DataOps is your way to build closer interaction between IT and business and to generate faster business value from data.
Many of us have witnessed companies deal with plenty of issues that arise from poor data models. A very typical problem is that the model has grown over time and the dependencies within a model are numerous, which makes the model maintenance exhausting. Another typical problem is that the model doesn’t reflect to real life anymore. Over time, the business environment has changed, but there has not been a possibility to refactor the model. Things are handled with short cuts and exceptions.
The knowledge of these peculiarities lies within very few people who have lived throughout the model development. For everyone else, it is difficult to see why the data model is built the way it is. When these few people leave the company, others struggle to take over and understand the data model, and it might set the company back to square one with their data.
Traditional ways of doing data modeling and warehousing do not really support business involvement or agile teamwork. Maybe due to the technical nature of data warehousing, it became purely an IT department task in many companies in the past. As a result, organizations often find themselves in a situation where business and IT are miles apart and data development lacks focus and ownership.
The root cause behind these problems is that changing a data model is a wearing task. The Data Vault modelling embraces the idea that data models change and expand over time. The Data Vault methodology enables incremental changes and frequent updates to the data model. The approach requires discipline, as modelling work needs to be done meticulously and correctly. Cutting corners at this point would sabotage the model. Also, changing the model frequently introduces more possibilities for human error.
A good data warehouse automation tool helps to overcome these issues, leverage the pros of a Data Vault, and essentially take the necessary steps towards DataOps.
DataOps (data operations) refers to an operating model that uses various personnel roles to support data-driven business development. Read a more in-depth definition of DataOps.
Automating the SQL generation needed in Data Vault development is something that many companies have noticed necessary when trying to build a Data Vault. There are multiple tools out in the market to automate this task. But why to settle just for techie type of scripting automation when it is possible to bring along an enterprise grade, visual data warehouse automation platform which is able to drastically change the data warehousing from IT department’s rehearsal to collaborative, business driven effort.
DataOps is about agile way of working (1), DevOps practices (2) and there is definitely a need for an automation tool (3) to enable frequent changes and multiple environments. The fourth ingredient in DataOps is the data understanding. And because data is a footprint of the business, the business needs to be involved.
When you choose a data warehouse automation tool with a visual interface and graphic data modelling capabilities, more people from the organization can participate in the data warehouse development. A good tool will not change your organization, but surely provides a good foundation to build on the cultural change towards genuine DataOps.
For me, this kind of democratization of data is one of the most exciting prospects that modern data warehouse automation brings. We are already witnessing steps that are moving data development further from hardcore coding and towards shared business development. Who knows, maybe in a few years the competences of business analysts include data engineering.
TietoEVRY partnered up with 2150 GmbH, and we can now support our customers in modernizing and automating the datawarehouse development using Datavault Builder automation tool.
The Datavault Builder automates work and brings efficiency into data warehouse lifecycle. For organizations ready to delve deeper into the opportunities of data-driven business, the Datavault Builder is a tool for modernizing their data warehouse development to business driven, collaborative effort while providing improved efficiency in the maintenance phase.
No coding expertise needed. Visual models and reporting means that there is no need for SQL expertise to be able to participate in the development. Business and IT people share an understanding, and everyone can contribute irrelevant of their coding skills.
Faster time to value for business. As the Datavault Builder enables incremental improvement of the data model, the modelling work doesn’t have to be finished (nor can it be) before it starts to provide insight for better decision making.
Flexible teamwork on the data model. Integrated version control allows different teams to work on the data model simultaneously, and the changes can be easily merged.
Built-in features cover many phases of data warehouse development. The Datavault Builder features eliminate need of separate tools as it covers functionality for designing the data model, building data integrations (ETLs), scheduling integrations and documenting the model. This brings cost-efficiency and streamlines work.
“Migration from legacy data warehouses to modern, cloud-based data warehouses with Datavault Builder takes less time and money than many expect. When we were choosing a partner, competitive pricing of the tool was an important criterion for us.”
Experts from TietoEVRY and 2150 GmbH share their insights into modern data warehousing, DataOps, and the Datavault Builder automation platform.
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Teemu Ekola is the Head of Big Data Solutions at TietoEVRY, leading a team of experts in the areas of Big Data, AI, and Data Advisory. If you want to know more about cloud data platforms or other related topics, you can connect with him on LinkedIn to find out more.