How The Potential Of Augmented Analytics Will Push The Analytics Adoption By 30 Percent

We hear it everywhere that business teams are hungry for analytics. They crave accurate forecasts and predictions to allow them to make more reliable business decisions. The complication is data has become so complex that most of the time users have to wait for it to be presented and analysed. They waste time waiting, and when the report arrives, it does not give any crucial insight they need and the insights become too delayed to act upon.

The entire exercise of doing business analytics today is a bit time-consuming, for everyone involved. Likewise, getting actionable insights into the hands of everyone is increasingly vital to improving business operations and future competitiveness. It's time for something to change in a big way.

This is why you're seeing a new wave of disruption in data analytics tools with the concept of augmented analytics gaining momentum.

Explore our Augmented analytics solutions for instant data insights that aid confident decision-making, merging ML and NLP together to automate the processes of preparing and analyzing data.- Book a Session with our business analytics experts

Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management and deployment. - Gartner

It is an umbrella term for technologies that automate data analysis and give human analysts valuable business insights. These insights can help in numerous business functions, from decision-making about business deals to identifying prospects. But the question is - why can't humans discover these insights, you might ask?

They can, but only to some extent. The key differentiating point of augmented analytics is that it streamlines the business processes, making it easy for the users to apply and get the results they need faster.

For instance, if the analytical team previously spent 70% of their time collecting and correlating data and 30% on analysing it, augmented analytics has the capability to take over that 70% and free up the remaining time for the team to dig for valuable insights.

1. Machine Learning (ML) - For instance, if you are trying to decide on the best pricing strategy for any of your services, you can use Machine Learning algorithms to automatically examine your customer relationship history and competitors' offers and suggest an appropriate price for a particular customer.

2. Natural Language Processing (NLP) - NLP is a conversational AI technology that authorizes human data analysts to interact and query the data using natural language -- either in the form of voice or text. These attributes have given rise to self-service analytics. It has found its way into platforms like - Qlik, Tableau, Microsoft Power BI, and more.

3. Automated Insights - Here, the technology draws together NLP and ML so that system users can get the answers to their questions much faster. For instance, your sales team could ask, "What are the growth projections for Q1 2020?" and receive a visualized answer.

Augmented analytics helps alleviate an organization's dependence on manual processes or data scientists by automating the insight generation process with the assistance of AI and advanced machine learning algorithms. It can also cut down the inconsistencies and potential errors produced by human intervention to generate insights.

It is invaluable to deliver unbiased decisions and an objective picture of the entire scenario, therefore transforming how users interact with data, materialize insights, and consume data.

The scope of augmentation is extending. Originally intended to assist analyst personas using self-service, augmentation and, increasingly, automation are now being applied to help end-users directly, giving rise to a new user category: augmented consumers. This change has the potential to push Analytics and BI beyond the approximately 30% adoption ceiling that has been in place for many years.

The most effectual augmented analytics combines the best aspects of ML and human curiosity to assist users get quicker insights, consider data from unique angles, increase productivity and assist users of all skill levels to make smarter decisions based on AI analytics. Have a look.

1. Immediate automated analysis: The heavy lifting of manually sifting through vast volumes of complex data (due to lack of time or skills constraints) is significantly reduced as the analysis is automated and can always be set run. If your augmented tool finds a spike or drops or change, it can also automate the delivery of that insight, ensuring that users can then act immediately.

2. Faster data preparation: Augmented data preparation brings data together from disparate sources swiftly. Algorithms can be utilized to detect schemas and joins, integrations and repetitive transformation can be completely automated, enrichment recommendations and data quality are auto-generated by the system, and you can even automate the tagging, profiling, and annotation of your data before you start the process of data preparation – resulting in reliable analysis in a fraction of the time.

3. Improved data literacy: With Natural Language assistance in place, providing automated analysis of results and the explanation of discoveries - can improve their data literacy. This can help in fostering a data-led culture that benefits the organization as a whole for the long term.

4. Conversational Analytics: Data Analysts can utilize Artificial Intelligence and Machine Learning, along with Data Science for Conversational Analytics. That means data users of various skill levels can access the data and obtain insights without being expert data scientists. They are required to ask questions from the data in natural language, and the combination of AI and ML will allow them to obtain answers from the data in the form of charts, graphs, etc. and output information also in natural language.

1. Smart cities- Smart Cities worldwide are utilizing Augmented Analytics to process huge volumes of collated data. As more Smart Cities adopt and follow this transformational technology in their city administrations, the city management practices will genuinely enter the Digital Age. With these advanced technologies in place, city planners will simulate a "Smart City," predict future natural disasters and better manage their existing resources.

2. Other Industry- In numerous industries, top management uses Augmented analytics to make the data relevant through sophisticated dashboards to make fast decisions. As most of the collated data are sensor-driven, smart technologies such as AR are highly sought after to manage, sort, collect and display that data in a capsule format so that the management can capture the most critical insights before making their business decisions. Organizations such as - Volvo, NASA, and Caterpillar are using Augmented Analytics for increased business efficiency and productivity.

We can say that the world of data analytics is no longer restricted to data scientists, IT, and analysts. If a company is going to be successful and productive today, it must allow its business users to access easy-to-use tools with sophisticated features and functionality so that the entire team can work from the same roadmap and stay on track.

Polestar Solutions helps large and medium organizations to generate actionable insights from their data with our advanced and augmented analytics solutions. So, if you're planning to implement Augmented Analytics in your company or want to know more about the topic, get in touch with one of our data analytics experts for a complimentary consultation.