Sunday, July 14, 2024

Impact Of Poor Data Governance Kpmg

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The Impact Of A Bad Data Flow

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The question just posed, is one that is relevant for many businesses. As indicated by Thomas C. Redman in a ResearchGate article, 1 to 5 percent of the data fields of the average company are erroneous. Assuming this estimation is correct, it would mean that of every 1000 data entries 10 to 50 are incorrect. Although this in itself is quite an issue, the disaster proceeds when this erroneous data enters your value chain.

A bad data flow within your company can have serious consequences for your value creation. Most companies do not know where bad data flows within their organization and therefore base their decisions on this low-quality data. For example, a company assuming that an investment will be profitable based on incorrect data, or relying on incorrect data and as a result picking a wrong location for a subsidiary. Although these are radical examples, it shows the scope of not knowing your data flow.

Kpmg Reports Lack Of Corporate Data Responsibility Eroding Consumer Trust Preventing Users From Sharing Data

A new study shows that most people are concerned about the amount of data collected, the collection process, usage, and protection practices by various companies.

The KPMG survey found that most companies had increased data collection activities, with several involved in unethical collection practices.

Consequently, the lack of corporate data responsibility has eroded consumer trust and consumers are becoming increasingly uneasy about data collection, with many unwilling to share their information.

KPMG conducted the online survey between April 30 to May 12, 2021, involving 2,000 adults and 250 business leaders for organizations with 1,000+ employees.

What To Include In Your Data Governance Strategy

As discussed, data governance is a broad category. Consequently, there are a number of things included in a strong data governance framework. As a start, it should include policies, rules, procedures, and structures for data management.

To help ensure that these procedures are focused and working towards the right objectives, its a good idea to create some foundational documents to guide the process. These should include:

  • A mission statement
  • Metrics by which the goals will be measured
  • Clear guidelines detailing who is responsible for various aspects of data governance

Further, throughout every phase of this process, its important to document all parts of the framework and share them throughout the organization.

In addition to the above policies and procedures, some enterprises make the decision to utilize data governance software to help with implementation and enforcement. While this isnt a requirement, many organizations decide to utilize this software to help support:

  • Program and workflow management
  • Development of policies
  • Process documentation

Such software can be an effective way to ensure that plans are effectively developed, implemented, and enforced.

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Data Privacy Is The Job Of Everyone

Now that breaches and hacks are in the news, companies must pay serious attention to protecting confidential information, not to mention the laws most are subject to. The problem is that protecting data and complying with rules require us to manage PROCESSES and DATA, not just data.

The quality of data in a data warehouse could be ITs responsibility in the past, with some direction from project or domain data stewards. Ensuring the privacy of information, if you think about it, goes way, way beyond that. Just think about how data is used or shared, and it can quickly escalate in terms of scope. How do you control that everyone is following the rules?

We must recognize that just like we had to develop data quality KPIs to manage data quality, coming up with KPIs around data security and data privacy requires harmonizing and unifying how all stakeholders will participate. Regulations are becoming quite specific and prescriptive. The same data may need a different approach to be compliant with a rule depending on its lifecycle stage, where it sits, how it is aggregated or how it is used.

  • Check whether the process is in place
  • If the process is adequate to support compliance
  • If someone or group is duly accountable to execute the process
  • If the process has been completed and has provided the expected result.

We can clearly create KPIs to track this stuff.

Common Data Management Issues

The 2020 SWIFT CSP update and its impact

Data management is essential for businesses in order to ensure regulatory compliance by handling data correctly. In addition, it drives operational excellence by delivering consistent data and thus stimulate growth. Conversely, data management problems could lead to a variety of issues. These range from poor risk management decisions to lost data. Therefore, we list some of the most common problems that can occur when it comes to data management.

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The First Step In The Healthcare Analytics Journey

Healthcare systems and providers have become increasingly focused on the need to use evidence to inform clinical and operational decisions. This has led to them assembling and critically evaluating ever larger data sets around care delivery, performance, and cost. As health systems continue to adopt technologies to enable new or improved approaches to diagnosis and treatment, the size of our data sets will continue to grow.

The vast amount of data generated and collected by a multitude of stakeholders in healthcare comes in so many different forms insurance claims, physician notes, medical records, medical images, pharmaceutical R& D, conversations about health in social media, and information from wearables and other monitoring devices. Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet .

It is the scale of this data that sits at the very heart of the fourth industrial revolution and the impact it will ultimately have on the way we care for patients and communities in the future.

