A guide to augmented analytics: use cases, platforms, and guidelines

A guide to augmented analytics: use cases, platforms, and guidelines

May 16, 2022

Andrea Di Stefano

Technology Research Analyst

One of the greatest, albeit fictional, data analysts of all time Sherlock Holmes used to say that "It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts."

Indeed, a major achievement of humanity throughout its history has been the gradual shift from interpreting reality based on beliefs and conjectures to analyzing events or scenarios according to actual data. Introducing increasingly advanced methods and technologies has certainly fostered this approach. On the other hand, our world has also become way more complicated over the centuries, and peering into the full spectrum of underlying trends and phenomena driving its dynamics has proved a challenging task, even for the most insightful and well-equipped researchers.

To address this and many other issues related to the never-ending pursuit of truth, the data analytics discipline has undergone an enhancement process whose result is now known as "augmented analytics".

What is augmented analytics

According to Gartner, which coined this term in 2017, augmented analytics involves the implementation of artificial intelligence and related technologies into the typical data analytics workflow to facilitate data collection and preparation, insight generation, and data query. This can be achieved by automating several phases of the data science and data analytics life cycle (including data management, data model development, and data visualization) and improving data analytics and business intelligence software with more advanced features to unlock the aforementioned upgrades.

Gartner has also contributed significantly to better defining the concepts of augmented analytics and augmented business intelligence in recent years, while keeping an eye on the core trends of these disciplines and their growing importance from a business perspective.

Gartner’s impact radar for emerging AI-related technologies, 2020

Tapping into Gartner's research and other relevant sources, let's explore the reasons behind the success of augmented analytics and augmented BI, their most important adoption areas, and the major platforms available on the market, along with a few tips for implementing them properly into your business.

From traditional to augmented analytics

Today, data is fuel for companies and any tool to capitalize on it represents an essential component in the arsenal of every business decision-maker. Unfortunately, traditional approaches to data analysis have failed to keep up with the times, as new potential threats are looming  on the horizon:

  • Complex datasets: Data can be an invaluable asset if you know how to manage it. But given the growing volume, complexity, and multifaceted nature of modern datasets (which includes both structured data, such as simple figures, and unstructured data, extracted from videos, images, and other multimedia content), this task has become more and more demanding in terms of time, computing power, and expertise.
  • Demanding analytical cycle: the process required to turn raw data into business insights is even more complex than data itself, as it consists of several steps that need to be performed by qualified professionals, such as data scientists and analysts. This includes selecting suitable data sources, setting up an ETL pipeline to extract, transform, and load data into proper data storages, processing data with algorithms to identify meaningful patterns, trends, or anomalies, and visualize such results.
  • Shortage of data expertise: We talked about professionals, but what about their actual availability? Well, according to Verified Market Research's 2021 Augmented Analytics Market study, the United States alone may be short of 25,000 data scientists by 2024.
  • Interpretation of results: The last challenge is to translate abstract insights into the real world, namely understanding the results of data analysis and using them as actual recommendations to drive decision-making and take action in the specific business scenario where we operate. This represents one of the biggest obstacles to adopting a data analytics strategy for informed decisions, as pointed out by Gartner in its Marketing Data and Analytics Survey 2020.
Data analytics adoption barriers in marketing decision-making

As mentioned above, a suitable solution to address such problems was found in the growing integration of data analysis techniques and tools with artificial intelligence, machine learning, natural language processing (NLP), and other cognitive technologies. These advanced tech enablers allowed for a greater focus on automation, data democratization, and business-centered insight generation, driving the transformation process that turned standard analytical functions into so-called augmented analytics and augmented BI.

Evolution of augmented analytics according to Gartner

Clear signs of this transition can be spotted in the most recent augmented analytics market dynamics. According to Verified Market Research’s aforementioned study, for example, the global augmented data analytics and augmented BI market was worth $7.7 billion in 2020 and is set to reach $62.5 billion by 2028 with a CAGR of 29.8%.

This positive trend can be explained by the growing share of corporations relying on advanced analytical software solutions and business intelligence services to fully leverage their data assets, along with the need to democratize access to business insights and further improve productivity based on such information.

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Augmented analytics adoption areas and opportunities

We have already detailed some of the key challenges that data scientists, data analysts, and corporate decision-makers face every day when it comes to turning data into business recommendations. We have also stated that augmented analytics, powered by artificial intelligence and other related technologies, can solve or at least significantly mitigate such issues. Now, we just need to explain how and what benefits it brings.

