Is Augmented Analytics the Future of Big Data Analytics?

Shaivi Chapalgaonkar | August 3, 2021

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We currently live in the age of data. It’s not just any kind of data, but big data. The current data sets have become huge, complicated, and quick, making it difficult for traditional business intelligence (BI) solutions to handle. These dated BI solutions are either unable to get the data, deal with the data, or understand the data. It is vital to handle the data aptly since data is everywhere and is being produced constantly.

Your organization needs to discover any hidden insights in your datasets. Going through all the data will be doable with the right tools like machine learning (ML) and augmented analytics.

According to Gartner, augmented analytics is the future of data analytics and defines it as:

“Augmented analytics uses machine learning/artificial intelligence (ML/AI) techniques to automate data preparation, insight discovery, and sharing. It also automates data science and ML model development, management, and deployment.”

Augmented analytics is different from BI tools because ML technologies work behind the scenes continuously to learn and enhance results. Augmented analytics facilitates this process faster to derive insights from large amounts of structured and unstructured data to gain ML-based recommendations. In addition, it helps to find patterns in the data that usually go unnoticed, removes human bias, and allows predictive capabilities to inform an organization of what to do next.
Artificial intelligence has brought about an augmented analytics trend, and there has been a significant increase in the demand for augmented analytics.

Benefits of Augmented Analytics

Organizations now understand the benefits of augmented analytics which has led them to adopt it to deal with the increasing volume of structured and unstructured data. Oracle identified top four reasons organizations are opting for augmented analytics:

Data Democratization

Augmented data science availability to everyone has become a possibility thanks to augmented analytics. Augmented analytics solutions come prebuilt with models and algorithms, so data scientists are not needed to do this work. In addition, these augmented analytics models have user-friendly interfaces, making it easier for business users and executives to use them.

Quicker Decision-making

You will receive suggestions and recommendations through augmented analytics about which datasets to incorporate in analyses, alert users with dataset upgrades, and recommend new datasets when the results are not what the users expect. With just one click, augmented analytics provides precise forecasts and predictions on historical data.

Programmed Recommendations

Natural language processing (NLP) is featured on the augmented analytics platforms enabling non-technical users to question the source data easily. Interpreting the complex data into text with intelligent recommendations is automated by natural language generation (NLG), thus speeding up the analytic insights. Anyone using the tools can find out hidden patterns and predict trends to optimize the time it takes to go from data to insights to decisions using automated recommendations for data improvement and visualization. Non-expert users can use NLP technology to make sense of large amounts of data. Users can ask doubts about data using typical business terms. The software will find and question the correct data, making the results easy to digest using visualization tools or natural language output.

Grow into a Data-driven Company

It is more significant to understand data and business while organizations are rapidly adjusting to changes. Analytics has become more critical to doing everything from understanding sales trends, to segment customers, based on their online behaviors, and predicting how much inventory to hold to strategizing marketing campaigns. Analytics is what makes data a valuable asset.

Essential Capabilities of Augmented Analytics

Augmented analytics reduces the repetitive processes data analysts need to do every time they work with new datasets. It helps to decrease the time it takes to clean data through the ETL process. Augmented analytics allows more time to think about the data implications, discover patterns, auto-generated code, create visualizations, and propose recommendations from the insights it derives.

Augmented analytics considers intents and behaviors and turns them into contextual insights. It presents new directions to look at data and identify patterns and insights companies would have otherwise missed out on completely- thus altering the way analytics is used. The ability to highlight the most relevant hidden insights is a powerful capability.

Augmented analytics, for example, can help users manage the context at the explanatory process stage. It understands the values of data that are associated with or unrelated to that context, which results in powerful and relevant suggestions that are context-aware.

Modern self-service BI tools have a friendly user interface that enables business users with low to no technical skills to derive insights from data in real-time. In addition, these tools can easily handle large datasets from various sources in a quickly and competently.

The insights from augmented analytics tools can tell you what, why, and how something happened. In addition, it can reveal important insights, recommendations, and relationships between data points in real-time and present it to the user in the form of reports in conversational language.

Users can have data queries to get insights through the augmented analytics tools. For example, business users can ask, “How was the company’s performance last year?” or “What was the most profitable quarter of the year?” The systems provide in-depth explanations and recommendations around data insights, clearly understanding the “what” and the “why” of the data.

It enhances efficiency, decision-making, and collaboration between users and encourages data literacy and data democracy throughout an organization.

Augmented Analytics: What’s Next?

Augmented analytics is going to change the way people understand and examine data. It has become a necessity for businesses to survive. It will simplify and speed up the augmented data preparation, cleansing, and standardization of data, thus assist businesses to focus all their efforts on data analysis.

