Accelerating Big Data Analytic Value Through Data Governance
Imposing order on raw data requires intention and a firm understanding of data governance principles. Data procedures, focused analytical scopes, and user-centric mentalities need to be developed from unprocessed foundations. Organizations that are either new to data governance or are looking to evolve their data governance program in the face of big data often find the following strategies enhance their analytical abilities the most.
Formalizing data stewardship One of the biggest challenges in working with big data is that most end users dont have the time or expertise to fully comprehend raw data. The business rules that have traditionally gone into processing these data sets often reside in the heads of SMEs who have full-time day jobs. Making this information relevant and discernable to other departments that need it can quickly become a burden on SMEs as more users reach out to understand the data.
An important note is that while the role is formalized, organizationally it is critical that these stewards remain connected to their operational areas as the business rules will evolve over time and they can quickly become disconnected if pulled out of these day-to-day processes.
Challenges Of Data Governance
Though the rewards are great, creating a data governance solution may feel difficult. Some of those challenges include:
Company-wide acceptance. Since data spans across multiple departments, there needs to be clear leadership from the top down as well as cross-functional collaboration.
Poor data management. If your data management is structured from an incomplete data governance program, the data will be unsecured and siloed as well as having undisciplined processespossibly leading to massive data breaches and non-compliance.
Standardization. Organizations need to find the right balance between governance standards and flexibility.
Aligning stakeholders. You’ll need to work hard to convince stakeholders of the value of your dataproviding transparency to stakeholders will persuade them to invest in your organization’s governance and securities budgets.
Assignment of responsibilities. There might be struggles with deciding who and who shouldn’t have access to particular segments of data. Creating a system of who sees what and when will help you and your team eliminate potential issues.
Your data governance strategy both the technical and business aspectsneeds to be accepted by everyone in the company. And to ensure you have a successful strategy, you’ll need to implement best practices and principles into your data governance program.
Value Of Data Governance In Business
Data governance treats data as a tool that helps identify trends in customer behavior and cost saving-activities. Better data analysis, operational support, and business decision-making result from using data governance properly.
Data governance can also help to alleviate those things that can lead to several business problems, such as errors or inconsistencies in data. A wrong decision that could easily be avoided is often based on flawed data.
Regulatory compliance also relies on data governance as it assists companies in complying fully with appropriate regulatory requirements. This is critical for a company that wants to avoid the hefty fines levied for noncompliance.
When data governance is done well, it effectively results in a reduction in data costs, an increase in access to good data and, of course, an improvement in the quality of the data that is available.
A company like Agile Recruit can help you to make the most of your data governance by developing a robust data governance strategy and getting experts to help you implement it.
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Build Data Governance Plans On Gantt Charts
Before you can manage your data you need to implement your data with a Gantt chart. Our interactive Gantt chart collects all your essential tasks to better implement your data governance. You can link dependencies, set milestones to help you track progress and even filter for the critical path. Once done, set your baseline to monitor variance and make sure youre always on track.
Who Is Involved In Data Governance
A well-designed, high-quality data governance program usually includes a responsible governance team, a steering committee that serves as the governing body, and a group of data stewards. Data stewards refer to an oversight role in data governance within an organization.
They are tasked with ensuring the quality and fitness of the organizations data assets. Data stewards are considered a specialist role, and they also assist with the development and implementation of data assets and recommend improvements to the data governance process.
All the individuals mentioned here work together to create the standards and policies for governing data, as well as the implementation and enforcement procedures primarily carried out by the data stewards. Senior executives and representatives from an organizations management also take part in data governance along with the IT and data management teams.
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Data Governance Policies And Procedures
Business policies and standards are critical for any data governance program. Its important to agree on policies that can apply throughout the enterprise. Typical policies include:
- Data accountability and ownership
- Data capture and validation standards
- Information security and data privacy guidelines
- Data access and usage data retention
- Data archiving policies
The culture at each organization is different. There isnt a right or wrong set of policies to consider. As you map out your data governance program, watch out for any potential perception of red tape. Instead, todays successful data governance programs work together and focus on improved collaboration. Decide together on whats best for the organization while also understanding that enforcement doesnt have to feel restrictive. By making this pivot, you will shift your data governance program from being policy centric to value centric.
Acknowledge The Disconnect Between It And The Business
Although IT supports your data needs as custodians, the business knows its data the best and is able to explain how supply chain, P& L results, and other business reports drive real business results.
In many cases, your IT organization should help the business by empowering end users to use technology platforms and enable self-service, rather than creating or managing data assets, terms, spreadsheets, and reports for departments. The amount of data a successful organization leverages on a daily basis should be exponentially beyond the capabilities of employees that support your IT tools.
