Every organization is going through a digital transformation process to stay in global competitive market. The existing process of analyzing data, transforming it, interpreting it into meaningful formats and then use it to standardize business process is time consuming. As data grows, it becomes more complex and thus it is even more difficult to find relevant actionable findings. Most leaders are aware, that static reporting is not enough to keep them up to the pace with competition in the market.
There is a huge demand for automation to draw insights from data quickly through programming or natural language processing. With automation executives and stakeholders will spend less time on handling data and will have ready to use insights available at their fingertips. This will in turn make them more responsive and agile.
In such a scenario Augmented Analytics can be a disruptor and will take analytics to higher levels of functionality. Augmented analytics democratizes data and enables all businesses, no matter their size, to extract meaningful insight from its data sources. Businesses have become more data driven due to Augmented Analytics.
Let’s understand the disruption points in today’s traditional approach where Augmented Analytics has proven benefits:
Semantic Layer Based platforms – This layer is driven by organization IT team in terms of a formal model to meet certain KPI. The data relationship is pre-defined. This is more descriptive model.
Visual based Platform – These analytics are driven by business needs and are used for diagnosis. Here analytics is in visuals specifically dashboard to monitor and for analysis.
Augmented Analytics – These analytics are driven by business analytics. They are an extension of predefined data model / predefined relationship of data. The insights are data driven through machine learning and diagnosis performed on user queries.
In next two to five years the demand for Analytics will be more consumer led with a focus on near real time data. Insights of data will be delivered in stories or news feed. There will be dynamic dashboards that can be pinned to other dashboards. The NLP, NLQ and NLG will be further explored over conventional analytics. The analytics will be embedded into apps.
The next wave of Analytics will be integrating AI elements and enable the business to accomplish business values faster, deeper and at large scale. The data injection, data discovery, understanding of data correlation and integration with different platform will all become streamline and modernization will enable powerful self-service. The workflow of augmented analytics will be as below:
With augmented approach, the data injection will be more simplified. The AI component will do most of the heavy lifting which was earlier done through manually cleanup of data, preparing data set, mashing up data for analysis. The AI components will perform analysis and suggest relationship on hidden data, so business users can explore vital business results to improve decision capability and productivity across the organization. Read the blog: Sell More! All-In-One Sales Analytics Solution
The high-level benefits from augmented analytics will be as below:
- Accelerates data discovery and data preparation
- Democratizes of data analytics
- Offers actionable insights to address core business requirements
About the Author
Sachin Shewale, Sr. Manager – Client Services
Sachin Shewale is Delivery Manager for BI/EPM practice. He manages the offshore delivery for BI/EPM projects. He has 20+ years of professional experience in IT industry as Solution Architecting, mapping of business requirements to system design and specifications, team development & mentoring. He has more than 15 years of experience in Banking and Capital markets , Retail industry, Manufacturing, Healthcare , working experience in various capacities and on diverse BI technologies for different leading customer in USA and Middle East. He has rich experience in Relationship & Project Management while handling projects in different portfolios with internal customer and external customer.