The contrast between Data Quality as a Service (DQaaS) and traditional data management methods is becoming increasingly difficult to ignore. As organizations grapple with the ever-growing complexities and demands of handling large datasets, choosing the right data management approach is more critical than ever.
Our goal is to equip you with the insights necessary to make an informed choice about which data management approach best suits your organization’s needs. Whether you are leaning towards the innovative DQaaS model or considering the merits of traditional methods, this comparison will offer a detailed perspective to guide your decision-making process.
Traditional Data Management Strategies
Traditional data management methods often hinge on highly technical skills, complex processes and expense platforms and tools. They typically require expertise in coding languages like SQL and Python, knowledge of next-gen data governance and quality standards, ELT, and security protocols. While these skills are still available, relying on such specialized skill sets inherently drives up the cost of an in-house data management team, as data engineers and other high-dollar resources become indispensable to the process.
We recently worked with a company that tried to implement a new forecasting system. Their objective was straightforward: integrate product inventory and sales data from a data lake into a forecasting system. The forecasting system would crunch the data to provide inventory recommendations and sales trend predictions, including pricing adjustments and discount strategies. But this integration wasn’t simple. The data coming from the data lake needed significant formatting before it could be effectively utilized by the downstream system. The latter required our client to write a custom script to transform the data appropriately as it moved from one platform to the other.
The solution that was deployed was not very complex, but this project highlighted some additional issues. For example, the team encountered an unforeseen complication when exporting data from Snowflake extraneous elements were added to it, whereas using an API call kept it clean. This led to further script adjustments to ensure data integrity. Beyond the complexity of creating these scripts, an ongoing challenge presented itself: maintenance. The team had to constantly monitor the script for failures or inaccuracies, adding an additional layer of operational burden.
This system does not work! Especially if you don’t have a dedicated data management team. These traditional methods, while effective in their time, can be difficult because:
If you don’t have the staff, or money, to dedicate to a data management team who can handle building and maintaining these complicated strategies, we recommend you don’t try traditional!
DQaaS Strategies
In a DQaaS setup, companies manage their data standards through a service provider. Which means that the business no longer needs to invest heavily in developing and maintaining their data management infrastructure. Instead, they can rely on the expertise and tools provided by DQaaS vendors to ensure their data is well-managed and meets quality standards.
DQaaS represents a paradigm shift in data management, where the complexities of traditional methods are replaced by a more streamlined, service-oriented approach. And a key feature of DQaaS is the use of low-code or no-code tools that are still powerful enough to handle code-based operations if needed. This accessibility means that solving problems within the system does not require high technical skills, making data management more democratized and less reliant on specialized data engineers.
In DQaaS, instead of writing custom scripts to solve data problems, the platform itself is implemented to build efficient operations. These operations facilitate data transfer between various points (e.g., from Snowflake to Rellix), while simultaneously analyzing and modeling the data. Which ensures anomalies or inconsistencies are identified and rectified before the data is used for further processing or decision-making. For instance, if we look back to the example above, DQaaS services can quickly detect and address if there is an issue with the data being transferred from Snowflake before the data is used by Rellix due to the built in operations.
The ultimate goal of DQaaS is to transform raw data into actionable intelligence. By ensuring that data is validated, enriched, standardized, and accurately segmented. To do this at Datagence, we put our data through six key processes we call Datagence Core:
- Validation: Ensuring the accuracy and legitimacy of data to maintain its integrity and reliability.
- Enrichment: Adding context and depth to existing data, enhancing its value and usability for more insightful analysis.
- Standardization: Creating uniformity in data formats and structures to ensure consistency and comparability across the organization.
- Profile: Implementing measures to protect sensitive data and comply with privacy laws and regulations.
- Authorization: Ensuring proper access controls are in place, allowing only authorized personnel to access and manipulate data.
- Segmentation: Organizing data into meaningful and manageable categories for targeted analysis, marketing, and decision-making.
Each component of Datagence Core plays a vital role in ensuring that data is optimized for use within an organization. You need all six of these operations to make good decisions for your business because only doing one or two of these operations (which is what many “data cleaning/enrichment/validation” companies offer) simply won’t solve your long-standing data problems.
The Future of Data Quality
The shift in the data quality we know today versus the data quality of the future starts with AI.
No longer can we kick proverbial can of clean data down the road of priorities. AI requires impeccably clean and well-structured data to function optimally. If we want to utilize the power of AI (and boy do we!) then we need clean data. This integration of AI (when done with clean data) will prompt better decision-making and a competitive advantage for those who do this right.
But traditional methods of data management are often too slow and too costly to implement quickly. Which is where DQaaS becomes the future of data quality. With a DQaaS services like Datagence you put your data in the hands of the experts and have the assurance that your data will be cleaned, managed correctly, and enhanced to add the insights you need to stay competitive in the market.
However, it’s not just about doing things faster. It’s also about protecting the data used.
Consumers are increasingly aware of the value of their personal data. Every exchange of information – be it for a white paper, an event, or a service – is viewed as a transaction where value is expected in return. This dynamic has elevated expectations around data protection. Consumers demand assurance that their data is not only used appropriately but also safeguarded diligently.
When data is accurately managed and cataloged (which is a service a DQaaS company like Datagence can provide), your company ensures compliance with privacy regulations and safeguards against breaches. A lack of data quality standards can lead to data being scattered and uncontrolled, undermining the promise of data security to customers. And sadly, we often see this scenario with our clients who try to try to manage this in house but don’t have the funding for a dedicated team. This can erode consumer confidence, damaging relationships and, ultimately, impacting revenue.
Trust, in this new world, is paramount; without it, financial transactions and customer loyalty are at risk. When you use Datagence, you know your data is protected.
The Datagence Difference
Datagence distinguishes itself through a unique fusion of expertise, cutting-edge technology, and strategic foresight. Unlike conventional data management that often operates in isolated silos, Datagence offers a comprehensive suite of services that address the entire data lifecycle. From ensuring data accuracy and consistency to safeguarding privacy and enhancing usability, Datagence covers every facet of data management with precision and care.
