“We live in an era of big data.”  

In the past year with the growing amount of Big Data, datasets have become so complex that traditional BI solutions either fail in getting, dealing, preparing or just understanding the data.  

But data is everywhere and growing with every click. We need to find ways to handle it and leverage it for our benefit.   

Google, Netflix, Spotify, Facebook, and Amazon are leveraging user data and mixing it with their unique profile to create new products. Such products are more likely to be loved by users.  

Similarly, governments and hospitals, are utilizing augmented analytics to find efficient ways to administer services.  

If an organization wants to thrive in the information age, they need to uncover the insights hiding in data. Digging through this data is tedious and tough but the right tools can make this process efficient and simple.   

In this blog, we are going to help you identify the solution to your changing data needs and their relation with augmented analytics.  

What is Augmented Analytics?  

According to Gartner’s 2018 research, Augmented Analytics Is the Future of Data and Analytics, “Augmented analytics uses machine learning/ artificial intelligence (ML/AI) techniques to automate data preparation, forensic discovery. and sharing. It also automates data science and ML model development, management, and deployment.”   

Augmented analytics is artificial intelligence that will change everything about business intelligence and business analytics.   

Machine-Generated Business Intelligence  

Traditional BI  

Analytics and business intelligence have been around for quite some time now. In 1958, Hans Peter Luhn, IBM researcher, published a white paper “A Business Intelligence System.” in the paper, he posited that because “information is now being generated and utilized at an ever-increasing rate because of the accelerated pace and scope of human activities.”   

According to him, to process this we will require new technological tools as organizations will need all that information for making better organizational decisions.  

Traditional BI was the first iteration of general-usage tools which focused on connecting to single databases and generating reports.  

Analysis was unsophisticated and under a small class of dedicated analysts. But there was immense potential for improvement. This improvement came in the form of the next gen-analytics and self-servicing tools.  

Self-Service BI  

The drawbacks of traditional BI, including the need for skilled technical workers, lengthy insight times, and poor-quality data analysis, were addressed with self-service BI. Today, almost all the BI tool claims to be self-service.   

Today, Self-service BI solutions possess user-friendly graphical interfaces. These systems can handle millions and billions of data drawn from multiple sources: in-house databases and cloud storage.   

This system can be used by non-technical users and get them ready for analysis to speed up insights and eliminate bottlenecks. Modern BI solutions make data security, governance, and access control simpler. 

Different Data Approach  

Lastly, self-service BI systems have dashboard-building features. These features offer a wide array of infographics and easy color selection to make insights look good. This gives users across the board access to insights and eases to tag other users and add them to dashboards. This leaves no need to reinvent the wheel. However, these enhancements aren’t enough and there is an immense need to approach data differently.  

Business Intelligence Changes Over Time  

BI tools are continuously evolving from monitoring performance to being sophisticated Artificial Intelligence-driven analytics platforms.  

Thinking About Data and Analytics Differently  

Modern analytics and BI systems have a lot going for them, but there are still places for innovation. We need to re-think about data itself. Data preparation could be simplified and the business-led aspects of the industry need to be countered.   

While modern BI solutions can handle greater volumes of data, even with self-service systems, cleaning that data is still a manual process. This leaves room for human error before the analysis has begun!   

With Augmented analytics systems, AI components will improve this process. BI solutions are great at showing users the insights but there is a small problem, that’s all they show. If someone wants to know exactly what they’re looking for, then chances are they’re going to find it leaves little room for unexpected results that they might have not been thinking of. This is exactly the kind of information that can have a huge impact on the organization.   

The AI-assisted system can help humans to tackle this problem and get more out of their analyses. Easy-to-use analytics tools, made by AI elements can change organizations and allow users to make smarter decisions.  

Another huge reason is data itself. Human activities never stop making new data as data comes from countless human and machine sources. This poses a huge challenge for IT departments to marry these disparate data sources.

By relying on BI to make decisions, they will understand that they can’t rely on a small cluster of analysts to do all the crunching and database management.   

In a modern company, insights can positively impact workers, and the next big idea could come from anywhere. Traditional BI doesn’t work for the new world. So, what does the future of augmented analytics and BI look like? 

Looking Toward the Future  

Data is called the “new oil.”  

Why?  

