Improving Data Efficacy and Data Reliability
What is Data Operations Management?
Managing data is not your core business but it is critical for innovation and making your business grow. Any company that strives to be on the cutting-edge needs datasets to be readily available. We realize that for most of our customer’s data is critical for the analysis needed to make accurate predictions to ensure business growth. DataOps (data operations) refers to a practice that brings speed and agility to an end-to-end data pipeline process, from collection to delivery. Improved data management leads to a better quality of available data, which results in better analysis. That single source of truth translates into better insights, business strategies and higher profitability.
Common Data Management Platform Challenges
While working on data operations for our customers here are some of the common data challenges we encountered:
- Multiple disparate data sources with no single source of truth
- Large amount of incorrect or missing data
- Hidden costs of data growth – also the data is not easily scalable
- Inaccessibility of data – the data is on multiple systems that are not accessible
- Buried and messy data that’s not easily available for analysis
- Duplication of effort in data creation and extraction
Tibil’s Data Operations Solution
Managing data is at the core of everything that we do at Tibil. Tibil’s DataOps Solution strives to foster collaboration between data scientists, engineers and technologists so that every team is working in sync to leverage data more appropriately and in less time. Our DataOps strategy enables our customers to leverage data for superior customer outcomes. Machine learning and deep learning applications require constant new data in order to learn and improve. From our experience, any company that strives to be on the cutting-edge needs datasets to be readily available. Self-service data access and the infrastructure to support it are also essential.
Tibil’s Data Operations seeks to reduce the end-to-end cycle time of data analytics from the origin of ideas to the literal creation of charts, graphs and models that create value. Our DataOps focuses on making your data more reliable. It is the process of verifying and validating data to ensure data integrity and data reliability.
As part of our Data Operations, we leverage Data Governance to provide tangible answers to further assist a company to determine and prioritize the financial benefits that can be gained by using data while mitigating the business risks of poor data
Some of the real-life value adds from our customer engagements in DataOps are:
- Rapidly building ML models and analytical applications
- Creation of standard interfaces and reusable building blocks
- Allowing Data scientists to focus their efforts on high end work rather than waste time on DataOps
- Improved ETL for faster and better data quality
Read more about Tibil’s Data Operations Solution by downloading the datasheet
Benefits of Data Operations Management
In Tibil’s view, DataOps is a critical data related activity that is much ignored in many organizations that are generating large amounts of data. We believe that by focusing on improving DataOps, our customers can benefit with:
- Rapid Time-to-Value: Application framework to cut down development speed for each model
- Robustness: Audit framework to increase trust for accelerated & simplified compliance
- Scale & Performance: Scalable models with new use cases and new data
- Managed Evolution: Rapid iterations on the features and model development
- Dynamic Data Platform: Eliminate data friction, accelerate innovation and secure data
- Data Privacy and Security: Continuously deliver data without the risk
Besides Data Operations, we have expertise in Data Engineering solution to help you prepare and normalize data and build data lakes, Feature Engineering solution to help transform the prepared data into derived variables and formats for data analytics algorithms and models, Data Analytics solution to analyse data using statistical models to generate dashboards and reports, Data Science solution to build advanced analytical solutions based on ML algorithms and AI models, and Data Maturity Assessment to help you baseline your current data posture.