Unless you have been operating a business in a niche without any digital aspect, you have heard of data-driven processes. This is simply the ability to collect, leverage, and gain valuable insight into your operations and customer interactions so you can compete and grow profits in today’s marketplace. The reason this is so critical is because that is what everyone else is doing. You cannot remain competitive if you have not embraced some form of data collection and analytics.
A critical feature of this process is data quality. You must be able to trust and rely on the data you collect to gain any kind of value for the future. That requires strong data quality management. The problem is that those who have a lack of data quality or the ability to properly manage such frameworks cannot benefit at the necessary level to compete.
What is Data Quality?
At its core, data quality is all about ensuring you have the correct information to conduct decision-making. That requires specific contributing factors like:
- Accuracy: Is the data collected reflecting the actual information used in decision-making? Inaccurate data leads to problems and poor outcomes because you are relying on information that may not be relevant to your decisions. If you think your demographic has people in their 50s, but you use data from people in their 20s, you will have poor marketing.
- Completeness: Are there any gaps in the information you are gathering? If you issue a customer survey with frequent skipped questions, your data is incomplete, and you may not gain the insights necessary for future promotions.
- Relevancy: Does the data being collected have anything to do with your operations? In general, a business that sells lawn equipment probably doesn’t need data about newborn behaviors in the American household.
- Validity: Is your data in the correct format for analysis? Date and time stamps are excellent examples of this contributing factor.
- Timeliness: How recent has the data been collected? You do not want to respond to a market trend based on 5-year-old data.
- Consistency: Are all of the data points the same across all your databases or sets? Without consistency, different users and teams may be operating under various assumptions that may not be true.
How to Properly Engage in Data Quality Management?
Luckily there are quite a few services and options for modern data quality management. This includes data quality tools that are streamlined for specific niche uses and parameters. These can include things like:
- Unified Data Platforms: The goal is to create a single source of consolidated information that tracks, manages, reports, and gains insight into the different data being collected and served. This alleviates many of the data fatigue issues in organizations by simplifying the entire process.
- Specialized Data Quality Tools: Here, the emphasis is placed on automation using tools that profile, match, monitor, and allow for bespoke metadata management. There are various options according to your industry and niche focus.
- Data Quality Services: Of course, if you would rather turn the entire operation over to a third party, there are plenty of vendors willing to take on that role. These tend to be highly specialized and experienced providers with transparent guidelines to ensure a solid data quality framework.
- Applications with Embedded Data Quality: By embedding safeguards into the data quality lifecycle of an application, you are empowered to manage your workflow far more efficiently. There may be some initial setup challenges, but in the long run, you will experience greater insight with fewer compliance issues because of the checks in place.
What are the Common Challenges to Maintaining Data Quality?
The velocity at which data management adoption by major industries is causing issues in the long-term insight and decision-making capabilities of analytics and tools. While the value of the data being collected is exponential, so also are the errors that can be easily made without a proper data quality framework.
A good example of this is data consistency. When your business does not have consistent data available to internal or external sources, you cannot build the trust and reliability needed to improve efficiency and profitability. There is a lack of enterprise programs to drive consistency, even though this is a critical aspect of any data quality framework. Every single decision made should reflect the driving forward toward your business goals and mission.
Again, we see the various timelines of digital transformation in the way data is managed through an enterprise. Take, for example, data silos. If you want to make valuable decisions about your organization for the future or in response to the market, you need access to the relevant information. Many organizations that utilize big data or business intelligence have data silos that separate specific groups of information from one another. This is in an effort to organize and respect the accuracy of data, but it is also preventing stakeholders from making critical decisions.
Sometimes the issue is a business does not know what it does not know. Whenever domain expertise is constrained to only specific fields of knowledge, the ability to cross-reference or create relevant connections is limited. This could be highly detrimental to the flexibility of the internal business process and the ability to make innovative decisions for the external customer and market pressures.
Finally, a lack of automation will inhibit modern data quality management. Simply put, there is too much data out there to manage effectively without the use of some form of automation. If you cannot streamline this process, you will fall under the wave of data being collected and not be able to move forward.
What is the Data Quality Lifecycle?
The best way to ensure you have quality data being collected and used for decision-making is to put in place a data lifecycle that accounts for the checks and balances needed. While each lifecycle can be easily modified to fit your specific field or niche business, a general outline would be:
- Data Profiling: This is how your data is examined and reviewed to remove any potential errors. You are creating summaries of data based on metadata and insights that will point out any data quality issues.
- Classification: With verified data, you can begin to organize information into different categories, so it is easier to retrieve, manage, and secure according to the formal rules and policies of your business.
- Rules & Validation: You must be able to place your data into the correct context using consistent formats and accurate representations to ensure quality. This could be anything from patient profile data at a hospital to ensuring an image is in the correct database column.
- Cleansing: Here, we can eliminate duplicates, faulty data, or anything else that could contaminate our analytics, insights, and decision-making (misspelled words, blank fields, etc.)
- Observability: Finally, we are able to view our data and gain insight into any data quality issues now that we are near the end of the cycle. This allows us to integrate any new rules or systems to eliminate unforeseen or unwanted errors.
Data Quality Management is Critical to Decision Making
Data quality drives the actions you will take in your day-to-day operations. If you act on information that is not relevant or accurate to your business, you risk losing out on valuable opportunities or end up making critical errors that could result in significant losses.
That is why it is critical you contact us at NextPhase to help solve your data management challenges. We have spent years perfecting our data quality management tools and frameworks to ensure your information is maintained correctly. With our tools, we are able to help you leverage incredible insights into your data so you can make real-time, evidence-based decisions that spur future growth. Reach out today and schedule a consultation with our expert team to improve your data quality systems.