Because companies see it as a powerful resource.   

And, if you want to leverage this resource, it’s not enough to have just a small team to plumb data for insights. Data access has to be democratized, especially with the stakeholders. This will enable them to access intelligent, self-service solutions to find answers to an important question that matter most to them, with game-changing analyses.   

Besides, data stored in off-site, proprietary, and third-party databases have to be ingested easily, securely, and with fewer resources for IT. This will quickly reveal insights and you can easily share them with others in the company.  

Democratization and Self-Service:   

With technology becoming more and more ingrained in organizations, the frontline workers have become are more tech-savvy than ever. But there just aren’t enough database experts to handle the tasks to govern and clean the data.   

Companies can leverage an AI-assisted self-service tool to derive insights on the scale of modern businesses where and when they need them with an easy-to-understand interface.  

Delivering Maximum Value:  

Adding data to analytics software is the most difficult of getting insights. In the Modern BI era, we are seeing solutions capable of handling disparate sources such as cloud storage, in-house databases, app APIs, CSVs, live data streams to perform complex analyses.   

But what if BI tools became smart enough to understand the connection between data sources? That’d be a game-changer.  

There has been a great demand for options for analyzing data and sharing results. The interface needs to have multiple ways to access it and make sense. Users also want a social way that provides simplicity to share their insights.   

Advanced modern BI systems allow the user to interact via chatbots and AI assistants like Ok Google and Alexa. AI assistances will even suggest people share findings with a fast process of disseminating findings to key members.  

What’s Next for BI: Augmented Analytics  

Augmented analytics and BI tools, in the future, will be quite different than the current tools. Augmented analytics, today, integrates AI into the BI process to help in data preparation and insight discovery. This will change with the subtle integration of artificial intelligence and natural language processing into augmented analytics.   

In the future of augmented analytics, data ingestion, understanding relationship in data, insight discovery, and interaction with the platform will become more streamlined.  

An intelligent augmented analytics BI system will start helping users when they begin interacting with it. Instead of manual data cleanup process, data ingestion will be radically simplified and AI components will do much of the heavy lifting.   

AI elements, within a few years, will surface these relationships via visualizations. This will allow users to drill down and look for more insights. Seamless processes from data ingestion to insight searching will be a huge time saver.  

Augmented analytics systems will also handle Big Data as companies already have data needs in billions. Despite the amount of data or its origin, these smart systems will handle them. They will also understand the differences between the datasets, how they interact, and how to query them for results. Instead of waiting for the users to perform analyses, these systems will be active in interweaving and analyzing the data.  

If a user is actively searching for new insights, AI systems will help them prepare. However, even at this stage, it will be different under the augmented analytics framework. AI in augmented analytics will be free of human biases and reveal crucial insights that humans never realized were important. They will do this by observing the connections in the ocean of data and suggesting relationships.  

Moreover, advanced machine learning algorithms aren’t biased, or bound by preconceived notions. They will deliver insights where they are found and will even become spatially aware of it.  

Once the insights are served, these smart systems will be able to walk users through the data and help them gain understanding. This is very opposite to them just giving the information. This change will extend beyond using the platform as many modern BI solutions now have mobile apps. Chatbot or Alexa integrations have allowed users to interact with the data in different ways and analytics will keep building on this trend.  

Augmented analytics and BI will become immersive as new insights and data will be accessible.

Conclusion   

Data is now Big Data. Countless users and devices are creating new digital records every second. This data is being processed and stored in complex ways. However, traditional technology is unable to handle the ever-mounting information. Hence, we need more powerful and robust AI-augmented analytic systems to make sense of this data.  

Every organization and government needs an augmented analytics platform to connect to these databases to find relationships within the data. These relationships are then converted into crucial insights with the help of human users and effortlessly share across the entire organization. 

However, in the coming year, they will also need ways to work with Big Data especially the one that goes beyond the usual analytics systems. This data can completely reimagine how users can relate to data. Augmented analytics will change how users experience analytics and change the world by serving up unimaginable insights. 

Author Bio:

The post Augmented Analytics: Future of Business Intelligence appeared first on Tech Research Online.

Every single day, we are generating massive volumes of digital records. To handle this much information, we need more powerful and robust analytics and AI systems to store and make sense of it.   