BI and analytics will become an immersive environment with integrations allowing users to interact with their data. New insights and data will be easier to access through various devices and interfaces like mobile phones, virtual assistants, or chatbots. In addition, it will help decision-making by notifying the users of alerts that need immediate attention. This will help businesses to stay updated about any changes happening in real-time.

Frequently Asked Questions

What are the benefits of augmented analytics?

Augmented analytics helps companies become more agile, gain access to analytics, helps users make better, faster, and data-driven decisions, and reduces costs.

How important is augmented analytics?

Augmented analytics build efficiency into the data analysis process, equips businesses and people with tools that can answer data-based questions within seconds, and assist companies in getting ahead of their competitors.

What are the examples of augmented analytics?

Augmented analytics can help retain existing customers, capitalize on customer needs, drive revenue through optimized pricing, and optimize operations in the healthcare sector for better patient outcomes. These are some of the examples of the use of augmented analytics.


Xentity Corporation

Xentity is the only IT consulting firm that puts the data program first. We do this by providing data programs strategy, design, and production services that put the desire of knowledge-driven decisions and management first. Xentity focuses our solutions and approaches on Open Data, Geospatial Data, Big Data, and IoT/Remote Sensing large data programs who want to either become more efficient, transform business, and integrate innovation in an era of large technology-first, cost center investment approaches. Since 2001, Xentity has provided over 100 engagements and over 45 data programs in the last 3 years alone across Federal, State, Local, Education, and Commercial Clients. Xentity has provided Data consulting and support services with major data program projects for USGS, USDA, DOI, BLM,, EPA, NARA, OPM, Navy, NOAA/NWS, NSF EarthCube, CDC, OGC, State of Colorado, Illinois, New York, and Cities such as New York City.


The Importance of Data Governance

Article | September 7, 2021

Data has settled into regular business practices. Executives in every industry are looking for ways to optimize processes through the implementation of data. Doing business without analytics is just shooting yourself in the foot. Yet, global business efforts to embrace data-transformation haven't had resounding success. There are many reasons for the challenging course, however, people and process management has been cited as the common thread. A combination of people touting data as the “new oil” and everyone scrambling to obtain business intelligence has led to information being considered an end in itself. While the idea of becoming a data-driven organization is extremely beneficial, the execution is often lacking. In some areas of business, action over strategy can bring tremendous results. However, in data governance such an approach often results in a hectic period of implementations, new processes, and uncoordinated decision-making. What I propose is to proceed with a good strategy and sound data governance principles in mind. Auditing data for quality Within a data governance framework, information turns into an asset. Proper data governance is essentially informational accounting. There are numerous rules, regulations, and guidelines to make governance ensure quality. While boiling down the process into one concept would be reductionist, by far the most important topic in all information management and governance is data quality. Data quality can be loosely defined as the degree to which data is accurate, complete, timely, consistent, adherent to rules and requirements, and relevant. Generally, knowledge workers (i.e. those who are heavily involved in data) have an intuitive grasp of when data quality is lacking. However, pinpointing the problem should be the goal. Only if the root cause, which is generally behavioral or process-based rather than technical, of the issue is discovered can the problem be resolved. Lack of consistent data quality assurance leads to the same result with varying degrees of terribleness - decision making based on inaccurate information. For example, mismanaging company inventory is most often due to lack of data quality. Absence of data governance is all cost and no benefit. In the coming years, the threat of a lack of quality assurance will only increase as more businesses try to take advantage of data of any kind. Luckily, data governance is becoming a more well-known phenomenon. According to a survey we conducted with Censuswide, nearly 50% of companies in the financial sector have put data quality assurement as part of their overall data strategy for the coming year. Data governance prerequisites Information management used to be thought of as an enterprise-level practice. While that still rings true in many cases today, overall data load within companies has significantly risen in the past few years. With the proliferation of data-as-a-service companies and overall improvement in information acquisition, medium-size enterprises can now derive beneficial results from implementing data governance if they are within a data-heavy field. However, data governance programs will differ according to several factors. Each of these will influence the complexity of the strategy: Business model - the type of organization, its hierarchy, industry, and daily activities. Content - the volume, type (e.g. internal and external data, general information, documents, etc.) and location of content being governed. Federation - the extent and intensity of governance. Smaller businesses will barely have to think about the business model as they will usually have only one. Multinational corporations, on other hand, might have several branches and arms of action, necessitating different data governance strategies for each. However, the hardest prerequisite for data governance is proving its efficacy beforehand. Since the process itself deals with abstract concepts (e.g. data as an asset, procedural efficiency), often only platitudes of “improved performance” and “reduced operating costs” will be available as arguments. Regardless of the distinct data governance strategy implemented, the effects become visible much later down the line. Even then, for people who have an aversion to data, the effects might be nearly invisible. Therefore, while improved business performance and efficiency is a direct result of proper data governance, making the case for implementing such a strategy is easiest through risk reduction. Proper management of data results in easier compliance with laws and regulations, reduced data breach risk, and better decision making due to more streamlined access to information. “Why even bother?” Data governance is difficult, messy, and, sometimes, brutal. After all, most bad data is created out of human behavior, not technical error. That means telling people they’re doing something wrong (through habit or semi-intentional action). Proving someone wrong, at times repeatedly, is bound to ruffle some feathers. Going to a social war for data might seem like overkill. However, proper data governance prevents numerous invisible costs and opens up avenues for growth. Without it, there’s an increased likelihood of: Costs associated with data. Lack of consistent quality control can lead to the derivation of unrealistic conclusions. Noticing these has costs as retracing steps and fixing the root cause takes a considerable amount of time. Not noticing these can cause invisible financial sinks. Costs associated with opportunity. All data can deliver insight. However, messy, inaccurate, or low-quality data has its potential significantly reduced. Some insights may simply be invisible if a business can’t keep up with quality. Conclusion As data governance is associated with an improvement in nearly all aspects of the organization, its importance cannot be overstated. However, getting everyone on board and keeping them there throughout the implementation will be painful. Delivering carefully crafted cost-benefit and risk analyses of such a project will be the initial step in nearly all cases. Luckily, an end goal to all data governance programs is to disappear. As long as the required practices and behaviors remain, data quality can be maintained. Eventually, no one will even notice they’re doing something they may have considered “out of the ordinary” previously.

