In the words of the famous British mathematician and data science expert Clive Humby, “Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so, data must be broken down, analyzed for it to have value.” This quote perfectly encapsulates the significance of data.
In this age of information, data is the new resource that reigns supreme across organizations all around the world. Similar to oil that fueled the industrial revolution, data is a valuable prospect that businesses have relied heavily on to drive innovation, technological and financial transformation. The vast amount of data produced, collected and analyzed dominates each aspect of life, from online searches to purchasing products to social media behavior; and influences decisions – from personal to governmental policies.
The biggest marketing challenge lies in harnessing the power of this collected data. While raw data is like unrefined ore, it harbors immense potential but to derive value it must undergo extraction, processing, formatting and must be analyzed by data science experts. Depending on what companies need – accurate, reliable and high-quality data is crucial for making effective business decisions that drive sales, upselling, customer retention and good revenue models. To achieve your business goals, you must structure and organize the data into a form that’s easy to read and visualize for realizing the full benefit of data investment.
A data strategy roadmap is your gateway towards realizing your business objectives and driving the highest revenue growth of your company.
What Is a Data Strategy Roadmap?
A data strategy roadmap is documentation that serves as a blueprint holding together various business facets such as the processes, tools and techniques for effective implementation of data. A well-defined data quality strategy entails objectives, resources and timeline to ensure consistent, complete and up-to-date data for effective business decisions.
Data strategy roadmap is the foundation upon which organizations build their operations, align data initiatives with business goals, track and measure progress and allocate resources efficiently. The rise of advanced technology, especially AI, has led to increased focus on developing a successful data quality strategy that meets milestones and drives the growth of your organization.
The Key Components of a Data Strategy Roadmap
Although data quality strategies vary based on what each business requires, the basic components of a roadmap include these elements:
- Data governance: It is the process of establishing a set of principles and managing availability, usability and security of data for your organization’s data management to ensure consistent and trustworthy use of data.
- Data profiling: Data profiling is the act of examining, reviewing and cleansing the source data to identify errors and to gain insights into the quality of data. This job can be done with the help of automated profiling tools to increase data integrity by transforming raw data into structured data sets.
- Data cleansing: Also known as data scrubbing, it involves steps like removing duplicates, identifying and fixing errors and correcting inconsistent data to increase data quality and make informed decisions.
- Data validation: This refers to the practice of checking, verifying and validating the cleansed data to ensure it adheres to predefined standards before it is used for business operation.
- Data monitoring: This falls under the category of evaluating the information entered into the database consistently to proactively identify potential errors and ensure quality data is integrated for use.
10 Steps to Creating an Excellent Data Quality Strategy
Step 1 – Determine business needs and assess impact of data quality
The first step towards data quality improvement initiatives is to establish and define the business goals to guide the data requirements of your organization. You can prioritize data quality by keeping these goals at the forefront of all business activities. This includes implementing quick wins, determining operational works and, identifying potential risks and challenges to put in contingency plans to mitigate them.
Examining the connections between data assets, business processes and key performance indicators (KPI) is useful for finding data quality issues and how they affect the revenue of your company. Further processes in this step include:
- Obtaining leadership support and key stakeholders collaboration
- Aligning business objectives with data requirements
- Identifying all potential sources of data including customer database, secondary data and other external data.
Step 2 – Analyze information environment and define data quality standard
Gather, compile and analyze information about the current business environment and verify data lifecycle to ensure only relevant data is being used. Understanding your data correctly is key to extracting the most value from it. Doing so helps you define the appropriate data quality standard, known as data quality service level agreement, that determines the metrics to be used for measuring data. These metrics should align with your business goals, as different sectors that use the same data may have varying results based on each company’s unique needs. The most commonly used metrics for data quality are accuracy, completeness, consistency, timeliness, and uniqueness.
Step 3 – Assess data quality
Often, data quality issues gets corrected only temporarily at the surface level, however, the original data retains the same value. To fix this issue, evaluating data quality at source prevents the propagation of bad data and its subsequent use at later stages of operation. Taking out the root cause provides the basis for future steps and discovering the areas of data rectification and improvement. Data ownership is crucial for guiding data management practices, streamlining the workflow and controlling data assets within the organization.
Data lineage and governance tools can assist you by automating your data quality strategy to trace the flow of data in your company. This allows you to identify potential threats, save time and resources and ensure accurate, consistent data. Data distribution analysis, data type examination, completeness checking and pattern recognition are some of the actions involved in data assessment.
