Automate & Uncover Accelerated Insights through Augmented Analytics




Augmented analytics assist in generating actionable insights from data and result in the deployment of these insights in decision-making and planning business strategy to benefit the growth of the organization. 

What is Augmented Analytics and how does it work? 

We know data is the utmost asset, it is generated on an everyday basis from a variety of sources. Manually analyzing and processing raw data to derive relevant insights is often a time-consuming and multi-step procedure. To have a well-informed understanding, the foremost step is to comprehend the data and recognize the requirements. Hence the concept of augmented analytics comes into place to ease the process using the latest intelligence along with storytelling and automation. Augmented analytics is the segment of analytics that uses assistive technologies such as ML and AI algorithms. These tools involve an extensive variety of data science techniques, including records mining, statistical modeling, gadget learning, Natural Language Processing (NLP), and more. These help in the preparation of data, generating and explaining insights, and improving the way data is explored and analyzed on analytics and Business Intelligence (BI) platforms. Augmented analytics has proven to benefit professional data scientists and citizen data scientists by automating several parts of data science, model construction, administration, and deployment. 

Elements of Augmented Analytics 
· Advanced data analytics tools- Artificial Intelligence (AI) and Machine Learning (ML) 
· Natural language processing (NLP) with subtypes 
· Natural language understanding (NLU) 
· Natural language generation (NLG)
· Automation 
· Data Visualization and Exploration 

 Features of Augmented Analytics

 Augmented Analysis caters to increased AI, data science, and machine learning services which are as follows: 
· Automatically associates with the relevant data 
· Facilitates predefined data models trained to automate with minimal human intervention 
· Delivers immediate insights to drive business decisions 
· Response to data changes and real-time updates 
· Insights accessible to any stakeholder in the organization 
· Traditional dashboards become less relevant with augmented analytics 
· Augmented analytics makes graph analytics more prevalent 
· The analytics propels IoTs to a more central role 

 Capabilities of Augmented Analytics 

Augmented analytics capabilities simplify tasks such as data preparation, querying, and report findings within a fraction of time. 
Automate routine tasks - Machine learning can help to clean and prepare data, find patterns and relationships, automatically generate code, suggest insights, and create visualizations. AI can assist to get insights faster by automating routine tasks related to data preparation, analysis, and visualization. Context-based insights - When the analysis takes into account user intent and behavior, the insights generated are contextually relevant. Based on the user's question, the machine presents the user with novel ways to view the data and hidden insights that may have remained unexplored so far. 
Real-time natural conversational analytics - Augmented Analytics provides a quick and easy way for users of all skill levels to ask questions and receive natural language answers. When the user is typing or talking, the relevant data fields are displayed, suggesting and validating what the user is trying to find. Each time a question is asked, the algorithm presents relevant charts, graphs, and information to help users gain insights more quickly. 

Benefits of Augmented Analytics

Quick data preparation- Augmented data preparation integrates various data sources which help to easily spot repetitive processes, gain faster insights, and increase productivity. These procedures ensure best practices to deliver fully automated and high-quality suggestions cutting down the need for manual intervention.
Detect and eliminate any anomalies- Augmented Analytics observes patterns, trends and detects anomalies. It provides enough potential to eliminate and stay alert about real-time hidden glitches in the data and minimize their impact on the business. 
Effective actionable insights - Machine learning algorithms used in augmented analytics help to identify the most relevant insights. It eliminates the risk of missing valuable insights in data, optimizes decisions and actions, and explains actionable choices to users. 
Facilitate insights helpful for a wider audience- Augmented Analytics provides elaborate data insights which can cater to all stakeholders in any organization. The analysis also provides user-friendly dashboards and visualizations allowing them to use it as per their needs. 
Improve productivity and reduce cost- With the advent of AI, time-consuming repetitive tasks can be automated to increase productivity. Augmented Intelligence aided with AI helps accelerate and scale up tasks resulting in improved business with long-term returns and reduced cost. 
Reduce cognitive biases - Augmented analytics has the potential to perform a wider range of tasks with data that focuses on statistical significance factors. The use of automated workflow functions reduces biases to draw data insights and actionable recommendations. 
Scalability and exactness - The augmented analytics tools are cloud-based, making them more scalable. These tools evaluate and calculate available data to provide accurate business insights. Companies that have leveraged Augmented Analytics have gained numerous benefits.

