Srikanth
We are all living in a generation of data. Big data to be precise. With the data becoming huge, the analytics becoming complex and the technologies fast-moving, the traditional methods giving BI solutions are no longer capable. Traditional methods are failing in the basics like pulling the data, dealing with it, preparing it or sometimes even understanding it. With data present everywhere and is constantly being produced it has called for an emerging need to have a proper handle to address these complexities.
Any organization that want to solve these issues need to unveil the valuable insights hidden in their data. This is going to be an incredible asset, to begin with. Of course, data digging is a herculean task, but the right tools can save us. Identifying the solution that answers all the data needs is what matters most. This should also suit the individual and organization’s business logic. Augmented Analytics has something magnificent to contribute here.
What is Augmented Data Analytics?
It is the future. The future of Data, Business Intelligence and Analytics. This analytics uses Artificial Intelligence and Machine Learning as the technique to automate the data. It helps in preparing the data to discover the needful insights and helps to share it across to bring the required results. Augmented data analytics can also automate the entire data science, development process, management as well as deployment seamlessly. This tool has the power to aid human intelligence in the entire life-cycle of analytics. The crux of the augmented data analytics is that the AI will swap everything about the business intelligence process. It will either simplify the steps or eliminate a few. Thereby focusing on the areas of development. But first, let us understand briefly the Business Intelligence evolution.
Business Intelligence
BI and Analytics have been in existence for a longer time now than anyone ever realized. Let us have a look at the brief history and how it evolved over years.
Traditional BI
The early 1950s is the time when business intelligence just started. The analytics done at that time was purely code-based. It took many months to discover significant insights that can help to change the functioning and simplifying the problematic areas. The data, then, was only available to the Information Technology users. Any data required was available only to such teams thereby creating barriers to work progression. The analytics were way more descriptive than pictorial or graphical. Hence, one can imagine the time consumed to read an analytics document. The analytics is done manually by the department of IT. Therefore, any visualization of a project was only seen through the report given by IT.
Self-Service BI
All the issues in the Traditional BI like lack of technical workers, lengthy insight processing, poor analyzing tools has led to the invention of Sel-service BI. This was set to address the above problems and guess what? It did succeed a bit. The analytics have changed their way of being represented. From being descriptive to more visual-based. The data that took months to bring out key insights is now able to pull the discoveries in days. Data has crossed the barrier of being available to just IT. It is now made accessible to all business users. Analytics are made more diagnostically, thereby adding value to the reports made. Self-service intelligence made to automatically help the data to explore and pull results needed. Visualization made possible with a variety of dashboards besides graphs, suitable to address different business logics.
Machine-Generated BI
This brings out the current scenarios of BI. The analytics these days are more AI-driven and AI-augmented. The insights are given in real-time. Data is made available to any user who needs it. Analytics has changed to be more prescriptive in addition to being predictive which is possible with the automation of AI and machine learning. Pervasive analytics makes the process run smooth while the action taken lies invisible. The visualization is also automated such that it can deliver relevant patterns.
What’s next in this process?
The next buzz word that is creating a wave in the world of BI tools and analytics is Augmented Data Analytics. What does it do to business?
- It has created a remarkable difference to those of the existing tools.
- This analytics helps in the integration of the AI elements into the process of analytics and business intelligence.
- This can help the users to prepare data as per the business demand, identify the potential insights, comfortably share the reports across the organization.
- So, this paradigm can feel different from that of routine tools. That is because of the finest integration of elements holding natural language and AI to give the user a greater experience throughout the process of BI.
- This tool which is self-service oriented can make every process of data analytics effortless driving powerful results.
- Helps in data ingestion, finding correlations, seamless interactions
- More streamlined, Stronger than any counterparts
- Cutting edge results with the right insights
- Moving towards smart data by utilising data cognition
- Making all those impossible datasets as possible productive information.
Workflow of the Augmented Analytics
The workflow in emerging data analytics goes like this
- Data Preparation: Preparation of data includes creating algorithms that can help to detect schemas. Developing a profile, designing the catalogue and doing the required enrichments can help in proper segmentation. It also helps to make an understanding of metadata and data lineage.
- Finding Pattens: Observing the data and the patterns helps to solve queries on Natural-Language. Created algorithms while data preparation helps to find patterns in the data. This helps in the auto-generation of models.
- Share Across & Operanationalize: From the above two actions of the workflow valuable insights are created in natural language and visualizations are made to help the user focus on significant strategies and actions. The data can also be easily embedded in all the applications and user interface that is conversational.
With clear visibility of how the approach has changed in the phase of business intelligence, one can understand the need to make the big picture of Augmented Data Analytics. Countless devices creating matchless digital records, and users creating fresh data every second across the organization needs some robust functioning and Augmented data analytics can be the future of it. Every company and business needs this kind of platform to connect, visualize and effortlessly find the solutions for their personalized business logics. Sooner these platforms change the way the world of BI works, that humans never imagined.