When the lines of business are able to create and define their own terms and data flows in a system of record, they will be able to better realize their efficiencies and solve their own business problems faster.
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Why Does Data Governance Matter
Data governance helps ensure that your data is consistent, reliable, accurate and trusted to enable data-driven decision-making. Traditionally data governance has been focused on risk and compliance. But the focus of data governance has shifted over the last few years. The advent of digital transformation and the exponential increase in the volume of data, distribution of data, data-related regulations and the number of users who want to be empowered with trusted data have contributed to the change. Data governance has moved from a rigid, one-size-fits-all approach to a more agile version tied to delivering the data intelligence required for data-driven decisions.
See how intelligent data governance lets you bring together people, processes and systems to deliver strategic business outcomes.
Hurdles such as the abundance of data, users and regulations can seem insurmountable. But the single greatest issue comes down to trust assurance in the quality and protection of the data. Trust that users can and should have approved access to appropriate and reliable use of that data, and trust that all team members are empowered to confidently use this information to deliver value creation opportunities. Effective data governance can help deliver the trust required in data. On that foundation, data governance can democratize trusted data to empower data consumers of all skill levels in the organization to propel analytics, AI and data-driven digital transformation.
Data Governance Creates A Shared Language
Data governance allows teammates across departments to define business terminology, KPIs, rules, policies and more so everyone in the organization speaks the same language. By creating this shared language, organizations build a common understanding and leverage the same, consistent and trusted information.
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Assess Your Strategy Position
We find that companies with the most-advanced data strategies started at one point and gradually migrated to a new, stable position. For example, they may have shifted their focus from defense and data control toward offense as their data defense matured or competition heated up. The opposite pathfrom offense toward defense, and from flexible toward controlledis possible but usually more difficult.
Consider how data strategy has shifted at CIBC. The bank established the chief data officer role a few years ago and for the first 18 months maintained a 90% defensive orientation, focusing on governance, data standardization, and building new data-storage capabilities. When Jose Ribau took over as CDO, in 2015, he determined that CIBCs defense was sufficiently solid that he could shift toward offense, including more-advanced data modeling and data science work. Today CIBCs data strategy strikes a 50/50 balance. Ribau expects that the new attention to offense will drive increased ROI from data products and services and nurture analytical talent for the future.
Where Does Data Governance Fit In The Modern Data Stack
Data governance brings trust from the raw data sources to domain experts dashboards
The typical data flow is the following :
- You collect data from various sources from your business. It can be product logs, marketing, and website data, payment and sales logs, etc. You extract that information with tools like Fivetran, Stitch, or Airbyte.
- You then store this data in a data warehouse . The data warehouse is both a place to store and transform your data to refine it.
- The new trending transformation layer for the past 3 years is DBT. It enables to perform data transformation in SQL within the data warehouse while implementing software engineering good practices.
- At last, the transformation helps you build your data mart, the golden standard in terms of refined data. The visualization brick helps domain experts visualize this gold-level data to share insights throughout the whole organization.
These steps are happening on different tools, with a high level of abstraction. It is hard to keep a birds eye view of what happens under the hood. This is what data governance is bringing to the table. You can see how the data flows, where the pipeline breaks, where risks lie, where to put your energy as a data manager, etc.
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Data Governance Goals And Benefits
A key goal of data governance is to break down data silos in an organization. Such silos commonly build up when individual business units deploy separate transaction processing systems without centralized coordination or an enterprise data architecture. Data governance aims to harmonize the data in those systems through a collaborative process, with stakeholders from the various business units participating.
Another data governance goal is to ensure that data is used properly, both to avoid introducing data errors into systems and to block potential misuse of personal data about customers and other sensitive information. That can be accomplished by creating uniform policies on the use of data, along with procedures to monitor usage and enforce the policies on an ongoing basis. In addition, data governance can help to strike a balance between data collection practices and privacy mandates.
Besides more accurate analytics and stronger regulatory compliance, the benefits that data governance provides include improved data quality lower data management costs and increased access to needed data for data scientists, other analysts and business users. Ultimately, data governance can help improve business decision-making by giving executives better information. Ideally, that will lead to competitive advantages and increased revenue and profits.
Effective Data Governance Provides A Variety Of Benefits To Organizations Including Improvements In Operational Efficiency Data Quality And Business Decision
- Andy Hayler,Information Difference
All companies face the need to measure and analyze their business performance. The bigger the organization, the bigger the problem, with various operating companies, business units and departments all having their own priorities and ways of doing things. That includes how they handle their data, which makes strong enterprise data governance a must.
For example, say you want to answer a seemingly simple question like, “Who are my most profitable customers?” To do so, you need to be able to gather data from around the enterprise on customers, the products they buy and the costs involved in marketing and selling to them. Even if you can do that, just figuring out how much revenue is associated with a given customer may be no trivial task.
