Wie können Sie Investitionen in die Datenqualität rechtfertigen?

Inaccurate or incomplete data: Leads to flawed decision-making, misaligned strategies, and missed opportunities. For example, incorrect customer information can result in failed marketing campaigns and lost sales. Inconsistent data: Creates confusion and hinders collaboration. Different departments may use varying data formats or definitions, making it difficult to share insights and work towards common goals. Outdated data: Renders analyses and forecasts obsolete. Organizations relying on stale information may misjudge market trends, fail to adapt to changing customer preferences, or misallocate resources.

To justify data quality investments, start by clearly defining the problem. Identify and document specific data quality issues affecting the organization. This could include: • Inaccurate Data: Errors in data that lead to incorrect insights and decisions. • Incomplete Data: Missing data that affects the comprehensiveness of analysis. • Duplicate Data: Redundant data entries causing inefficiencies and confusion. Use real-life examples and assess the impact of these issues on business processes, such as delays in decision-making, increased operational costs, or decreased revenue opportunities. By presenting a comprehensive and detailed problem statement, stakeholders can better understand the urgency and necessity of addressing data quality.

Data Quality has a direct impact on ROI! Often DQ issues are picked up downstream of where the data was collected or generated...unfortunately in an attempt to fix it quickly staff tend to "fix it where they find it". This might work for their current project/process but results in 1. Someone else fixing it again or 2. The next person missing it (impacting the solution) It's essential to always go back to the route cause...this takes longer initially but in the long run you are fixing the problem not patching the solution!

One thing I found helpful was to get into client's shoe and think about the problem. Few reasons because businesses are not able to generate meaningful insights out of data can be: - Issue with Quality of Data from the channel they use. ** Non- compliant Data (Duplicates and NULLs in data are most common reasons) - Incomplete and Inaccurate data because of lack of communication between departments. * * Performance Issues in Data Platform also lead to issue of data consistency (when data is loaded from different sources/departments)

I speak to a number of large enterprise businesses daily who rely on the accuracy of their data. Most businesses have adopted makeshift or loose steps to capture and govern their critical data. More often than not this relies on good old fashioned MS Excel. I love a spreadsheet as much as the next person, at some point though Excel fails to provide the most optimal setup for data management. Imagine, as a manufacturer of retail goods, you set up a new customer. You provide goods on credit but you don't have any formal approval steps. Your team creating your new vendor entered and captured all of the details but a critical step was overlooked. Just one simple error or oversight could cost you thousands. How much risk can you afford?

Investing in data quality is justified by: • Reduces errors and inefficiencies. • Supports improved business outcomes. • Meets regulatory requirements and reduces penalties. • Enhances experiences and builds trust. • Streamlines processes and reduces redundancies. • Provides valuable insights for market differentiation.

Low data quality leads to inaccurate results to any kind of data driven solution across functional areas. It can lead to poor data driven decisions, inaccurate forecasts, increased operational costs, increased regulatory risks, decreased productivity etc. In many cases impact of the bad data may not be visible at the functional level, but might be causing huge cumulative impact at the organizational level.

Before defining what is the data quality problem, we must understand who needs the data, how the data is driving a business process, and who is complaining about poor data quality. Any investment in data quality must map the benefits to problems or inefficiencies in the business process. We will need to gather evidence of users' negative experiences related to data quality. The more complaints we can have, the easier it will be to define a compelling business case for investing in data quality. It will be the business that will sponsor a data quality improvement project, so the problem must affect the business at a scale that we can estimate the impact using money as a measure.

In my previous role, we faced significant challenges due to inconsistent customer data across multiple systems. To quantify the problem, we conducted a data audit that revealed a 15% error rate in customer addresses, leading to delays in order deliveries and increased customer complaints. This data profiling exercise provided concrete evidence of the operational inefficiencies and customer dissatisfaction caused by poor data quality. Presenting these findings to the management was crucial in justifying the need for a data quality improvement initiative, as it directly linked data issues to measurable business impacts, such as increased costs and revenue loss due to customer churn.

Cost reduction is achieved through savings from fewer errors, less rework, and reduced redundancy. Improved decision-making results from accurate data, leading to better strategic decisions and competitive advantage. High-quality data enhances customer service and loyalty, boosting customer satisfaction. Maintaining data quality supports compliance and risk management by meeting regulatory requirements and reducing legal risks. Clean data streamlines processes, increasing productivity and operational efficiency. Additionally, strong data quality drives revenue growth through better marketing and sales outcomes, and supports scalability for future needs.

