So raw data becomes action options With DataOps to the data-driven enterprise
04.05.2021 author /
Wolfgang Kobek *
/ Sebastian Human
Business with and around data is flourishing. But to get a part of the cake here, it is not enough to just hoard mountains of data. Intelligent data integration and automation solutions can bring order into chaos.
Companies on the topic
Even if the storage media are becoming more and more powerful: owning data does not yet make a business success, only their well thought-out storage and evaluation enables companies to make a real profit.
Experience has shown that it can take a while before a person acts according to plan from his insights: to see something does not always mean to recognize it. To detect something means to understand it and to understand something, does not mean to act accordingly. This includes experience, a holistic view of the world – and always a bit of intuition. The path to data-driven corporate control works in a very similar way.
Faster and faster, more and more and more different: who deals with data, has quite something planned. Data Lakes full of structured and unstructured digital information, big data environments with real-time requirements or complex IoT scenarios: The generation and use of data drives the Analytics economy to new heights-and enables data-based value creation in almost all business processes, from purchasing to after-sales control.
“Data Hoarding” is not a strategy
But this is not self-defeating. For example, big data: Those who rely on scalable cloud solutions-and cloud – first concepts are part of their corporate strategy in more and more organizations-are no longer dependent on the capacities of their own IT infrastructure. Where previously storage limitations limited ambitious projects at some point by themselves, storage capacities and computing power are now available without end – which is comfortable, but without a strategy can also lead to downright “data hoarding”. Then data is collected first – the ” Why?”think about it later.
But that’s not all: While the cloud storage is flooded with ever new information, many companies also have a widely branched, often highly fragmented data environment, which often consists of inconsistent, inconsistent data sets and isolated data silos. Two questions therefore arise almost automatically:
- 1. How can existing data silos be broken up?
- 2. And how can information from all available sources be integrated into a modern analytics scenario in such a way that (error-prone) manual processes are eliminated as much as possible?
End-to-end experience based on data warehouse automation
As intelligent as possible data management is required here, which is as agile as it is high-quality. In short: a real “DataOps” approach, which already understands raw data as the first step to the data-based action option.
The idea behind this is to support later associative analytics scenarios with the previous steps (data management, processing and provision,…) from the very beginning. A decisive factor here is speed. Why? Human intuition does not take detours – nor should its data be forced to do so.
Technologically, this means using automated and process-oriented technologies, i.e. using on-demand IT resources, automating tests and automatically providing suitable, valid data sources (internal and external). Real-Time Data Integration, Change Data Capture (CDC) and Streaming data pipelines in real time are the most up-to-date methods for this and turn DataOps into an end-to-end experience that is actually based on consistent data warehouse automation: from the raw data source to the concrete data-based action option. Organizations implementing such a DataOps concept can deliver about four-fifths of the business data to their users as relevant core data.
The largely automated, operational data management on the one hand and user-friendly analytics applications at the location of the business decision thus ensure a data – based value chain – across companies and locations.
Cataloged metadata as a bridge between data sets and analysis application
In order to localize raw data in heterogeneous and widely branched data sets, merge them and examine them for comparability features, metadata catalogs are an intelligent solution approach. Metadata catalogs that work with AI engines ensure rapid and complete data delivery and enable agile data projects that can be executed and shared without infrastructure hurdles.
If you want to implement a”DataOps” concept and consistently think about the path to data-driven organization from your raw data sources to valid business insights (end-to-end), intelligent data integration should be understood as a pivotal point. Data warehouse automation can drive the appropriate speed – and also minimize the risk that manual processes mean in order to make data truly analytics-ready.
This post originally appeared on our partner portal industry-of-things.de .
* Wolfgang Kobek works as SVP EMEA at Qlik.