Industry Use Cases 

The innovative potential of augmented analytics demonstrates a wide range of applications across various sectors of the economy and aid to transform the digital landscape by streamlining business operations and expanding access to useful data. 

Manufacturing 

A manufacturing company had a data-driven culture but lacked professional data scientists. It had implemented a discount pricing strategy for customers that was based on the volume levels of their purchases. A business analyst requested the augmented analytics system to review pricing data for their trends. It was brought forward that very few customers availed the volume level discounts. Most of the customers were availing discounts from the salesperson. The company took immediate action to curtail the unauthorized discounts and kept a check on such activity thereby generating tremendous revenue. 

Healthcare

A doctor-in-training could treat a patient while remotely relaying the interaction to a doctor using digital wearable technology (such as Google Glass). The doctor-in-training benefits from a real-time evaluation without the need for someone to be present. The patient has the option of seeking two medical opinions for consultation. The entire procedure lowers the cost of that healthcare visit. 

Pharmaceuticals 

Pharma companies use augmented analytics to improve equipment reliability, prevent manufacturing losses, and identify underutilized plant capacity. They then make adjustments to improve yield, quality, and cycle time. AI-driven analytics optimize operations while increasing visibility into the supply chain's performance. Asset performance management tools improve supply chain efficiencies, while predictive systems analyze failure patterns and warn of impending equipment failures. This lowers the manufacturing costs and provides savings opportunities throughout the product life cycle. Thereby, resulting in Capital expenditures (CAPEX) reduction. 

Banking 

The banking sector realized there was a gap in their insights while targeting older customers for management services, they began using augmented analytics to automate the analysis of their high-volume data. 

Agriculture 

The agricultural industry gains from augmented analytics by reducing the time in building the models. Using augmented analytics, the agricultural sector was able to find the best hybrid seed combinations which they can sell to farmers from the larger database. Additionally, augmented analytics allows the industry to reduce time-to-insight from months to days. 

Human Resources 

HR can use augmented analytics to feed information into software that analyses the data. It can also assist in keeping track of an employee's performance and potential in the company. This abets in the maintenance of the data and the analysis of any voluntary attrition to prevent or accept. Augmented analytics is not only limited to maintaining employees data. Instead, it also offers insight into an employee’s lifecycle as well as what can help in increasing his potential. 

Advanced business intelligence 

Augmented analytics makes advanced business intelligence available to a broader set of stakeholders. Augmented analytics can be used to improve processes by anyone, from the IT team to the payroll division, from C-level executives to third-party employee engagement consultants. Thereby, reducing the need to hire experienced data scientists resulting in lowering the cost component of the analytics function significantly. 

Conclusion

Augmented Analytics helps in data democratization, permitting information to be accessible to data scientists as well as citizen data scientists or average non-technical end-users. In the era of advanced digital transformation, organizations must move ahead with the progression and adopt smart analytical tools. Companies should leverage AI and ML tools to streamline the data analysis process, allowing to discover which aspects are genuinely influencing by comparing via extensive data combinations. Hence, Augmented analytics benefits organizations to yield fast, dependable insights and improve the business across the value chain continuum. To gain a competitive edge, companies should leverage cloud-based AI ecosystems, such as ours, to make the most out of their ever-growing data. This can unfold hidden patterns, build mechanisms to digitally transform, and therefore reap the benefits of advanced augmented analytics.

We hope this article was insightful and helped you to understand the concept of augmented analytics, its benefit, and pertinence, which are providing unmet opportunities to grow across the value chain. Thank you for showing interest in our blog and if you have any questions related to Augmented Analytics, Machine Learning, AI-based platforms, please send us an email at info@futureanalytica.com

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