The strongest value proposition for investing in data quality would be measurable savings resulting from data quality improvement. If we know how much users waste by double-checking data, we can add automated data cleansing and enrichment tasks to automate it. The next strong benefit is related to new opportunities, but most business executives will not believe that data quality can magically increase revenue by 10%. Maybe if we find some missing customer opportunities during data profiling, we can build a use case on that. Finally, the third type of benefit is around user satisfaction. If users waste time due to poor data quality, they can tell us how much time they will save. Reputation, trust, and innovation are just cherries on the cake

Accurate Insights: High-quality data provides a reliable foundation for analysis, leading to accurate insights about customers, and market trends. Data-Driven Decisions: With trustworthy data, decision-makers can confidently make informed choices based on evidence, rather than assumptions or gut feelings. Reduced Risk: Data quality reduces the risk of making decisions based on incorrect or outdated information, which can lead to costly mistakes. Upselling and Cross-Selling: Clean data helps identify customer preferences and purchasing patterns, enabling targeted upselling and cross-selling opportunities. Pricing Optimization: Data quality can support dynamic pricing strategies that maximize revenue based on demand and customer behavior.

The impact it has to operational efficiency and revenue optimization via Data Science and Analytics. Align data quality to tangible improvements to key organizational KPis.

Next, outline the tangible and intangible advantages of investing in data quality. This helps in building a strong business case by demonstrating the positive outcomes of improved data quality. Potential benefits include: - Enhanced Decision-Making - Customer Satisfaction - Operational Efficiency - Regulatory Compliance - Cost Savings Quantify these benefits whenever possible, such as estimating potential revenue gains or cost savings, to provide a compelling argument for data quality investments.

You can talk about justifying data quality investments, but you probably also need to talk about not investing in data quality. Do people really understand the risks? Do people really know that they're making decisions with bad data? Is anyone confident they're submitting accurate financial statements? Too often, people assume someone has it covered and they can just open a perfect report. More often than not, someone has struggled to produce that report that might only be directionally accurate. That might be good enough, but you don't know unless you ask.

Investing in data quality enhances decision-making by providing accurate and reliable insights, leading to better strategic and operational choices. It also reduces risks by minimizing errors and uncertainties associated with poor-quality data, ensuring that decisions are based on solid, trustworthy information.

Investing in data quality improves decision-making, operational efficiency, and customer satisfaction while reducing costs and errors. It ensures regulatory compliance, enhances trust and reputation, and provides a competitive edge. High-quality data fosters innovation and better collaboration, supporting organizational growth. Additionally, it aids in effective risk management, safeguarding the organization's interests. By leveraging accurate data, businesses can achieve strategic goals and sustain long-term success.

Do you want to make better predictions? How much is your business dependent on it? Can you get away with poor predictions? Find the ROI

Data Architecture and Data Lineage play crucial role in this process. By reviewing how data flows through your systems and relationship between upstream and downstream datasets you will be able to build a more fine-tuned model for benefits estimations. Remember, whenever you push data quality upstream, you decrease the costs associated with issue remediation. According to IBM research, total costs of bad quality in US was 3 trillion dollars per year. Most of that was due to extra work required to fix bad data. Basically, the cost of bad data can grow exponentially as data moves though the lifecycle stages. That's why data lineage and architecture are so extremely important.

We articulate the benefits of data quality improvements by aligning them with our clients' strategic goals. Using frameworks like the Data Quality Business Impact Model, we estimate the ROI of data quality initiatives. For example, enhancing data quality for a retail client not only improved operational efficiency but also boosted customer trust and satisfaction, leading to increased sales and a stronger market position.

The budget of a data quality improvement project should include the whole cost of ownership. The tools will not be the biggest cost component here. We need to engage some business users, such as data owners. Many organizations first define data owners and data stewards before moving forward with data quality activities. The next cost factor is technical data stewards and data engineers, who need to be engaged to perform data quality testing, map data quality requirements to data quality checks, and perform data cleansing. The data architecture that we can apply will strictly depend on the skillset of the specialists that we have in the organization. These are the people who should propose a feasible solution.

