“Garbage in, garbage out”. Since the invention of punched cards and teletype machines, this maxim has been true. The value that today’s advanced IT systems provide to their users, whether it be in accounting, production, or business intelligence, depends much on high-quality data. Data doesn’t, however, automatically format itself appropriately or proactively inform you of its location or intended use. No, data simply exists. A data governance strategy is thus necessary if you want your company data to meet the requirements of availability, usefulness, integrity, and security. IT managers, database administrators, and other data management experts used to frequently worry about data governance. This has drastically altered. The need for greater data privacy protections, a public uproar, and a robust response from government authorities in Europe and the US have all contributed to putting data governance on the C-suite agenda and causing reputational harm to large firms. A record of 1862 data breaches occurred in the US in 2021, according to a report released by the Identity Theft Resource Center (ITRC). In comparison to the 1108 breaches in 2020, this figure increased by 68%, breaking the previous high of 1506 breaches achieved in 2017.
Given that over 90% of data breaches are caused by cyber attacks, the Identity Theft Resource Center estimated that data breaches will pick up speed in 2022 after a record-breaking 2021. Make no mistake, a data governance strategy is essential in light of the alarming increase in data breaches. Without regulation, the vast amounts of data that are being gathered and used for a growing number of purposes in modern company operations might lead to a legal, technical, or reputational quagmire. Enterprise data can be made more consistent, even across divisional borders, more accessible, and simpler to utilise with excellent governance, allowing for more intelligent business decisions.
Poor facts make lousy law. Likewise, poor data results in poor decisions in the world of analytics. A data governance strategy aids in preventing “bad data” in your organisation and the potentially harmful actions that could arise from it. Here are seven steps to Developing a Data Governance Strategy.
Also read: Data Verification and Data Validation Techniques
SEVEN STEPS FOR BUILDING A DATA GOVERNANCE STRATEGY
The framework provided by a data governance strategy links people with procedures and technology. It designates roles and holds distinct people responsible for specific data domains. For the organization’s data collection and management procedures, it establishes the standards, procedures, and documentation frameworks. By keeping data clean, precise, and useable, the data governance strategy maintains integrity. You guarantee secure data storage and access by using this framework.
STEP ONE: IDENTIFY AND ORGANIZE EXISTING DATA
Simply getting started with data governance can be a costly, time-consuming task for many businesses. But the truth is that your business is probably currently engaging in data governance at some level that may be turned into a plan. Despite the fact that data governance has not yet been formally established as a policy, the necessary personnel to manage corporate data already exists. These personnel includes a database administrator who controls access, IT personnel who diligently backup and restore data, and a network manager who verifies that the business intelligence tools are licensed appropriately. The company’s data assets should then be listed in a directory, along with everybody who has responsibility for that data as well as their direct reports.
This informal approach may reflect the disorganized reality of the current governance procedures in your firm, but the end result will be a more condensed list of resources, responsibilities, and accountabilities. Once finished, it’s time to become more tactical.
STEP TWO: CHOOSE A METADATA STORAGE OPTION
Traditionally, businesses’ departments have their own databases for managing metadata. As a corollary, data is now segregated, which restricts the exchange and reuse of metadata assets. For collection across multiple platforms, efficient reuse of metadata, insight into data history, and effective governance and stewardship choosing a storage choice that centralizes metadata are essential. The scalability and flexibility required for analytics are ensured by centralizing metadata. Additionally, it aids in the understanding of data lineage by various departments.
STEP THREE: PREPARE AND TRANSFORM THE METADATA
The next step is returning to the original metadata, reformatting it, making the necessary corrections, and integrating datasets into data catalogs. Start by eliminating outliers, adding missing values, establishing a baseline for the data, and masking sensitive entries. In order for all data to be understood and used within the company, update values or formats thereafter. Following this, make templates for a data dictionary, a business lexicon, and business metadata. By doing so, you may arrange your data vocabulary and keep track of the number of data assets or terms you upload to your network.
STEP FOUR: CONSTRUCT A GOVERNANCE MODEL
There isn’t a data governance approach that works for everyone. Companies have traditionally utilized passive, compliance-focused frameworks. These specify how users produced, stored, maintained, and discarded data. To fully benefit from analytics, businesses need contemporary data governance models that respond to a variety of learning styles, are contextually aware, and promote creativity. Additionally, it should offer a dynamic, adaptable business approach across the ecosystem, including distributed decision-making rights tied to value and adopt a proactive risk management strategy. Furthermore, the governance structure must be centralized or federated. The model you select should be designed to meet the requirements of your firm.
STEP FIVE: CONSTRUCT A DISTRIBUTION PROCESS
Strategies for contemporary data governance should democratize data. Only when people abide by data governance principles can they be effective. For the same reason, policies operate best when they are incorporated into everyday tasks, processes, and tools. Organizations should take into account proper employee onboarding, use policy and guideline training, promote knowledge sharing among staff members, and develop processes for requesting and implementing amendments in order to effectively manage regulatory risk.
STEP SIX: IDENTIFY AND EVALUATE POTENTIAL RISKS
Compliance mandates and new security rules are constantly being updated. Companies must implement suitable security controls, for instance, in accordance with the General Data Protection Regulation (GDPR) and the California Privacy Rights Act (CPRA).
STEP SEVEN: CONSTANTLY MODIFY YOUR DATA GOVERNANCE FRAMEWORK
Both businesses and their data strategies evolve over time. Data governance processes need to be regularly adjusted and enhanced by businesses. This enables them to respond as problems arise, such as growing hazards to data privacy. The process of data governance places a strong emphasis on people. In order to inspire people to take ownership of the assets in the data catalog, it is important to establish a data culture. It’s critical to identify your data stewards—those who have the most in-depth knowledge of the data—before implementing a data catalog and ingesting new assets. Your subject matter experts on an asset are those who use it the most. Stewardship is automatically assigned by the Alation Data Catalog based on actual, individualized usage. Additionally, it’s crucial to motivate your knowledge stewards to impart their tribal wisdom by paying them with accolades like certification levels, badges, etc. Money bonuses, tangible presents, and additional time off are among other types of recognition. Users are thrilled to acquire ownership while using this strategy. Additionally, it gives people assurance that they can unquestionably trust the source of their knowledge. As a result, people are more equipped to make use of the information at hand.
Also read: Applications of Robotic Process Automation
Your data strategy may very well be supported in large part by data governance. However, it’s important to gain the community’s support. From an organizational standpoint, people must be involved in understanding how and why their departments use information. These data stewards should also have automated tools at their disposal, such as machine learning and crowdsourcing capabilities, to support them in maintaining these procedures. The key to a successful data governance strategy is persuading people to accept responsibility. Companies must create plans that will be implemented and operationalized across teams in order to achieve this.
Also read: Big Data Integration: The Benefits and The Challenges
WHY CHOOSE INTONE?
We at Intone take a people-first approach to data governance. We are committed to providing you with the best data integration service possible, tailored to your needs and preferences. We offer you:
- Knowledge graph for all data integrations
- 600+ Data, and Application and device connectors
- A graphical no-code low-code platform.
- Distributed In-memory operations that give 10X speed in data operations.
- Attribute level lLineage capturing at every data integration map
- Data encryption at every stage
- Centralized password and connection management
- Real-time, streaming & batch processing of data
- Supports unlimited heterogeneous data source combinations
- Eye-catching monitoring module that gives real-time updates
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