Here are three use cases representing the mainstays of augmented analytics and augmented business intelligence in a typical business scenario.

1. Automated data management

We've already pointed out that well before we analyze data, we need to find and collect it. This process, given the growing size and complexity of datasets in the age of digitization, requires the precision and patience of an amanuensis monk. Data scientists do their best, but unlike monks, they would rather not stick to the ora et labora (pray and work) rule by  spending their entire lives performing mechanical tasks such as cleaning and labeling data.

Fortunately, much of these data preparation procedures can be automated through augmented analytics. Specifically, algorithm-based systems will take care of integrating multiple data sources (such as corporate databases or online advertising platforms) into a coherent data stream, transforming it into a unified storage format, loading it into data warehouses, and re-engineering it for future processing. This ensures way higher data management speed and accuracy compared to manual methods.

2. Insight generation

As the motion of sea currents reflects the action of multiple forces, such as the winds and the thermohaline circulation, the vast ocean of data is crossed by countless and rather mysterious dynamics that even the most attentive observers may struggle to grasp and decipher. In less poetic and more business-related terms, how can we identify key parameters among huge datasets, relate them to specific market trends, customer behavioral patterns, or other events, and therefore get practical suggestions to guide our decisions?

Considering thousands of metrics and identifying any relationships among them sounds extremely challenging and time-consuming for humans. Machine learning algorithms, on the other hand, can easily sift through data, spot patterns, anomalies, and correlations among variables, and build data models representing them to help us get a full picture of the phenomena we need to analyze. Furthermore, such algorithms fine-tune their models as they process new data, learning new relationships between data points through experience (as the term "machine learning" suggests).

As for actual deployment on the ground, for example, this means that an augmented analytics platform can monitor data related to your sales performance, ad reach on social media, and market stats. Based on such information, it may find out that a recent growth in sales didn't depend that much on your marketing initiatives but resulted from a favorable trend in your target market, therefore suggesting that you should rethink your advertising campaign despite your enthusiasm.

Standard vs augmented analytics workflow, source: Gartner

3. Conversational and visual analytics

When you ask analysts for business suggestions, they will answer you in perfectly understandable human language. But the machines we rely on to automate the aforementioned insight generation processes can't do the same because, well, computers don't talk. Or maybe yes? Actually, this is a rhetorical question, as we all know that recent developments in artificial intelligence and NLP have enabled virtually any software or device surrounding us to understand and mimic human communication.

Augmented analytics platforms are no exception, as they provide users with a variety of functionalities to query data and receive clear business insights and recommendations in natural language (be it written or spoken) rather than code. NLP-based conversational interactions are typically complemented with data visualization features (such as dashboards, reports, and graphs) to highlight relevant metrics or trends and further simplify the interpretation of the platform's findings.

The most remarkable effect of this combination is the possibility of democratizing the way we approach and leverage data, ensuring access to valuable information for non-technical users.

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Augmented analytics and augmented BI platforms

Augmented analytics and augmented business intelligence represent the hottest trends in the analytics platform market, and all major players have implemented a consistent range of features in their products to enhance or automate the analytical workflow. This means that any organization willing to rely on such capabilities and therefore step up data-driven decision-making can count on the full gamut of software solutions to choose from.

Despite a rather competitive landscape that seems destined for further fragmentation in the coming years, we may identify 5 key players in terms of market share, as pointed out by Mordor Intelligence in its 2021 Augmented Analytics Market report.

Major players in the global augmented analytics market

Selecting a platform that fits your business needs is not a simple task. To help you choose the right one, it's worth defining the core functionalities that you should look for in an augmented analytics and augmented BI solution and examining the strengths and weaknesses of the major tools on the market.

As for the first point, Gartner's analysts compiled a list of recommended augmented analytics features, which include:

  • Possibility of cloud-based and on-premises deployment
  • Solid cyber security features, such as identity and access management
  • Connectivity with storage platforms and other data sources
  • Assisted data combination from various sources and preparation
  • Automated insights to provide end-users with business recommendations
  • Data visualization and storytelling to easily interpret and share key findings
  • NLP-based data query and reporting to get information fast and smoothly

Regarding the second aspect, namely the pros and cons of the five major platforms for augmented analytics and augmented business intelligence, here's a brief roundup.

Microsoft Power BI

Microsoft Power BI

At present, Microsoft's flagship platform can easily be recognized as the market leader in augmented analytics. Power BI is fairly priced, offers solid automated ML functionalities, and can work in synergy with other Microsoft products, such as Office 365 and Teams.