Outline a Data Quality Improvement Plan: Develop a comprehensive plan for improving data quality, including specific actions, timelines, and responsible parties. Consider a multi-pronged approach that addresses data governance, data cleansing, data standardization, data integration, and data monitoring. Identify the resources required, such as technology, personnel, or training, and estimate the associated costs. Build a Business Case: Create a compelling business case that compares the costs of poor data quality with the potential benefits of investing in data quality improvement. Highlight the return on investment (ROI) by showing how the financial benefits outweigh the costs of implementation.

Identify and select the most appropriate solutions to address the defined data quality problems. This involves evaluating different DQ tools and methodologies. Key considerations include: - Scope: Determine whether the solution can handle the volume and complexity of your data. - Integration: Ensure the solution can integrate seamlessly with existing systems and processes. - Ease of Use: Choose user-friendly tools that require minimal training. - Scalability: Select solutions that can grow with your organization’s needs. - Vendor Support: Evaluate the support and services offered by the solution provider. Presenting a well-researched solution helps in convincing stakeholders that the proposed investment is practical and effective.

Selecting the best solution involves evaluating factors such as functionality, scalability, and cost. For a financial services client, we compared multiple data quality tools and selected one that integrated seamlessly with their existing systems. We outlined a clear scope, timeline, and budget, ensuring that the client understood the resources required to achieve their data quality objectives and the expected outcomes.

Assuming organization strategy is inline and we already have stakeholder buy-in. Selecting a solution or tool for improving data quality will depend on 3 main factors: 1. Current data quality and risk assessment: Data quality has to be assessed to determine the current state which will also act as starting point and to understand the risk organization is exposed to because of bad quality data both these points will help in preparing a business case. 2. Budget: Depending on budget and skillset of the implementation team (training required, technical skills needed to use the tool), tools can be decided. 3. Data integration capability and functionality: Tool should be able to integrate with different data sources, and able to profile data.

In my experience once problem has been defined correctly, first step has already been taken towards identifying solution. Second part to the solution can be framework which provides periodic report on data quality and alerts when an issue is identified. Multiple Data Quality Frameworks are available (paid and free), which can be analyzed to identify which is best fitted for our Problem statement. All the Data Quality Frameworks combine principles of Data Profiling, Data Observability, Data Reliability and Data Governance.

We have to understand that data quality is a change in the team's culture, and it has to be introduced in many iterations. A common recipe is following these steps(no matter the tech stack): 1. Ensure PK(granularity) of each table. All tables have a grain that reflects what a row is, and it's represented by a column or a combination of columns(simple or compound Primary Key) 2. Ensure referential integrity between tables. Tables have to be joined with a validated relationship(either 1:1 or 1:N) 3. Add custom business validations. For instance, all percentages in the system are represented as 0-1 format (not 0-100) so we have to check for >1 values as those are outliers

Identify potential risks associated with data quality investments and outline strategies to mitigate them. Common risks include: - Implementation Challenges: Ensure a detailed implementation plan with timelines, responsibilities, and resources. - Costs Overruns: Prepare a realistic budget and include contingency plans for unexpected expenses. - Resistance to Change: Develop change management strategies to gain buy-in from all stakeholders. - Technical Issues: Conduct thorough testing and have a support plan in place to handle technical problems. By proactively managing these risks, you demonstrate diligence and increase stakeholder confidence in the anticipated investment.

Demonstrate Risk Mitigation: Clearly articulate how data quality investments can directly address the identified risks. For example, data cleansing initiatives can improve data accuracy and reduce the risk of financial errors. Data governance frameworks can ensure compliance with regulations and mitigate security risks. Show how data quality investments can improve decision-making processes, leading to better risk identification and mitigation strategies. Explain how high-quality data can enhance operational efficiency, reducing the risk of errors and delays.

Risk management is crucial in our approach. We conduct thorough risk analyses and develop comprehensive risk management plans. For an insurance client, we identified potential operational risks during the implementation of a new data quality system. By defining contingency measures, we mitigated these risks and ensured a smooth transition, ultimately enhancing the client’s data accuracy and operational reliability.

- 📊 Quantify the ROI by demonstrating how improved data quality reduces costs, mitigates risks, and enhances efficiency. - 📈 Use case studies and success stories to illustrate the positive impact on decision-making and business outcomes. - 💬 Tailor your message to stakeholder interests, emphasizing how data quality aligns with their specific goals and concerns. - 🎯 Highlight metrics and KPIs that showcase improvements in data accuracy, consistency, and timeliness, linking them to business performance. - 🛠️ Provide ongoing reports and visualizations to keep stakeholders informed about progress and tangible benefits of the data quality initiatives.

