According to Gartner, Augmented Analytics Is the future of Data and Analytics – the next disruption in Analytics and Business Intelligence. It’s the capability of automating insights using machine learning and natural-language generation. If we look back over the last two decades, there are three different waves in the Analytics and Business Intelligence space.
Analytics Industry Waves
The first wave of disruption was 25-30 years ago, when organizations were building centralized data warehouse platforms. At that time, analytics platforms were expected to provide the ability to access predefined dashboards and reports via a common semantic layer, ensuring a single source of truth for all the reports and metrics across the enterprise. A data model was designed to fulfill a well-outlined set of business requirements. Similarly, data consolidation largely involved pulling data from ERP and CRM platforms, mostly relational table structures. It was very time consuming and involved a lot of manual effort even to automate the data consolidation and cleansing process.
During this period, looking back at what had happened, with biases around what we should be looking at and how we should analyze our data was the norm, since users had to outline exactly what they needed to see before the start of a project.
Also, this was very IT-centric and required a long turnaround to fulfill any new insights requested by business users in order to make a timely decisions.
Then came the second wave of disruption, which was about a decade ago. The gold rush by various players in the market was to fulfill the void that existed within the business user community – the need to empower the business users, provide more self-service and ad-hoc analytics capabilities.
Empowering users by allowing them to create their own visualizations, mashup data and create their own reports by connecting to various sources. All, without the need to learn any kind of coding. Essentially, helping democratize self-service analytics.
Explore key considerations, integrating the cloud with legacy applications and challenges of current cloud implementations.
There was one challenge though that continued to grow, the complexity of data structures. The type and speed at which the data complexity continued to grow was exponential. That led to complexity of not being able truly empower all business users.
Which brings us to the third wave of disruption, the period of augmented analytics. This is the period we are in now and for the next decade. In this wave, we rely on machines and algorithms to do the job for us.
- The job of understanding which data sets to connect to and to automatically establish the connection.
- The job of preparing the data for us without any manual intervention.
- The job of scaling the environment to support the performance needs so it can complete its own tasks.
- Most importantly, the job of finding the insights at the speed that will have an impact on business.
Data Visualization Does Not Equal Analytics
Data Visualization alone is not analytics. It does empower users, you need more depth beyond that. With so much data today, there are way too many variables and relationships that exist between them, which makes it practically impossible to manually process and get insights. With digital transformations going on – the speed, volume, and variety of data is not getting any smaller, it only continues to grow exponentially. The modern business analytics platforms need to understand these growing data complexities and simplify the process of getting timely insights into the hands of the decision makers.
Lets explore a very simple HR use case of finding insights into employee attrition. We need capabilities to not only build visualizations to tell a data story, but we also need the machine to find the correlation and tells us where there is a cluster based on employee profiles that we are not thinking about, but has powerful insights. For example, with Oracle Analytics Cloud, you can certainly analyze all your workforce profile data, but you can also ask the platform to, ‘Explain Attrition’ where it does the job of automating the process of connecting all your HR data, finding relationships with each other, and discovering powerful insights into employee attributes and behaviors that directly impact attrition – all without writing a single line of code. Take it even further and use the tool to make predictions based on historical data and all these powerful insights with lightning speed. This goes beyond visualization and has much more depth.
Augmented Analytics, one of the key strengths of Oracle Analytics Cloud (OAC)
Oracle is leading the wave of augmented analytics by democratizing machine learning for everyone. Not just data scientists or programmers. With powerful machine learning, natural language query and autonomous data warehouse capabilities, Oracle Analytics Cloud can do the job of finding powerful insights for you and your business. In Gartner’s 2019 Magic Quadrant for Analytics and Business Intelligence Platforms, Oracle Analytics Cloud was mentioned as the innovator in the space of Augmented Analytics which really is machine learning, AI technologies, natural language processing and autonomous data management. Other strengths of Oracle Analytics is cloud presence with optimizations and integrations to Oracle enterprise applications. Also, product vision around augmented analytics is cited as one of the key strengths.
With this platform, you can continue to get a single source of truth for any static dashboards and reports. You can continue to empower your users with performing self-service analytics and data visualization, along with simplifying the process of data consolidation by leveraging leverage machine learning to automate data preparation and insights generation.
Many users are not aware of what’s possible with OAC and also how to architect this new platform and go about it. This is evidently clear, coming out of the OAC Workshops that we have been running as well as number of assessments and strategy engagements we are driving right now. We are seeing very positive feedback and the adoption is growing as users become aware of these augmented analytics capabilities around machine learning and data preparation as well as data visualization on OAC.
With Oracle Analytics Cloud and its autonomous data warehouse capabilities, you can today, not tomorrow, not in the future, but today, right now, leverage the powerful machine learning capabilities of automating data preparation, automating insights generation, using natural language to interact and provide you with the valuable and timely insights.