Business leaders are rapidly integrating powerful data quality tools to improve the decision-making process. Rapid data collection is spreading to measure everything from customer relationships to uncovering valuable insights in business operations. With so much information being collected at growing volumes, it is critical to improve your data governance and data quality.
These are not interchangeable terms to be thrown around. Data quality maintains the credibility of the information by ensuring it is accurate, complete, consistent, and follows a practical standard of authenticity. Data governance is the framework companies use to manage data within the organization. This could be security measures, methods of compliance with legal standards, and ensuring proper access to the appropriate team members.
Modern business leaders need to integrate solutions that allow data quality and data governance to work in a complementary capacity. This way not only is the information being presented in reporting and live dashboards relevant to current business environments, but the system collecting and interpreting that information is equally reliable.
Elements to Consider for Maximizing Data Quality
Data needs to be reliable and serve a particular purpose. We live in an incredibly fast-paced marketplace Data quality best practices should be the foundation of your company’s decision-making. It should be the bread and butter for thoughtful attention you make about process evolutions, customer touchpoints, product offerings, and so much more. If you make these critical decisions from inaccessible, irrelevant, or incomplete data, you risk the future growth and development of your company.
By implementing critical considerations for data quality into your system and team ethos, you ensure all the relevant information is accurate and reliable when it is needed. Consider these elements:
1 – Data Curation
This is the active and ongoing management of data through its lifecycle of interest and usefulness. In other words, the flow of data and how useful it is for diverse groups. Even inside your business, some departments or stakeholders may need data for one set of parameters, and others need different relevant information. For example, sales teams do not need to know the same data as the IT department and will have different data quality metrics for their own focused goals.
2 – Data Management Controls
Your data is a critical competitive advantage for your business. The carefully collected information you have on your customers informs us what products, services, promotions, and interactions will benefit your potential future revenue streams. Data management controls ensure this information is collected following internal policies. This is not just regulated to security and access controls.
Your business needs to have a total view of the data management architecture. This is typically accomplished through a Data Catalog to manage meta data. A well-designed meta data management framework delivers visibility across the data management infrastructure, it provides traceability and data definition standards which helps in democratizing data and supports audit and compliance requirements through lineage.
Your information should hold up to security testing as well as performance under various conditions. You want to make sure whenever you have spikes or dips with information collection, it can be easily managed. The same is true for accuracy and validation. You want to reassure your stakeholders that the information they are receiving is highly accurate to the questions being asked by running it through system testing that operates in the background so it will not further restrict current assets.
The goal here is to create, optimize, and maintain those data travel pathways throughout your organization and that all information is appropriately moving in an exemplary fashion with full automation. This includes removing redundant tasks by mapping out the workflow and freeing up any bottlenecks that could slow down production or hitting KPIs (key performance indicators).
Your business needs to orchestrate movement from siloed data in multiple storage locations and then combine, organize, and distribute that information to the correct users. The more streamlined your data orchestration, the better your leadership will have to respond to critical market trends and data-driven decision-making.
3 – Data Dictionary
At its core, data quality relies on the ability to verify and provide highly relevant and accurate information to the people who need it the most. This cannot be done without a glossary of sorts. You need to create key terms and metrics with their definitions that align with the business. Removing this confusion will directly affect the ability of data to influence output.
The goal here is to create a “golden template” that all departments and sources use. This allows all data to pass through a framework and quality control that is accurate, relevant, and reliable. Best practices to formulate this type of data dictionary include:
- Create a list of terms and give them parameters like data type, dimensions, examples, sources, notes, etc.
- Offer up definitions of each term and data point so there is no confusion or redundancy.
- Remove any conflicting definitions. This includes considering cross-database reporting when leadership needs to compare Marketing to Operations and the like.
- Make sure everyone is on board. Get final approval from the relevant leaders, and then publish your data dictionary to ensure your data quality best practices are met.
Establishing a Single Source of Truth
All your decision-making will be scrutinized by the rest of the organization. There is no way to please everyone. Even in the best data compliance situations, different perspectives play a role in the final actionable steps the business takes. That is why it is necessary to formulate a single source of truth (SSOT). This ensures all decision-makers are working from the same data and source information.
If you have a manager working from information collected through a marketing firm and another from social media metrics, you are bound to see discrepancies in how they frame decision-making. There is nothing wrong with combining the two sources, but what needs to happen is a single source that acts as a traffic cop. Someone reliable to direct the flow and ensure everyone is operating from the root information.
This matters because reporting and analytics without data quality metrics like SSOTs lead to inaccurate decision-making. Think of this in legal terms. What would happen in a murder trial if half the jury saw one set of information and the other half saw something completely different? They are all there for the same purpose but acting on different source data. Would they have the same verdict? Today, most organizations deploy data lake architectures to help consolidate data from disparate sources. This is used as the repository that can spawn data marts for advanced visualization
Services that Lead to Data Quality and Data Governance
The collection, retention, and interpretation of data is one of the fastest-growing technology fields in the NextPhase Data Management is available to companies in need of data quality solutions and data governance frameworks. Our experienced team knows how to implement data quality best practices that ensure highly accurate, reliable, and relevant data is presented to the appropriate teams and stakeholders. We offer flexible models for implementing Data Catalogs services and Data Transformation routines to help maintain the integrity of your data as well as support compliance requirements.
We understand the need for accurate data-driven decision-making in today’s fast-paced world. One critical decision can make or break the future of a business and every employee relying on that infrastructure for income. That is why we take the time to understand your company’s needs and then implement data quality metrics and solutions for a successful outcome.
Rely on our professional team to integrate new AI and ML automation into your data management so you can remain focused on the more critical elements of your business, like customer relationships and new product development. We follow a data lifecycle methodology utilizing our automation library that has delivered successful business outcomes for our valued clients.
Data quality and governance is not an impossible standard to meet. With some bespoke integrations and communicating clearly so everyone on your team is on board, you can experience the beneficial power of data-driven insights that will fuel your business growth and development for years to come.