The term augmented analytics is coined by Gartner. They say that it is the future of data analytics that harnesses disruptive technologies to automate insight discovery, data preparation, and intelligence sharing.    

Augmented analytics has the potential to merge traditional data analytics with technologies such as artificial intelligence (AI), machine learning (ML), and NLP.   

The next wave of BI tools and analytics will be different. They will change the user experience across the BI process with augmented analytics. Here’s how:   

• Data discovery, analysis, ingestion, predictions, and interactions between platforms will be streamlined    
• There will be easy share-ability and dissemination of results across integrated functions   
• Automate and democratize the whole data analytics/ BI process   
• More action-oriented experiences and cost reduction    

Augmented Analytics in Action    

Gartner says that augmented analytics marks another level of disruption in the analytics landscape.   

Data science, AI, and augmented analytics make analytics accessible for the organization. This, in turn, enables them to ask relevant questions and auto-generate insights in an easy manner.    

Augmented analytics systems recommend necessary metrics for your business which can then be analyzed. On the data preparation side, augmented analytics has the power to intelligently drive key insights automatically.    

For instance, assume a data point that indicates that revenue is down by 20%. You can dive deeper to uncover the true meaning behind it and why it is important.   

Augmented analytics will help you put perspective on the reasons behind the decrease. It may be either because your marketing isn’t effective, or is it because it is an industry-wide trend?   

It takes into consideration everything from analyzing the geographical spread, comparing relevant benchmarks, and giving a commentary around it.    

On the contrary, in today’s world, if you just knowing declining revenue you will lose time, money, and energy as it will not be valuable to your organization.   

Instead, you should focus on drawing out the reason for the decline. These are the only actionable insights. With this analytic method, you cannot only help deliver insights automatically but also flag certain threshold breaches.   

What are the Benefits of Augmented Analytics?   

Today, drawing out crucial and relevant insights from data is a huge challenge for businesses. Hence, it is so important for all businesses to invest in this new analytics method.   

It can make the search easier, speeds up a time to value, data literacy more accessible, and visualization faster across the organization.   

It is the best solution for large enterprises that are looking to reduce their analytics load on their teams, or from an e-commerce company detecting out-of-stock events to the order/relevance of news based on user behaviors, all in all, the use cases for analytics are broad.   

What are the Key Capabilities of Augmented Analytics? 

1. Data Preparation:   

Augmented Analytics solves the problem by reducing the process that data analysts need to automate repetitively every time they receive new data sets to work with.    

Also, it helps decrease the time required to clean data in the ETL process and allows for more time to find patterns and relationships, create visualizations, auto-generated code, and propose recommendations in the data.   

Lastly, it automates the process of data preparation, visualization, and analysis.   

2. Contextually-Aware Insights:    

Augmented Analytics takes into account behaviors and intents to create contextual insights. It presents new ways of looking at data and identifies patterns and insights based on questions that companies might have completely missed otherwise. It, thereby, enhance human intellect and transforming the use of analytics.    

It also highlights the relevant hidden insights that are extremely powerful capabilities. For instance, users can manage the selection state (context) at the step of the exploratory process.   

Besides, it also understands data values associated with or without the context. It results in relevant suggestions powerful context-aware.   

3. Enabling a Citizen Data Scientist:    

Augmented analytics can democratize data analytics and automated insight. It can do this by generation them through the use of ML and AI to convert insights into actionable steps. This can benefit companies by reducing their dependence on data scientists and making analytics accessible.   

Gartner says “augmented analytics is the future of data analytics because it moves us closer than ever to that vision of “democratized analytics” because it will be cheaper, easier, and better.”   

What is the Future of Augmented Analytics?

Augmented analytics is capable of communicating, analyzing, and visualizing data. Besides, it can also propose actions.    

Soon, this will have an inherently social component that will link analysis once insights are identified. It will then connect team members to those findings within the company.    

Going forward, we will see augmented analytics systems become more powerful and productive tools.

Author Bio:

Shreeya Chourasia is an experienced B2B marketing/tech content writer, who is diligently committed for growing your online presence. Her writing doesn’t merely direct the audience to take action, rather it explains how to take action for promising outcomes.

The post Augmented Analytics – Is This the Future of Business Intelligence   appeared first on Tech Research Online.