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Big Data Could Undermine the Covid-19 Response

Article | September 7, 2021

THE CORONAVIRUS PANDEMIC has spurred interest in big data to track the spread of the fast-moving pathogen and to plan disease prevention efforts. But the urgent need to contain the outbreak shouldn’t cloud thinking about big data’s potential to do more harm than good.Companies and governments worldwide are tapping the location data of millions of internet and mobile phone users for clues about how the virus spreads and whether social distancing measures are working. Unlike surveillance measures that track the movements of particular individuals, these efforts analyze large data sets to uncover patterns in people’s movements and behavior over the course of the pandemic.

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Will We Be Able to Use AI to Prevent Further Pandemics?

Article | September 7, 2021

For many, 2021 has brought hope that they can cautiously start to prepare for a world after Covid. That includes living with the possibility of future pandemics, and starting to reflect on what has been learned from such a brutal shared experience. One of the areas that has come into its own during Covid has been artificial intelligence (AI), a technology that helped bring the pandemic under control, and allow life to continue through lockdowns and other disruptions. Plenty has been written about how AI has supported many aspects of life at work and home during Covid, from videoconferencing to online food ordering. But the role of AI in preventing Covid causing even more havoc is not necessarily as widely known. Perhaps even more importantly, little has been said about the role AI is likely to play in preparing for, responding to and even preventing future pandemics. From what we saw in 2020, AI will help prevent global outbreaks of new diseases in three ways: prediction, diagnosis and treatment. Prediction Predicting pandemics is all about tracking data that could be possible early signs that a new disease is spreading in a disturbing way. The kind of data we’re talking about includes public health information about symptoms presenting to hospitals and doctors around the world. There is already plenty of this captured in healthcare systems globally, and is consolidated into datasets such as the Johns Hopkins reports that many of us are familiar with from news briefings. Firms like Bluedot and Metabiota are part of a growing number of organisations which use AI to track both publicly available and private data and make relevant predictions about public health threats. Both of these received attention in 2020 by reporting the appearance of Covid before it had been officially acknowledged. Boston Children’s Hospital is an example of a healthcare institution doing something similar with their Healthmap resource. In addition to conventional healthcare data, AI is uniquely able to make use of informal data sources such as social media, news aggregators and discussion forums. This is because of AI techniques such as natural language processing and sentiment analysis. Firms such as Stratifyd use AI to do this in other business settings such as marketing, but also talk publicly about the use of their platform to predict and prevent pandemics. This is an example of so-called augmented intelligence, where AI is used to guide people to noteworthy data patterns, but stops short of deciding what it means, leaving that to human judgement. Another important part of preventing a pandemic is keeping track of the transmission of disease through populations and geographies. A significant issue in 2020 was difficulty tracing people who had come into contact with infection. There was some success using mobile phones for this, and AI was critical in generating useful knowledge from mobile phone data. The emphasis of Covid tracing apps in 2020 was keeping track of how the disease had already spread, but future developments are likely to be about predicting future spread patterns from such data. Prediction is a strength of AI, and the principles used to great effect in weather forecasting are similar to those used to model likely pandemic spread. Diagnosis To prevent future pandemics, it won’t be enough to predict when a disease is spreading rapidly. To make the most of this knowledge, it’s necessary to diagnose and treat cases. One of the greatest early challenges with Covid was the lack of speedy, reliable tests. For future pandemics, AI is likely to be used to create such tests more quickly than was the case in 2020. Creating a useful test involves modelling a disease’s response to different testing reagents, finding right balance between speed, convenience and accuracy. AI modelling simulates in a computer how individual cells respond to different stimuli, and could be used to perform virtual testing of many different types of test to accelerate how quickly the most promising ones reach laboratory and field trials. In 2020 there were also several novel uses of AI to diagnose Covid, but there were few national and global