Manage The Processes That Manage Data

The body of practices we have developed over the years for Data Governance is still good. But there will be way more to do. What we left to data stewards to figure out will no longer work. We will have to map out the processes in detail because these will be required for audits anyway. The good news is that it will drive data operations.

Managing processes that manage data rather than just the data will also help as the privacy laws will change. We will need agility to understand what are the operational impacts and react quickly.

Adding a layer of process management will give more visibility to everyone in the organization, no less to executives who have been somewhat in the dark or having to trust that someone is doing their job well to prevent, say, a data breach.

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Its Too Big To Do Manually

Many organizations are still enforcing Data Governance policies and standards through ad hoc, manual or outdated tools. Data teams try and vet reports and data sets, setting up custom rules all over the place and comparing expected numbers. Technology stacks and the explosion of data mean that this old way is inefficient and cant scale.

Even if the problem gets bigger, it still needs to be managed. We will distribute the work to the rightful owner and automate the validation of its completion. Whatever value the process owners bring in the value chain, they will optimize some of the processes they are responsible for by automating with technology, which might use AI to do some heavy lifting.

These are technologies we are used to, including lineage and mapping, data quality monitoring, etc. But because of the sheer size of the problem , we cant just leave it to the various stakeholders or data stewards to figure out. We need to add discipline to the orchestration of how everyone needs to participate we need to manage the processes of managing or manipulating data.

Data Overload And The Rise Of Unstructured Data

Paths to Value for the Data Executive

As the volume and variety of data increases, the challenges of effective data governance will only grow more difficult.

Digital transformation technologies have resulted in an explosion of new data sources. Mobile phones, for example, have made it possible to understand human activity and mobility at a granular level that would have been inconceivable just 15 years ago. Location intelligence can help companies better understand detailed foot traffic patterns not just the volume of visitors to a location, but information about who is visiting and when. Thats a game-changer if you have a good handle on your data.

The Internet of Things is providing up-to-the-minute information on machinery and equipment, shipments in transit, vehicles, and more. That data can be used to make supply chains more efficient, to improve vehicle safety, and to decrease machine downtime with predictive maintenance.

Then there is the challenge of unstructured data. Videos, e-mails, social media posts, and similar unstructured data sources create a whole host of new challenges in data governance.

As the adoption of artificial intelligence and machine learning accelerates, organizations must be prepared to rein in the chaos. That means taking a comprehensive and proactive approach to data governance. The process begins with a complete inventory of data assets, identification of the most important elements of that inventory, and a prioritized approach to moving forward with data governance.

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Key Actions: Solving The D& a Dilemma

Put in the work to harmonize data sources. While ensuring data consistency across multiple systems can be a tedious, painstaking process, poor quality data can only lead to poor quality analysis. Organizations need to put in the hard work to create a âsingle source of truthâ that can be relied upon to generate meaningful insights.

Start with the end state, then work backwards. Rather than resolving to adopt a high-impact analytics technology and then determining where best to apply it, first ascertain what business questions the company most struggles to answer, then determine what data, technologies and other capabilities are required to solve them.

Create non-traditional KPIs to measure business performance. More sophisticated analytical techniques facilitate the creation of more sophisticated performance measures. Measures such as customer lifetime value and customer experience profitability are being used by exemplar organizations to uncover the true drivers of business performance.

Consider COEs and other centralized resources to solve governance issues. Data-focused COEs can provide enterprise-wide expertise on how to source and integrate data, how to govern it, and the methods and technologies to analyze it. The Finance function is uniquely well positioned to create and manage such a COE.

The Foundation Of Your Strategy

Data governance defines how an organization manages its data assets, and, in a digital world, how improved decision-making should be operationalized. This calls for an appropriate authority model to manage data functions. Many healthcare leaders understand the importance of data governance, but struggle to:

  • Understand where their data lives and how to access it
  • Put in place effective processes to protect data from threats of inappropriate release and access and
  • Acquire and develop the right resources and skillsets to manage healthcare data.

To access the very latest thinking on the subject, we have gathered the experience of KPMGs leading global D& A professionals and interviewed healthcare CEOs and CIOs to better understand their concerns and ambitions. Our framework for designing and implementing data governance aims to demystify the topic and helps to overcome common challenges and pitfalls, by outlining practical steps to effectively manage enterprise data assets.

First, we define data governance and its key elements. Appreciating the importance of data stewardship, ownership, policies, and standards lays the groundwork for sustainable governance. We highlight some of the typical data governance traps that healthcare organizations fall into when beginning their D& A journey.