However, it's still far from being the "One tool to rule them all" due to a general lack of flexibility in terms of deployment options. Power BI runs only in Azure and its on-premises functional capabilities are pretty limited.

Microsoft Power BI’s interface
Qlik Sense

Qlik Sense

Organizations deploying Qlik's lead product for augmented analytics can count on its powerful Associative and Cognitive Engines to get context-aware insights in self-service mode. Furthermore, Qlik Sense offers both cloud and on-premises deployment, flexible data visualization and storytelling capabilities, and a constantly expanded range of features thanks to new acquisitions.

On the other hand, its product licensing model is rather complex and the aforementioned acquisitions have resulted in overlapping or poorly integrated functionalities.

Salesforce Tableau

Salesforce Tableau

Previously known as Einstein Analytics, Tableau is a business-centered augmented analytics platform providing decision-makers with advanced visualization functionalities to help them explore data and share valuable insights. Another core strength of Tableau for data science is its vibrant user community of customers and partners sharing their expertise.

Anyway, all this comes at a (relatively high) cost, which is Tableau's premium pricing. Furthermore, based on Gartner Peer Insights' feedback, its overall service and support quality is slightly lower than the competition, especially in terms of response speed.

IBM Cognos Analytics with Watson

IBM Cognos Analytics with Watson

IBM's watchword is "flexibility", since its augmented analytics solution offers a variety of on-premises, cloud and hybrid deployment models, supports several IaaS platforms (such as Azure, GCP, AWS, and IBM Cloud), and covers a comprehensive range of use cases. These include automated insight generation, NLP-based data storytelling, visual data exploration, and other augmented analytics features.

The flip side of the coin is that Cognos' pricing per user is higher than other cloud alternatives and that IBM lacks key enterprise-focused applications in its product offering, including digital workplace software solutions.

SAP Analytics Cloud

SAP Analytics Cloud

SAP's answer to the growing demand for augmented analytics tools from business decision-makers is a cloud-based platform that boasts excellent usability and an intuitive user interface. It also combines advanced NLP-powered data query and automated insight generation features with a one-of-a-kind planning functionality based on predictive analytics.

Unfortunately, SAP's augmented analytics solution is essentially a cloud-native platform that requires additional tools from the same ecosystem (but with completely different support and maintenance schedules) in order to be deployed on-premises.

Other potential challengers

Let's close our overview with another bunch of noteworthy contenders that might carve out a prominent place in the augmented analytics and augmented business intelligence market in the future.

Platform Pros Cons
Google Loocker Direct data query of cloud databases Requires coding and more advanced expertise
Domo Great connectivity with corporate applications and rapid deployment Lack of a comprehensive app ecosystem
ThoughtSpot Everywhere Strong focus on NLP and automated insights Limited global reach and broader ecosystem
Sisense Extensible architecture and NLP-based interactivity Lack of third-party resources and support
Oracle Analytics Cloud End-to-end solution for data management and analytics Embedded analytics features work only with Oracle apps

Four tips before starting augmented analytics

Augmented analytics is revolutionizing the way we explore data and use it as a potential asset. However, it's not a panacea, and the general recommendations for facilitating the implementation of data analytics into your corporate workflow still hold true for its more advanced counterpart:

  • If you need a solution with lower upfront costs and a shorter implementation timeframe, opt for an augmented analytics tool built on top of the platforms described above. If, on the other end, you prioritize full personalization and a tailored set of features, a custom analytical system may be the best choice.
  • Data protection is not just a matter of cyber security measures embedded in the platform you create or select. Take care of your corporate datasets by implementing solid data governance policies and tools (such as data quality notifications, enterprise data catalogs, and mapping features) to oversee data sharing and utilization across your company.
  • Make sure that the augmented analytics solution you implement complies with your industry's data management quality regulations and data protection international legislation, including GDPR, HIPAA, FISMA, and more.
  • While augmented analytics considerably simplifies the most challenging steps of the analytical cycle, it's still a tool that should be adopted in full knowledge of the facts. Invest in targeted training initiatives, promote a corporate culture embracing data literacy and analytics proficiency to demystify this discipline, and establish centers of excellence to supervise its implementation.

Such guidelines should help you unleash augmented analytics’ full potential, streamline the workflow of your analysts and managers, and make the most out of your data assets. In this regard, let me conclude our overview with another famous quote from good old Sherlock.

“Data! Data! Data! I can’t make bricks without clay.”

Well, he was certainly right. But with better instruments, shaping bricks will be significantly easier. And nowadays, the most powerful tool at our disposal is augmented analytics.