mechanisms to deploy these at scale. One example was the use of AI imaging, diagnosing Covid by analysing chest x-rays for features specific to Covid. This would have been especially valuable in places that didn’t have access to lab testing equipment. Another example was using AI to analyse the sound of coughs to identify unique characteristics of a Covid cough. AI research to systematically investigate innovative diagnosis techniques such as these should result in better planning for alternatives to laboratory testing. Faster and wider rollout of this kind of diagnosis would help control spread of a future disease during the critical period waiting for other tests to be developed or shared. This would be another contribution of AI to preventing a localised outbreak becoming a pandemic. Treatment Historically, vaccination has proven to be an effective tool for dealing with pandemics, and was the long term solution to Covid for most countries. AI was used to accelerate development of Covid vaccines, helping cut the development time from years or decades to months. In principle, the use of AI was similar to that described above for developing diagnostic tests. Different drug development teams used AI in different ways, but they all relied on mathematical modelling of how the Covid virus would respond to many forms of treatment at a microscopic level. Much of the vaccine research and modelling focused on the “spike” proteins that allow Covid to attack human cells and enter the body. These are also found in other viruses, and were already the subject of research before the 2020 pandemic. That research allowed scientists to quickly develop AI models to represent the spikes, and simulate the effects of different possible treatments. This was crucial in trialling thousands of possible treatments in computer models, pinpointing the most likely successes for further investigation. This kind of mathematical simulation using AI continued during drug development, and moved substantial amounts of work from the laboratory to the computer. This modelling also allowed the impact of Covid mutations on vaccines to be assessed quickly. It is why scientists were reasonably confident of developing variants of vaccines for new Covid mutations in days and weeks rather than months. As a result of the global effort to develop Covid vaccines, the body of data and knowledge about virus behaviour has grown substantially. This means it should be possible to understand new pathogens even more rapidly than Covid, potentially in hours or days rather than weeks. AI has also helped create new ways of approaching vaccine development, for example the use of pre-prepared generic vaccines designed to treat viruses from the same family as Covid. Modifying one of these to the specific features of a new virus is much faster than starting from scratch, and AI may even have already simulated exactly such a variation. AI has been involved in many parts of the fight against Covid, and we now have a much better idea than in 2020 of how to predict, diagnose and treat pandemics, especially similar viruses to Covid. So we can be cautiously optimistic that vaccine development for any future Covid-like viruses will be possible before it becomes a pandemic. Perhaps a trickier question is how well we will be able to respond if the next pandemic is from a virus that is nothing like Covid. Was Rahman is an expert in the ethics of artificial intelligence, the CEO of AI Prescience and the author of AI and Machine Learning. See more at

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Article | September 7, 2021

Quantum Mechanics created their chapter in the history of the early 20th Century. With its regular binary computing twin going out of style, quantum mechanics led quantum computing to be the new belle of the ball! While the memory used in a classical computer encodes binary ‘bits’ – one and zero, quantum computers use qubits (quantum bits). And Qubit is not confined to a two-state solution, but can also exist in superposition i.e., qubits can be employed at 0, 1 and both 1 and 0 at the same time.

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Xentity Corporation

Xentity is the only IT consulting firm that puts the data program first. We do this by providing data programs strategy, design, and production services that put the desire of knowledge-driven decisions and management first. Xentity focuses our solutions and approaches on Open Data, Geospatial Data, Big Data, and IoT/Remote Sensing large data programs who want to either become more efficient, transform business, and integrate innovation in an era of large technology-first, cost center investment approaches. Since 2001, Xentity has provided over 100 engagements and over 45 data programs in the last 3 years alone across Federal, State, Local, Education, and Commercial Clients. Xentity has provided Data consulting and support services with major data program projects for USGS, USDA, DOI, BLM,, EPA, NARA, OPM, Navy, NOAA/NWS, NSF EarthCube, CDC, OGC, State of Colorado, Illinois, New York, and Cities such as New York City.