Finally, we explore other important considerations, such as protecting information privacy , data sharing , and enabling technologies for data management.

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  • Smartsheet is a flexible project management software suited for organizations of all sizes. It has planning, automating, and reporting tools to give your team a more effective workflow. Know more about its features in our Smartsheet review.
  • Asana allows you to track and manage all your projects on a single platform. It streamlines communication across your organization and lets you plan and set goals with your team. More details about its features can be found in our Asana review.
  • Jira is perfect for developer teams for planning, tracking, and releasing a software. It lets you create a custom workflow that best suits your team. You can learn more about its tools in our Jira review.
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    What Is Data Governance

    The starting point of this article should be the definition of data governance. A commonly used definition has been formulated by Thomas : Data governance is a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods. Essentially, data governance is an overarching concept that defines who is responsible and has which role for what data at what point in the process. Roles and responsibilities for enterprise data alone, however, are not sufficient to improve data quality in a sustainable way. Data governance needs to be complemented by other elements of data management that together form a comprehensive data management framework.

    We defined a framework for data management , based on our experience in the field of Enterprise Data Management and commonly used data management frameworks such as DAMA DMBOK . The framework consists of nine building blocks, representing the approach for Enterprise Data Management. Data Governance is part of the approach, together with, for example, data Definitions, Standards & Quality, providing a single source of truth within an organization. The package of measures creates a sustainable data management organization, given that they are properly embedded in the organization, guided by a strategy and principles for enterprise data.

    The Rise Of Better More Accessible Data

    Big Data has been a commonplace term for the last 10 years, and yet rarely has such an over-hyped term failed to live up expectations. The promise of bigger, better, and broader insights failed to materialise for most organisations and most people carried on about their business. Part of the problem was that data resided in on-premise systems, or hard-to-scale data centres, coupled with poor database structures that made big-data hard to achieve.

    The rise of Cloud is now starting to transform how we approach data, and with many scalable platforms available on the market, data has never been easier to obtain, categorise and model. We also anticipate a growing trend of the need to incorporate external data sets alongside internal information with the realisation that some insights are driven as much by what is happening in the outside world as what is happening within the organisation.

    We still need good data governance, master data management and such to be a key part of any organisation structure, but we are now starting to realise the promise of Big Data and proper BI empowering corporate services to effectively tackle the challenges they face as decisions can be based on better, more accessible data available at the right time.

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    Lack Of Dedicated Ownership

    In many organizations, it is a commonly held belief that the IT department owns data governance. While IT clearly has a role, organizations should recognize that data governance spans multiple domains within the business and is best served by a dedicated ownership model.

    In other cases, data governance occurs across a collection of silos in which marketing owns the CRM database, accounting owns core financials, and logistics looks after inventory and fulfillment data. Unfortunately, this results in a disjointed approach to data governance, which is to say, no data governance at all.

    Cross-functional collaboration is key for successful data governance initiatives. Someone whose primary role is data governance needs to take the lead. This, in turn, requires senior management alignment and the willingness to put budget, resources, and enforcement authority behind the data governance role.

    When organizational leaders are successful in communicating the why of data governance, the other two pieces generally follow more easily. As big data capabilities continue to increase, the mandate for effective big data governance becomes more and more obvious. So, too, does the realization that effective governance requires a commitment that it cannot be a part-time endeavor fulfilled by IT, marketing, or any other department whose primary focus is elsewhere.

    Users Need More Control Over Their Data

    KPMG Audit Data & Analytics: Delivering quality, insight and value

    Lucas said that the loss in consumer trust originating from the real or perceived lack of corporate data responsibility threatens business innovation. He added that organizations were at risk of being denied valuable data and insights that spur growth.

    While there is no standard definition of unethical consumer data use, it shouldnt be terribly difficult to identify, Martin Sokalski, Principal Advisory, Emerging Technologies, and Digital Solutions leader at KPMG, said. If companies would not want their data practices in headlines out of fear of what consumers might think it makes sense to reconsider.

    Sokalski added that organizations were obligated to communicate with their customers on data usage and protection.

    He advised businesses to leverage data discovery and governance tools and explore the implications of using emerging technologies like Machine Learning and AI, to enhance data protection and regain consumer trust.

    These technologies can help organizations build greater visibility into their data practices, from better data tracking to helping ensure integrity and fairness throughout the lifecycle.

    Another way to regain consumer trust was by giving consumers more control over their data.

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