In an earlier blog, we reviewed how Oracle Departmental Data Warehouse enables business teams to get the deep, trustworthy, data-driven insights they need to make quick decisions. We described how the governed, secure solution reduces risks and complexity while increasing both IT and analysts’ productivity - allowing IT teams to rely on a simple, reliable, and repeatable approach for all data analytics requests from business departments.
Did we stop there? No we didn’t.
We aim to keep providing more value to analysts, as well as to line of business developers, data scientists, and DBAs. To that end, we recently released a new suite of data tools included for free in Autonomous Database.
Indeed, while Oracle Departmental Data Warehouse enables business users in finance, HR and other departments to independently set up data marts in minutes and rapidly get insights from a single source of truth, they may still have needed to turn to IT for operations such as data loading and transformation. The new Autonomous Data Warehouse data tools further decrease business users’ reliance on IT teams – representing a benefit for both groups.
This new suite of built-in, self-service tools includes:
1. Data Loading
Business users can perform drag and drop data loading to swiftly load data themselves from local files such as spreadsheets, databases, and object storage (Oracle and non-Oracle) in Autonomous Data Warehouse. No need to call on IT.
Once loaded, analysts can easily inspect the data and identify quality problems. They can then remediate any quality issue with data transformation, which we’ll talk about next.
2. Data Transformation
Analysts can, independently from IT, perform drag and drop data transformation with zero coding required. Business users simply drag and drop to specify what they want to do, without worrying about how the tool does it under the cover. Transformations can for example include filtering out unneeded data and cleansing.
The new Data Transforms tool leverages the full power of Oracle Data Integrator (ODI), and a new, easy to use web interface. Code is automatically generated for all data sources and targets supported by ODI, including databases, applications, and other sources.
3. Business Modelling
Considering that analysts typically work with semantic/business models rather than directly against tables in a database, our new Business Models tool automatically discovers relationships within data (hierarchies, facts, attributes, levels) and makes it extremely easy to build a business model. This business model is created in Autonomous Data Warehouse, representing a consistent view that can be accessed by all analysts, application developers, data scientists, and used with any analytics tool. It therefore prevents the potential inconsistencies arising from different business models being defined in different analytics tools by different teams.
Another very important benefit is query performance. By recognizing the hierarchy defined in the database, Autonomous Database can automatically pre-compute and store top-level aggregates. It then transparently re-write queries to access them, delivering exceptional performance, even with huge data sets and federated data sources.
4. Data Insights
How would you like to automatically get insights from the data you’ve just loaded? This is exactly what Data Insights does. Autonomous Database uses its knowledge of the business model to automate the ‘slice and dice’ process that an analyst usually performs manually. Data Insights automatically discovers anomalies, outliers and hidden patterns in data using built-in Machine Learning algorithms. It then represents deviations from expected values via charts in Autonomous Data Warehouse, enabling analysts to further investigate. This capability saves them significant time and efforts.
5. Catalog
The new Catalog, built-in Autonomous Database, centralizes metadata to deliver a full and consistent view of your organization’s data and its location. It provides 2 key benefits:
◉ Data lineage: A clear view on data provenance, what data sets were combined, what transformations were completed…etc means that all stakeholders trust the data – and therefore the insights and predictions – which is a major benefit. Teams can focus on making decisions and taking actions, as opposed to arguing about the data (sounds familiar?). Additionally, data lineage is extremely useful in terms of data governance and helping to ensure regulatory compliance.
◉ Impact Analysis: A tree of dependencies helps understand the impact of potential changes, and fix data processing issues by replaying lineage steps.
In summary, the new Autonomous Data Warehouse data tools help make business teams even more independent from IT to rapidly go from data to insights. It equips them with powerful enterprise-class data management capabilities that they access from a simple, drag and drop interface. Nothing more to buy, install and integrate. The more independent business users are, the faster they can make decisions and take actions.
And that’s also great news for overworked DBAs and IT teams who may have a hard time keeping up with business users’ demands for increased access to always more data from disparate sources. IT can provide business departments with the autonomy they desire, within the framework of a governed and secure solution, reducing risks while improving their own productivity and ability to focus on higher value tasks.
In addition to the new data tools, analysts, line of business developers, data scientists and DBAs can leverage the following Autonomous Database built-in, self service capabilities:
◉ In-database Machine Learning: to build and deploy high-performance Machine Learning models using Python or SQL, without moving the data to another system.
◉ Graph analytics: to learn from the relationships in data by easily converting relational data into a graph model with automated tools.
◉ Spatial analytics: to manage different types of geospatial data, perform hundreds of location intelligence analytics, and use interactive map visualization tools.
◉ Low-code application development: to build and deploy modern data-driven applications up to 38X faster than with traditional coding in Oracle APEX, a preconfigured, secured and fully managed low-code application development platform.
◉ Security assessment: to discover sensitive data, mask it, evaluate security risks, and implement security controls with Oracle Data Safe.
Oracle Autonomous Data Warehouse eliminates all the complexities of operating a data warehouse, automating provisioning, configuring, securing, tuning, scaling, patching, backing up, and repairing of the data mart/data warehouse. The complete suite of built-in, self-service tools, including the new data tools we discussed in this blog, enables analysts, data scientists, and line of business developers to independently deliver even faster results, accelerating insights and time to market. Hence for them, it’s indeed Independence Day every day! And IT teams can enjoy the fireworks with a solution reducing risks and improving productivity.
Source: oracle.com
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