Companies need solutions that help BI departments produce customised reports or data scientists to extract data sets quickly and easily, and reduce the work of IT teams. They need real-time data management platforms designed in such a way that the data consumers never need to worry about its technicalities.
In this complex new world where big data and real-time data are essential, Most companies do not have the means to address the BI and data science needs. We can summarize what they lack in 3 big challenges:
- Extract value from a BI system, decision-makers need to access the right data, in the right format, and at the right time through intuitive dashboards. But often, the overloaded IT departments are not able to deliver this at the desired time or with the desired efficiency. As a result, executives still struggle to turn their data into actionable business insights.
- Build pipelines to prepare data for data scientists, and to industrialise their models, companies need data engineers. Yet, they often underestimate this need or fail to find the right profiles or there are not enough data engineers to handle the demand. Because of the deficit in engineers, data scientists get bored and leave companies that didn’t provide them with the data they need. Sometimes, they even find themselves in a situation where a company makes them take on the role of a data engineer they had not agreed on. It could lead to a lack of technical knowledge at the backend to handle multiple data sources and heavy volumes.
- Being confronted with a rapidly changing environment and an increasing volume of exploited data, IT teams need to access and build a lot of real-time and distributed data pipelines. Companies need to master streaming and distributed computing technologies such as Spark or Flink, maintain data pipelines with Kafka, or build data lakes with Hadoop HDFS. Unless the company already has a solid data infrastructure and its employees have the needed qualifications, chances are good they will overload their IT department.
Digazu solves these issues with its user-friendly platforms (MLOps platform & end-to-end data science platform). They are designed and built in such a way that data consumers do not need to worry about its technicalities:
- The Digazu platform facilitates the work of data engineers by automating the creation of data pipelines for BI users and data scientists. Data scientists can easily select, transform and extract the raw data they need amongst the available data. When their model has been tested and validated, IT production can deploy the model in production in just a few clicks.
- The Digazu platforms facilitate the data engineer’s work due to its abstracted technical complexity and its capacity to easily be integrated with legacy applications. The platforms were designed to be scalable and customisable. Once the platforms are implemented, it allows decision-makers to create their own visualisations and data scientists to prepare their data sets without depending on the IT department, which can then focus on other requests.
- With Digazu, not knowing how to use cutting-edge technologies is not a problem any more. Digazu is built on top of cutting-edge technologies (including Flink, Kafka, Kubernetes, Schema Registry and HDFS) that we support and follow-up, ensuring high reliability, performance, and scalability of the end-to-end data flow, while being plug-and-play and user-friendly.
Let Digazu (end-to-end data engineering platform, MLOps data platform and end-to-end data science platform) help your organization optimize its workload. Save up to 80% of your work-time related to data pipelining by automating data collection, storage, transformation and distribution. Standardise and industrialise your data science models up to 10 times faster than today.