Speak Their Language: Executives: Focus on the bottom line impact of data quality. Emphasize how it can increase revenue, reduce costs, improve efficiency, mitigate risks, and support strategic decision-making. Use financial metrics such as ROI, net present value (NPV), and internal rate of return (IRR) to quantify the value of data quality initiatives. Managers: Connect data quality to their specific departmental goals and pain points. For example, demonstrate how accurate data can improve marketing campaign effectiveness, streamline sales processes, or enhance customer service. Use metrics relevant to their areas of responsibility, such as customer satisfaction scores, conversion rates, or operational efficiency.

Use clear and compelling language to highlight how improved data quality will benefit the organization. Techniques include: - Storytelling: Share success stories from other organizations or departments that have benefited from data quality improvements. - Visual Aids: Use charts, graphs, and dashboards to visually represent the benefits and impact of high-quality data. - Business Impact: Connect data quality initiatives to strategic business goals and priorities. - ROI Analysis: Present a detailed return on investment (ROI) analysis, showing the financial benefits relative to the costs. Tailor your communication to the interests and concerns of different stakeholder groups to ensure they understand and support the investment.

The biggest challenge in incorporating data quality is demonstrating its value proposition and securing funding. Many teams limit data quality to technical checks within data pipelines, missing out on broader benefits. True buy-in comes from showcasing how data quality directly impacts the business. For example, missing records in financial data can lead to inaccurate cost analysis, affecting budget forecasting and resource allocation. By highlighting such real-world consequences, you can effectively communicate the value of comprehensive data quality initiatives.

If you have to justify data quality investments, you don't wait to communicate value until after you've already defined the solution. The people who are experiencing the problem need to be involved in the solution. If you define a solution without them, you've solved a problem that might not be the actual problem the users are having. Especially if this is coming from an IT team, you have to be solving the problem with the business team. If you're working in a governed environment, the Data Governance Lead will be able to bring people together to clearly understand the problem and identify the best solution.

In my experience use cases are the best way to do so. Select one or two scenarios that illustrate the consequences of poor data quality then propose a viable solution. For example, incorrect contact information for a customer means marketing material doesn’t reach that customer, resulting in loss of sales.

For communicating value of investment done on ensuring Data Quality is to communicate with client in a clear and direct manner using Value Report. Value Report should clearly and precisely document issues being faced (our Problem Statements) and what went into resolution of those concerns, thereby putting light on the benefits like Clean Data leading to increased confidence on reports for taking strategic and business decisions. Once value brought by new approach has been established, a dashboard with periodic refresh rate is always helpful so that we have tangible information using which actual and notional ROI can be identified , which also helps us to secure more investment required for maintenance/enhancement of Quality Framework.

You need to measure data quality and assign impact in order to identify value. Without measurement you are just using hearsay to quantify data quality.

Effective communication is key to gaining stakeholder buy-in. We use clear, compelling language and visuals to present the business case for data quality investments. For a logistics client, we tailored our communication to highlight how improved data quality would streamline operations and reduce costs. By addressing their specific concerns and demonstrating the value through success stories, we secured the necessary approvals for the project.

Wrong data (incorrect or inaccurate data) can conduct to wrong decisions. Validation is also an important step ya take before the decision is made. Data can provide the steps to prioritize needs and strategize those.

In our experience, fostering a culture of data quality is essential. For example, in a project with a manufacturing client, we implemented regular training sessions and established data stewardship roles. This not only improved data quality but also promoted a sense of ownership and accountability among staff. Additionally, showcasing quick wins and early successes helped in maintaining momentum and securing continued investment in data quality initiatives.

High-quality data enhances revenue through better decision-making and improved customer experiences. Accurate data supports strategic choices, driving growth, while also ensuring correct customer interactions, increasing satisfaction and loyalty. This leads to higher returns on investments and a stronger competitive position in the market.

Hopefully your effort has some return on investment. If poor quality data yields outcomes that work for you, then you may not need as big of an investment.

First step is to bring out the problem. Demonstrate if there is an issue with data quality from an end user's perspective (If no investment is done in that space - for sure there will be issues). Once established, from the initial investment spend on building data observability which will further highlight DQ issues and build a case for investing more. Also get along users who are most vocal about DQ issues and get them on board with the initiative showing this is aleviating their problem. At this point you should have enough willingness to invest heavily into the space - now start building from the foundations up - a quality culture, engineers with specialized skills in data quality, push towards fully automated DQ with good coverage.