More and more organizations see the strategic importance of their data analytics. Most organizations have invested in better data analytics solutions and processes over the years. The output and results vary greatly though. We see two common scenarios: it takes too long to get to significant data analytics results and there is not enough synergy between business, IT and data science teams. What are the main causes and how can you improve this?
In most situations data analytics is a fairly complex process. Different data sources have to be combined, data has to be pre-processed and analytics models have to be tweaked.
The first logical step is to automate the individual steps. In data engineering terms the focus is mainly on extract, transform and load (ETL) operations. Most organizations achieve better performance and throughput in this phase because manual tasks are automated.
The second step is often the biggest challenge: deploying the new automated data pipeline to production. Especially in high volume and high traffic production environments this requires different knowledge, skills and resources. Important matters have to be considered such as scalability, fault tolerance, infrastructure, privacy, realtime data etc. This step requires in-depth knowledge of architecture and infrastructure. This is not typically the field of expertise of data engineers or data scientists.
The third step is often ignored or forgotten, until the first incident occurs: the data pipeline has to be managed too. Incidents and disruptions have to be solved, preferably in the shortest amount of time and preferably 24×7. This step requires another and different set of skills and processes.
Software development teams have felt more and more pressure to deliver more value in a shorter amount of time. And to shorten the amount of time between idea and a solution that customers can use. This has led to the widespread adoption of the DevOps way of working. The foundation of DevOps is a multidisciplinairy team that can independently develop and maintain products. This leads to faster results, better incident resolution times and less handover situations.
The core principles of a DevOps approach can also be applied to data engineering and data science. Luminis is using the term DataOps for this approach. Applying a DataOps approach can quickly improve the results, predictability and collaboration of data science and data analytics teams.
Combining the various skills and knowledge in a team will improve the collaboration and increase the overall knowledge level. This will not only improve the quality of the output. By using a multidisciplinairy approach the way of working and adoption by the business will increase as well. A DataOps team will deliver much more value in a shorter amount of time.
Luminis has successfully introduced and applied DataOps in various organizations. This has led to significant improvements in results and quality. We have developed several best practices which could help other companies. Would you like to know more? Please contact us for more information.