extraneous data and de-duplication, to eliminate redundant occurrences of data.

Organizations may use master data management (MDM) tools and techniques to clean their data and leverage business rules that can prevent inaccurate data from being entered into the database. MDM seeks to standardize the data, and ensure there is one “single version of the truth.” MDM is a quality-control tool and set of processes used to ensure control and consistency of data over time.

Data governance focuses on information quality from the ground up at the lowest or root level, so that subsequent reports, analyses, and conclusions are based on clean, reliable, trusted data (or records) in database tables. Data governance is the most rudimentary level at which to implement information governance. Data governance efforts seek to ensure that formal management controls—systems, processes, and accountable employees who are stewards and custodians of the data—are implemented to govern critical data assets to improve data quality and to avoid negative downstream effects of poor data. The biggest negative consequence of poor or inaccurate data is poorly and inaccurately based decisions.

Data governance is a newer, hybrid quality control discipline that includes elements of data quality, data management, IG policy development, business process improvement, and compliance and risk management.

The stakeholders involved for data governance include all the individuals required for IT governance plus a few more executives: the board, executives in finance, operations, marketing, sales, HR, vendors, CIO, IT management.

However, the individual responsible for aligning data with the organization’s business metrics is the chief data officer (CDO). The CDO will also enlist data scientists, programmers, and any department that generates data, which is every department within an organization.

CDOs are a recent addition to the C-suite, and they help lead companies in generating business value from data. According to Gartner, 90 percent of large organizations will have a chief data officer by 2019.

CDO is very much a technical role, but this position also requires business and change management skill sets. After all, they have to aggregate the data, analyze the data and the most challenging of all, get the business to act on the data.

Since this data governance is a relatively new field, there aren’t established frameworks, such as COBIT 5.

Summing Up the Differences

IT governance consists of following established frameworks and best practices to gain the most leverage and benefit out of IT investments and support accomplishment of business objectives.

Data governance consists of the processes, methods, tools, and techniques to ensure that data is of high quality, reliable, and unique (not duplicated), so that downstream uses in reports and databases are more trusted and accurate. Master data management (MDM) tools can assist in this effort.

Once the definitions of these three information-related governance disciplines are clear, their differences become more distinct. While they’re two of the most commonly used terms in the records and information management (RIM) industry, the definitions and differences of information governance vs. data governance are widely misunderstood. Both information governance and data governance are subfields under the broader umbrella of RIM, but they’re often used interchangeably or confused as one and the same. Though they do complement one another, they have significant differences.

Information governance tends to be associated with the business- and compliance-driven approach to managing the use, retention and disposition of business records. The Information Governance Initiative defines information governance as “the activities and technologies that organizations employ to maximize the value of their information while minimizing associated risks and costs.”

Information governance includes both structured and unstructured data. Its strategies include categorization, information lifecycle, definition of use, information access, secure disposition and eDiscovery.

On the other hand, data governance is usually an IT responsibility. Data governance accounts for all data, both structured and unstructured, as it correlates to data storage and transfer. Aspects involved in data governance include data security, data lineage, data service levels, master data management and data loss prevention.

To understand information governance vs. data governance, you must also understand the skills each entails. Information governance requires specialists with a background in RIM, privacy, technology, collaboration, disposition and discovery, whereas data governance specialists should be adept in data architecture, data modeling, data privacy, data integration and master data management.

Despite the various key differences, there are specific aspects of both information governance and data governance that overlap, creating a strong potential for fruitful collaboration and integration. Identifying the capacity of all business information involved in your organization is an initial step toward a universal approach to a general consensus and collective comprehension of roles and responsibilities.