How Augmented Analytics Can Help Businesses?

In today's competitive business landscape, success is directly affected by the accuracy that goes into your decision-making process. The best business decisions are driven by data aggregated from a vast array of white papers, market research reports, surveys, and domain experience. Augmented analytics is a leading data analytics subdomain emerging in the current market.

The international market for augmented analytics is projected to grow to $ 25 billion by 2026, as per the EMR Global Augmented Analytics Market Outlook. The same market had already reached a market capitalization of $ 5.1 billion in 2020.

In this article, we will discuss what augmented analytics means and its relevance to the industry. We will go on to discuss the various benefits of implementing augmented analytics.

What Is Augmented Analytics And Why Is It Relevant?

Augmented analytics can be defined as a holistic approach to data analytics, wherein the insights are richer owing to well-prepared analytics data. The industry leaders in data and analytics are adopting this technology to stay ahead of the expected wave of disruption that it will bring to the data analytics domain.

The increasing complexity of business data is closely coupled with the advancement of analytics tools and new funding avenues. Additionally, the data analytics industry is also witnessing an extension of the customer base across verticals where an innovation-friendly environment is slowly aiding the market growth.

Amidst a market where the banking, e-commerce, software services, supply chain, and other industries hold data with such value, a rich analytics methodology like augmented analytics is a dire need. Some of the key industry players investing heavily in augmented analytics include Salesforce, SAP Analytics Cloud, Microsoft, Oracle, Tableau including many more.

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Understanding Data With Augmented Analytics

The initial step of data preparation with augmented analytics involves defining the data requirement specifications. This is followed by the gathering of relevant data from proprietary sources. Even in the early stages of any type of data analytics, tasks are so complex that it requires the expertise of experienced data scientists who charge heavily.

But with augmented analytics, even small to medium-sized businesses can fully leverage the power of data. It is not just expensive business intelligence tools with self-service capabilities that help bridge this monetary gap. What helps is the insistence on augmented analytics strategies to inculcate customizable interfaces and visualizations for better understanding. 

The holistic approach that is taken by augmented analytics data preparation ultimately helps make better business decisions. While drawing useful and comprehensive insights from data, business leaders are also reducing the drain on IT resources and related data science expenditure.

The features of augmented analytics that make all this possible for businesses are the following:

• ML algorithms are integrated into the analytics workflow. This is done with the end goal that the entire workflow can be automated at some point. The analytics process effectively learns and adapts as it processes the enterprise's entire data. With every iteration, the data analysis process becomes faster.
  
  
• Natural language processing implemented with the analytics process helps translate the analysis into actionable and easily understandable terms. The learning curve related to mastering augmented analysis is flattened immensely with this approach. Business intelligence can then be adopted at several levels of an organization, even the non-technical departments.
  
  
• The complex processing and analysis of data lead to insights but it is a lot slower if done manually. This is where insights automation comes in handy. Algorithms are assigned to handle repetitive iterations of data analysis and reports are prepared in no time straight from the available data. Regardless of the size of the organization, insights are prepared in very little time and can be scaled as required.

The Benefits Of Augmented Analytics

Companies can become more agile and can make better, well-rounded business decisions with the many benefits offered by augmented analytics. Business intelligence platforms such as Salesforce and Tableau name augmented analytics as to the key differentiator of their performance. Based on how well it is implemented, augmented analytics offers some of the following benefits for organizations of all sizes:

1)Greater Access To Analytics

More users can access the benefits of data analysis because of the cost-effective implementation offered by augmented analytics. Companies where, formerly, only financial planning departments had access to data analytics can afford to extend this power to order management teams as well. The associated learning curve with augmented analytics is also very flat, further increasing the extent of its accessibility.

2)Accelerated Decision Making

A 2021 RevealBI survey revealed that over 41% of the surveyed companies witnessed a hike in requests from clients for access to data analytics. This was mainly due to the word-of-mouth generated popularity of analytics in enabling better data-driven decision making.

Augmented analytics goes a step further in finding the root causes of issues, which boosted turnaround times on various decisions. With certain accelerated push functions, a business decision can be driven following a cursory glance at the data as most of the components of the analytics process are automated.

3)Cost-effectiveness

The data preparation procedures defined for augmented analytics, help enterprises save tons of money that they would have spent on hiring dedicated personnel. Cleaning data with augmented analytics methodologies and then arranging it into reports and tables can accelerate the journey to getting insights.

On average, companies tend to yield an ROI that is three to five in one year, and possibly tens of times more in less than a decade. The longer the enterprise continues to implement augmented analytics strategies, the faster it can hope to get to the top of its respective market.

4)More agile workflow: 

The quality and cleanliness of data brought to companies by augmented analytics help them make their workflows more agile. Continuous integration and delivery of software products are accelerated greatly by the insights produced.

The ease with which augmented analytics can clean and merge data can improve the quality of the data furnished and also the insight reports. This can help enterprises accelerate digital transformation strategies.

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Augmented Analytics Can Accelerate Growth For Your Enterprise

If your enterprise implements augmented analytics in gaining insights into its business data, it can stand to improve its turnover, reduce its costs, and gain more actionable insights. They can stay ahead of disruption by accelerating growth using these valuable insights.

The right method for data analytics that suits your business needs can help yield results that can sustain your organization's pole position for decades. Comprehensive digital transformation demands a robust analytics methodology. For enabling end-to-end digital transformation for a seamless business workflow and a faster time-to-value, you can book a free consultation with us today.