For example, if it’s a complex multinational company, you need to be sure your sales teams have correctly identified that operations they’ve invoiced for purchases are part of the broader entity. It’s easy enough to figure out that Shell USA and Shell UK are part of parent company Shell PLC, but what about Pennzoil or Jiffy Lube? They’re also Shell subsidiaries.
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Cloud Data Governance Framework
Moving your data to cloud storage will be crucial in your growth. Your cloud data governance framework serves as a blueprint and lays the foundation for how your data strategy is stored in the cloud. Products like Microsoft Purview helps your team explore data flowsthe ins and outswhile your governance integrates your rules, responsibilities, procedures, and processes on how those data flows are managed and controlled safely within cloud storage.
Libor Transition Benchmark Study
Sia Partners and the law firm Cadwalader, Wickersham & Taft conducted a global benchmarking study to provide detailed market feedback on the status and plan of action for LIBOR transition. The study included over 75 organizations and identified leading practices for a successful transition as well as common challenges. The study concluded that leading organizations made the transition an imperative and allocated resources to the program. Companies that have strong data governance are at an advantage when it comes to the transition of contracts.
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You Need To Get Your Data Governance In Gear If:
The organization wants to provide ease of data access or build big data environments like data lakes but are struggling with data quality measures.
Business/operations are overly reliant on IT resources for all things data.
Business users dont use data assets because they dont trust them, cant nd them or dont understand them.
The organizations data management team spends more time xing data quality issues than running analytics.
Data Governance Principles And Best Practices
When creating the framework needed for your data governance, you’ll need to create one that fits the objectives of your organization. Some things you’ll need to think about are how to use your data properly, improve data security, create and enforce data distribution policies, and keep in compliance with all regulatory requirements.
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Identifying Business Value For Data Governance And Data Stewardship
Ensure that your organization can identify the actual business value data governance and data stewardship contribute to start and maintain the program
Often, data governance and data stewardship programs are cited for a lack of tangible metrics that indicate the success of the initiative. Without identifying criteria for measuring the results of the data governance program and the activities of the data stewards and data management professionals, an organization cannot feel confident that the program is achieving its business goals or contributing quantifiable business value. Many data governance programs are not funded fully or are cancelled after a pilot when the effort does not demonstrate detectable positive results in pre-defined criteria.
To avoid the stigma of cancellation when the program is successful but has not demonstrated that success, it is essential that every data governance and data stewardship program follow these guidelines:
Guidelines for Identifying Data Governance Business Value
1.) Create a set of business value goals for the data governance program that are approved by senior management. This should be a short set , based on the business goals and related to how the data governance program will address them. Example drawn from an EWSolutions client:
2.) Identify the specific measurement types to be used to calculate results for the data governance program:
What Is Data Mesh
Data Mesh is all about empowering data producers and consumers. It does this by decentralizing data ownership and giving domain-specific groups the ability to manage data as a product. This decentralized approach enables data producers and consumers to access and manipulate data without a central data warehouse or data lake team. It also allows organizations to be more agile and responsive to change.
In the concept creator’s own words, Zhamak Dehghani‘, “Data mesh is a decentralized sociotechnical approach to share, access, and manage analytical data in complex and large-scale environments within or across organizations.”
She explains it through four pillars on which Datamesh is founded:
Data Governance Challenges Are Not The Same For Everyone
Diverse governances use-cases based on industry needs and company size
There are two main drivers for data governance programs:
Data regulation pushes the minimum bar of data governance processes higher. It requires businesses to add controls, reporting, and documentation. This is a need to ensure transparency over sometimes unclear processes.
Having strong governance becomes increasingly important with the exponential growth of data resources, tools, and people in a company.
The level of complexity increases with the scope of business operations , the velocity of data creation, or the level of automation based on data.
Identify Quantitative And Qualitative Benefits
The results of the research, interviews, identification and assessment of opportunities and scope can lead to the identification of benefits.
Sample qualitative benefits include:
- Improved decision making. Well governed data is more discoverable, making it easier to find useful insights. It also means that decisions will be based on higher quality data, enabling greater accuracy.
- Enhanced data quality.
- Reduction in time spent by knowledge workers in finding and acquiring information.
- Elimination of redundant hours spent across knowledge workers looking for the same or similar data.
- Reduction in rework and rationalization due to poor data quality.
- Enhanced productivity, since data is well understood and more readily leveraged.
- Reduction or elimination of fines as data is better managed to support regulatory compliance.
- ROI associated with specific analytics initiatives can be quantified, based on the assumption that improved data availability and quality support these.
- Elimination of impacts due to financial restatements.
Finally, pull together your business value case, making sure you take the following into account:
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