DataOps for Digital Transformation
DataOps for Digital Transformation
In recent conversations, I've often been asked about DataOps and how Telm.ai fits in. So here is my point of view on how we can shift the conversation around Data.
In the perfect world, data would be predictable, trustworthy, flexible, and yield a high ROI without much effort.
But most businesses that deal with copious amounts of data know first hand that this is not the case. Many businesses are in need of the best quality and reliable data today, and that’s already too late.
In the last decade, digital transformations have been at the forefront for many businesses, and with this shift, processes need to be in place to ensure data is ready to be used in real-time, with minimum latency. However, data complexity due to volume and velocity has increased greatly, and despite a Gartner report that states 75% of all businesses will shift to operationalizing AI, the data infrastructure has yet to catch up.
The solution? Re-think how data is handled end-to-end. Operationalize Data.
By making a conscious effort to implement Data Operations, emphasizing extensive collaboration, automating various aspects of the ever-evolving nature of data, building resilient systems, technology, and implementing targeted roles; data products can deliver continuous high flow quality output that you can depend on.
As defined by Michele Goetz in her Forrester paper DataOps For The Intelligent Edge Of Business, “DataOps is the ability to enable solutions, develop data products, and activate data for business value across all technology tiers, from infrastructure to experience.”
Using the Agile and iterative methodology for faster and ever-changing requirements that is well known by the data science and engineering professionals, data operations are now getting traction.
More and more enterprises are rapidly realizing this need and making the shift today by re-defining with the 3 P’s:
- Processes
Due to the fluid nature of data and business requirements, having documented processes that determine the source of data, how they persist and flow in and out of dependent systems, ensuring that business requirements are executed as short bursts of agile lifecycle modules allow for high-quality output, faster turnaround times and collaboration across various data expert teams. Data management and data governance are prominent logistics and strategy based processes that have demonstrated value to data quality and business financial decisions.
- People
To respond to the growing data needs, personnel with specific roles and responsibilities are needed to collaborate towards an intelligent solution - data specialists, engineers, scientists, analysts, architects. Each layer of roles cater to specific operational needs, from building, testing, and maintaining environments, analyzing the quality of data using monitoring tools, to integrating and consuming data systems for business decision making.
- Tools
Tools help people increase productivity and improve performance. DataOps equipped with intelligent, advanced, and real-time systems cut the overall cost by reducing manual intervention and providing diagnostic incident reports of anomalies for a faster turnaround. The expectation of most organizations has been redefined by using high performing, on time machine learning technology that can automate tasks like managing data in motion, automation, monitoring data at a semantic level, analyzing, improving data quality, and notifying data engineers as soon as issues are discovered.
By making a conscious effort to implement Data Operations, emphasizing extensive collaboration, automating various aspects of the ever-evolving nature of data, building resilient systems, technology, and implementing targeted roles; data products can deliver continuous high flow quality output that you can depend on.
Where does Telm.ai fit in DataOps?
Our intelligent real-time monitoring fills in for tedious, time-consuming rule-based data quality systems requiring intensive human intervention. A self-training machine learning model that performs deep semantic analysis to ensure accuracy, completeness, timeliness, and consistency, allows for detection in data drifts as they occur, supporting streaming as well as batch processing is a perfect fit in a DataOps environment for today's complex pipelines.
As Michele states, “DataOps speeds up delivery and improves its product
quality with data pipeline intelligence. Go beyond standard lineage analysis and find capabilities to do deep metadata and code analysis of pipelines. Incorporate test automation, managed services, and database automation to continuously monitor performance, commits, quality, and cost.“
Automating using tools that add value and go beyond traditional modus operandi will improve product quality, and at the end of the day, if data is trustworthy, you see an immediate return of value in business and financial decisions.
#dataengineering #dataops #dataobservability #telmai #dataquality
Data profiling helps organizations understand their data, identify issues and discrepancies, and improve data quality. It is an essential part of any data-related project and without it data quality could impact critical business decisions, customer trust, sales and financial opportunities.
To get started, there are four main steps in building a complete and ongoing data profiling process:
We'll explore each of these steps in detail and discuss how they contribute to the overall goal of ensuring accurate and reliable data. Before we get started, let's remind ourself of what is data profiling.
1. Data Collection
Start with data collection. Gather data from various sources and extract it into a single location for analysis. If you have multiple sources, choose a centralized data profiling tool (see our recommendation in the conclusion) that can easily connect and analyze all your data without having you do any prep work.
2. Discovery & Analysis
Now that you have collected your data for analysis, it's time to investigate it. Depending on your use case, you may need structure discovery, content discovery, relationship discovery, or all three. If data content or structure discovery is important for your use case, make sure that you collect and profile your data in its entirety and do not use samples as it will skew your results.
Use visualizations to make your discovery and analysis more understandable. It is much easier to see outliers and anomalies in your data using graphs than in a table format.
3. Documenting the Findings
Create a report or documentation outlining the results of the data profiling process, including any issues or discrepancies found.
Use this step to establish data quality rules that you may not have been aware of. For example, a United States ZIP code of 94061 could have accidentally been typed in as 94 061 with a space in the middle. Documenting this issue could help you establish new rules for the next time you profile the data.
4. Data Quality Monitoring
Now that you know what you have, the next step is to make sure you correct these issues. This may be something that you can correct or something that you need to flag for upstream data owners to fix.
After your data profiling is done and the system goes live, your data quality assurance work is not done – in fact, it's just getting started.
Data constantly changes. If unchecked, data quality defects will continue to occur, both as a result of system and user behavior changes.
Build a platform that can measure and monitor data quality on an ongoing basis.
Take Advantage of Data Observability Tools
Automated tools can help you save time and resources and ensure accuracy in the process.
Unfortunately, traditional data profiling tools offered by legacy ETL and database vendors are complex and require data engineering and technical skills. They also only handle data that is structured and ready for analysis. Semi-structured data sets, nested data formats, blob storage types, or streaming data do not have a place in those solutions.
Today organizations that deal with complex data types or large amounts of data are looking for a newer, more scalable solution.
That’s where a data observability tool like Telmai comes in. Telmai is built to handle the complexity that data profiling projects are faced with today. Some advantages include centralized profiling for all data types, a low-code no-code interface, ML insights, easy integration, and scale and performance.
Data Observability
Data Quality
Leverages ML and statistical analysis to learn from the data and identify potential issues, and can also validate data against predefined rules
Uses predefined metrics from a known set of policies to understand the health of the data
Detects, investigates the root cause of issues, and helps remediate
Detects and helps remediate.
Examples: continuous monitoring, alerting on anomalies or drifts, and operationalizing the findings into data flows
Examples: data validation, data cleansing, data standardization
Low-code / no-code to accelerate time to value and lower cost
Ongoing maintenance, tweaking, and testing data quality rules adds to its costs
Enables both business and technical teams to participate in data quality and monitoring initiatives
Designed mainly for technical teams who can implement ETL workflows or open source data validation software
Start your data observibility today
Connect your data and start generating a baseline in less than 10 minutes.
No sales call needed
Start your data observability today
Connect your data and start generating a baseline in less than 10 minutes.
