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Summary. As organizations deploy business intelligence and analytic systems to harness business value from their data assets, data governance programs are. As organizations deploy business intelligence and analytic systems to harness business value from their data assets, data governance programs are quickly.
Find, understand, and trust your data. Poor-data quality is another common problem that often has a significant impact on an organization's efficiencies and consequently its performance. According to Gartner Group, over 50 per cent of CRM and data warehouse programmes fail or do not achieve business case benefits due to poor-quality data. This is costing organizations tens of millions pounds in direct investment, management time and lost opportunities.
Larry P. He estimates that between 20 per cent and 35 per cent of an organization's operating revenue is wasted in recovery from process failure and data scrap and rework. It often delivers substantial benefits in its own right.
Instead of seeing data governance as an extra cost of business, however, it should be recognized that brand values and perceptions can be positively impacted. Not least, it should be seen as an investment in an important asset, which should have a significantly positive impact on business profits and growth. Significant consumer trust can be built through a clear, fair and positive approach to the collection and management of their data, as well as through proactive data security measures and a clearly demonstrated commitment to preventing data loss.
An instance of data loss will often have a profoundly negative impact on consumer trust, and can cause irreparable brand damage. Good data management enhances an organization's ability to cross-sell and up-sell to its customers. According to sales figures, 35 per cent of Amazon's sales are done through its recommendation system, which automatically suggests products to customers based on their previous purchases. There are many other examples that make the case for investing in good data governance.
People often have different understandings of the term data governance. To some it's all about privacy and regulatory compliance. To others it's more about security or data quality. In fact, it covers all of these key topics and much more. Data governance encompasses the people, processes and technology required to create a consistent and proper handling of an organization's data across the enterprise. It encompasses all aspects of data management and not just data quality, data security and regulatory compliance.
It is much more than just having a strategy. Data Governance is a mindset. Certainly, without effective data governance the organization will fail to maximize the value of its data assets. If your organization has an IT infrastucture, it has data. If it has data, it needs data governance. It is also a process that has to be enacted in day-to-day business activities every time personal information or sensitive data are being handled.
This means that data governance is both corporate and individual. Every employee needs to understand and conform to stated policies and regulations. The organization needs to be able to audit that understanding and demonstrate to regulators that it is maintaining best practice.
The enormity of the collapse of the banking sector in even surpassed the numerous corporate failures that have taken place over the last decade such as the Enron and MCI Worldcom frauds. Such Corporate failure and legislation has created a new management discipline known as Corporate Governance , which today is at the top of every Board Agenda — a key responsibility of every Director and a driver to reduce Risk. Both are closely related to Compliance. The relationship between corporate governance, risk and compliance Source : Racz et al. The goal of GRC and data governance is a shared one — to add value to the enterprise while mitigating risk.
This is what attracts support and investment from the Board and makes data governance a sustainable activity. For any organization with a focus on performance improvement and a significant data resource, the objective should be to progress its data governance capability. Part 2 of this paper outlines a proven approach to understanding and measuring your current data management capability and monitoring progress on an ongoing basis as you move towards the ideal state of an optimized set of processes aligned with your corporate vision and business goals.
Understanding your organization's current data capability is the first task when embarking on a data governance programme. On starting a data governance programme, a priority is to identify quickly any key risks to which the organization is exposed, before looking at your overall capability and opportunities to grow value. Rather than leading to increased red tape in order to ensure compliance, however, it offers a pathway towards an enabled business that has an assured, fully compliant data asset at its heart.
As a starting point for a data governance programme, a thorough investigation will examine your existing policies around data security and compliance in relation to current legislation, such as the Data Protection Act. Once the initial risk assessment is complete and any immediate issues addressed, you can focus on really understanding your broader data governance capability.
Data should be central to the whole organization. In fact, leaving data to IT, with its many other priorities, is highly unlikely to deliver a data asset that truly supports the demands and opportunities of the business. Crucial to success is executive level backing, with a properly funded team focused on delivering high-quality, secure and compliant data that are fit for purpose for all business users.
Data management is never a one-time programme, but very much an ongoing process. Similarly, it cannot be tackled all at once. You need to recognize that your organization needs to make step changes to develop its data to be successful; this process must be evolutionary, taking many small, achievable, measurable steps to achieve your longer-term goals. Redundancy: in line with good business practice not least the Data Protection Act , the often difficult decision to archive and store redundant data that is no longer of value to your organization. Technology can and in fact needs to play a significant role in developing data that are fit for purpose, in reducing risks and in growing data value over the longer term.
It will bring significant benefits around standardizing data and improving data quality generally, for monitoring and reconciling data, managing risk and implementing a much more secure data culture throughout your organization. In addition, the right technology tools will enable data to be more efficiently accessed and used throughout the organization.
Your success in developing the data asset your organization needs will depend on people issues, your processes, and the technology your organization employs to support them and data management. As your data governance capability matures, you will enjoy the benefits of higher-value data, such as increased sales.
You will also reduce the risks of data breaches that can do so much damage. Improving your data governance maturity level is hard and will take time.
It is a long-term process that must be addressed in small, careful steps. To start, this paper takes you through the characteristics of each stage of the Data Governance Maturity Model. Your data management is undisciplined. You have issues but are doing little about them. Data cost is an overhead, not a strategic investment. There are few defined data rules and policies.
There is little or no senior management oversight. Sales and financial systems do not synchronize. Sound familiar? Some 40 per cent of the organizations we assess fall into this first category! Reactive organizations struggle to achieve regulatory requirements. Data is not governed by the organization as a whole. Data sharing is rare. There is little data quality deployment.
Action is usually driven by crisis, for example, a failing CRM programme. Often failure of CRM triggers focus on data. The Reactive Stage is the most difficult to progress beyond. It requires top level support, clear vision, goals and defined strategy that is adequately resourced. We estimate that 30—40 per cent of organizations in the UK are in the Reactive Stage. You will be analysing and monitoring your data, for example for accuracy, on an ongoing basis.
There will have been a major culture change. The organization will view data as a strategic asset across the enterprise. Data will be recognized as adding real value to the organization. We estimate that 10 and 15 per cent of organizations in the UK are in the Proactive Stage. By the Managed Stage you will have a mature set of data processes. You can identify issues as they arise and can define strategy in a manner focused on data development. You believe that power and most value are delivered from sharing data. You will be employing company-wide data definitions and business rules designed for data consistency.
Delivering a common information framework for uncommon business agility Highlights. This is one of the key objectives of data governance. Through the series, we will follow this not for profit organization as it develops a new data organization, a realistic data strategy and roadmap and makes its way towards establishing optimal data governance throughout the organization and the highest level on the Data Governance Maturity Model. The Economist. Can business users quickly retrieve information about specific customers when they need it?
We believe that less than 10 per cent of organizations in the UK have reached the Managed Stage. Data and data development is a core competency across people, process and technology. Emphasize the risks of legal and compliance issues. Action items: Upper management to promote EIM as the solution for resolving cross-functional information issues.
The value proposition for EIM is put together and presented. Action items: Develop and present the EIM business case to management and stakeholders. Identify EIM opportunities at department or unit level. Action items: Information management tasks and projects need to be inventoried and ensure they are in sync with the EIM strategy.
Create a balanced scorecard for information management.