Step 4 – Leverage data quality tools
Poor data quality severely impacts the expected outcomes for your business. It leads to missed opportunities, financial loss, and compliance issues damaging customer satisfaction and reputation of the company. A robust data quality strategy is vital to leverage data effectively and enterprise data quality tools offer optimization of the following key dimensions of data:
- Accuracy
- Consistency
- Completeness
- Reliability
- Timeliness
Data quality tools can increase the reliability of your data, which improves efficiency and leads to better decision-making for your business.
Step 5 – Implement data quality standard across the organization
Once the data issues are fixed, you may establish a high-quality data standard in your organization which guides the high-level milestones within the business. The milestones are critical for achieving the desired business goals at the right pace within anticipated timeframe. When you implement data quality standard, you effectively communicate the value of data quality to your organization for maintaining stakeholder engagement and support. A data-driven culture in your organization establishes policies that drive readily accessible and consistent data insights to make better, faster business decisions. Furthermore, it also attracts and retains powerful stakeholders and your best employees.
Step 6 – Appoint data stewards for data governance
Data stewardship is the management, oversight and governance of data assets and plays a pivotal role in any data quality strategy. You can nominate a data steward to oversee data domains such as quality, integrity, and compliance, optimize review processes and implement the required tools for metadata management. Appointing data stewards in your organization ensures accountability for data quality and that it is managed, measured and maintained in a consistent manner. Remember to provide them the necessary training and regular access to data quality tools to help stay updated with the latest practices in the data management field.
Step 7 – Conduct data quality audits
Data audits are the comprehensive assessment of all data for quality, completeness and accuracy throughout its lifecycle to ensure consistency. Conducting regular data quality audits against predefined metrics provides assessment on how well the data protection obligations are followed at your organization. Data audits result in improved data quality, identifying sections for growth and implementing corrective solutions. You can hire a third-party company that specializes in data cleansing services to do this task for your business.
You can also employ a visual dashboard to track, analyze and aggregate data quality trends and patterns over time to inform future operational improvements. Furthermore, they monitor the quality of your operational efficiency that may arise due to data integration, manipulation or transformation for various business processes. Over time, a quality dashboard tailored to your company’s specific needs can be used to communicate the quality of data to your stakeholders, clients or managers for overview of your business’s long term performance.
Step 8 – Form a special interest group
Set up a data quality interest group in your company comprised of various professionals from different departments of work such as sales, marketing, IT, and HR to strengthen the collaboration between them for significantly improving the data quality across the organization. The implementation of a group focused on data quality fosters team effort and reduces operational risks in your company. With a dedicated team working towards a common goal, your organization can effectively leverage the skill and knowledge of talented individuals to maximize data quality impact.
Step 9 – Continuous improvement and training programs
Data quality strategy is not a one-time effort but rather an on-going process that requires improvement. This involves root cause analysis, encouraging feedback loop, and leveraging technologies. Continuous improvement towards data quality isn’t always about linear progression but also about preventing future mistakes. Implement appropriate data correction at the root cause to ensure that your data quality strategy remains effective in the long run, aligning with the evolving business needs and trends.
In order to cultivate a data-driven culture in your organization, it is necessary that all staff members in every department participate equally, while sustaining their individual interests and passion towards the work. Effective communication from the senior level encourages your staff to share innovative ideas. Giving access to data within the corporation and specialized trainings help to develop a high-quality data strategy at your company. Regular training for the usage of metrics, concepts and tools will help your staff stay up-to-date with the latest trends and technology in their field of work.
Step 10 – Monitor, review and revise actions for results
As a data quality strategy is a dynamic and adaptable system, you need to be proactive in constantly monitoring, reviewing and revising it regularly to ensure that it remains relevant and aligns with your business objectives. To do that, you can communicate the results of data quality tests, errors made and improvements achieved with your staff, stakeholders and clients. Understanding the quality of your data by documenting the progress, actions and results goes a long way in improving the knowledge and practice required to maintain high data quality standards.
Organizations and the divisions and individuals that comprise them, often fall behind when it comes to fostering an efficient data quality strategy due to lack of a good data management process. Achieving a high-quality data strategy isn’t light work, it requires a proper foundation, dedication and hard work of every single person within an organization. Understanding and managing data quality can be a daunting task, but it is imperative to unlock the true potential of data assets for making smart, informed decisions that drive a business to success. Investing in the services of a data cleansing company ultimately increases the productivity, gives you the highest quality data information in business decision-making, and boosts the revenue of your company.
