SAP Datawarehouse Cloud [Part IV]
SAP Datawarehouse Cloud [Part IV]
Governance & Architecture with SAP Datawarehouse Cloud
Note: This is the last part of the blog series. If you haven’t read the 3 previous articles, please check them first at Part I, Part II, and Part III.
Data Governance is evolving rapidly with the rise of self-service BI and the technologies it governs. In a traditional Datawarehouse set up, it often takes a lot of time to build, deploy, and gain the benefits of it. Organizations today want a simple, agile, integrated set up to meet their new and evolving business requirements in a fast and cost-effective manner.
A data governance structure needs to be in place to facilitate these and allow. If you had effective data management in a secured and trusted environment. That will allow highly governed Enterprise reporting and Loosely Personal reporting and data Exploration. This will fulfill the business needs and avoid the setup of shadow IT efforts of tech-savvy data analyst under the radar developments with numerous inefficient Excell Spreadsheets and rogue database setups. These setups are hard and challenging to elevate to a higher corporate level and wider usage across the organization.
Analytic self-service levels
As technology has been evolving the involvement, empowerment, and requirements of Business users: Analytic Leaders, Data Scientists, Power users, and Business Analysis have been evolving. One can distinguish three Analytic self-service levels: Output Control, Report Control, and Data Control.
Output Control: Control the output of a specific report by using customized filter criteria. For Example, a predefined and strictly governed WebIntelligence report in which the user can set predefined filter parameters.
Report Control: Control the report layout by building reports and dashboards. This is typically done in a self-service reporting environment as SAP Analytics Cloud.
Data Control: Control the data sourced to the reports or dashboards by acquiring, blending, and enhancing data sources. This is a step further and requires a new tooling set: SAP DataWarehouse Cloud.
Self-Service Data Analytics Process
Various charts and dashboards have been built to support administrative functions and tell what happened or are happening the business user or analyst in a descriptive and diagnostic way. Mostly these cannot disclose why it happened, therefor a more causal analysis is needed and associated with BI.
Exploring the data for a deeper understanding and finding the key to reveal a business, customer or product… opportunity is an iterative exploratory process.
This exploration requires additional data enrichments like location, weather, social media, streaming data information. These need to be cleaned transformed added and combined with the available transactional BI data.
Once a useful result is found, they should be published and made available.
Data Governance needs to guide this Self-service data process so the different steps and great data diversity, can be handled and controlled in an efficient manner. Most businesses are not in the need of storing petabytes but require only data diversity.
The process that is necessary to prepare data for analysis is called “Data Wrangling”. The steps to make structured, semi-structured, and structured data are:
- is the first step and determines what is exactly in the data stores. The data should be described (metadata) and the nature of the content.
- builds a structure that can be used by the analytical processes once the nature of the data has been disclosed by exploration.
- once the data has been proven to be useful it can be desired to make it available in a repeatable, secure, robust, governed, and automated process.
Data governance needs to find a balance between agility and data governance.
Depending on the type of data, usage, and industry regulations data can be loosely or Highly governed. Some industries like banking and pharma need an effective data management solution for supporting legal and regulatory compliance, mitigating risk, and improving efficiency.
Sensitive data might need to be encrypted or anonymized and GDPR or similar legislation requires visibility and traceability on your data flows and life cycle.
SAP Data Warehouse cloud offers companies an analytical and persona-driven data-warehouse-as-a-service designed for business and IT. SAP Data Warehouse Cloud focuses on orchestrating various data sources and maintaining security, trust, and semantic information to accelerate business-critical insights. It offers instant access to application data via prebuilt adapters.
Bog by Youri Van Heester & Jeroen Coppens