With technology being woven into the very fabric of life, we as a species are constantly generating data. And the rapid pace of the evolution of technology is ensuring that data sets are growing in size and becoming increasingly complex. It is impossible to manually sift through these mountains of data, looking for insights that can add value. This is where it becomes necessary to employ augmented tools for smart data discovery. AI-powered solutions that use machine learning (ML) and natural language generation (NLG) to make sense of unstructured data, and automate the route to insights.
Gartner coined the term and defines augmented analytics as
the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and BI platforms
Gartner also states that it is not just the analytics which is augmented, but the analysts themselves; adapting to a new solution where automated data analysis is key, and time to insights is drastically short.
It allows both qualified and citizen data scientists to harness AI and ML, enhance analytics capabilities, and connect with the data at a contextual level. It enables not just qualified personnel but team leads and stakeholders to manage their data, access actionable insights through an automated process, and make data-backed decisions that can boost the business as a whole. This has completely revolutionized the field of data analytics, making business intelligence (BI) more accessible at every level of an enterprise, and not just reserved for qualified analysts.
A core feature of most augmented solutions, ML is the process of understanding data through algorithms and learning models. It eliminates the repetitive and tedious manual task of sifting through data, automating the process, and reducing any chances of human error. The raw data is cleaned, structured, filtered, and examined, providing insights faster. Quick access to relevant insights helps make data-based decisions in real-time, optimizing operations and enhancing ROI.
Natural Language Generation helps to translate data analytics findings into words, for easier understanding. Rows and columns of data are hard to read and even more difficult to interpret. But NLG shares the results in a conversational voice, simplifying the consumption of BI by users. If the analysis shows that your company’s overall sale is down, instead of showing sales numbers across quarters, NLG helps the tool convey the finding: “Sales for last quarter has declined by 15%.”
A common feature of augmented tools, automation is often misunderstood as a concept. Technology can automate tasks and reduce human effort, but automating the decision-making process eliminates the necessity for human capability. But augmentation on the other hand offers a framework for the technology to enable users to find insights that they would otherwise have not discovered.
Even with these advances in technology, domain knowledge still remains a crucial element for successful analytics. AI and ML can analyze all the data that is being provided, but there are gaps in BI that humans need to fill in contextually, and leverage the insights gained to solve problems, optimize processes, and effect change.
Executives and business users receive tremendous value from augmented solutions because it allows them to access insights in real-time, without having to be proficient in data analytics and having the requisite skillset. They can easily access data that is relevant, make specific queries, and gather insights that directly impact the business. Just as it helps the uninitiated to harness data, these BI tools are also of great value to professional analysts who want to examine a data-set thoroughly or conduct data-prep tasks at a faster pace and free of errors.
Analysts employing augmented tools can work with greater efficiency and deliver more accurate results. Domain expertise is also amplified by machine learning and natural language technologies, by removing the requirements for technical proficiency and bringing analysts into direct contact with their data. Advanced technologies become available to personnel without the requisite skill-set and experience, helping them access insights previously reserved for senior analysts, and promoting data democratization.
Augmented solutions powered by AI accelerate the speed to insights by shortening the search space, providing relevant data to the right analyst at the right time, and prescribing profitable routes for analysis. Simultaneously tracking user behavior enables the system to personalize customer journeys even further, offering better default settings and smarter recommended actions. Faster answers to data questions let employees focus on developing and improving strategic tasks rather than spending all their time sifting through data. Agility is the order of the day, and the solutions absolutely deliver on that front.
Machines are the answer to conducting repetitive tasks based on calculations, without the risk of human error. ML and AI-based technologies can work around the clock looking efficiently analyzing all the data and offering accurate insights every time, and in near real-time, helping to make informed decisions. The insights gathered through these technologies also help analysts avoid confirmation bias when coming to conclusions and enhance both accuracy and decision-making.
Tasks related to data discovery, data mapping, statistical analysis, and more, are extremely time-consuming and energy-intensive. Augmented tools automate the entire process from end-to-end, freeing up personnel to focus on tasks that require human ingenuity and creativity, and ways to elevate other processes within the enterprise. The drastic evolution of both ML and AI technologies has paved the way to automate operational tasks through highly specialized solutions and applications.
With augmented solutions, analytics are no longer reserved for analysts. The solutions are easy to use, the data is more approachable through interactive dashboards, and insights can be easily gathered through contextual analysis and querying. The solutions can be customized to analyze and surface contextual insights, enabling stakeholders to confidently confirm their instincts and make data-backed decisions. Some solutions are integrated into workflows, tools, and related software, allowing business users to clarify specific queries without carrying out additional data preparation tasks and without disrupting other ongoing analyses.
Augmented technologies not only offer greater access to data but as mentioned before, enable citizen data scientists to view, understand and communicate data as information. Even without the expertise, SMEs with enough industry experience can easily interpret the data from interactive dashboards and leverage valuable insights. Augmented analytics offers greater access to easily-consumable data, and promotes data literacy, paving the way for greater innovation and optimization when enhancing enterprise from end to end.
Business intelligence platforms are customized to automate the detection of data attributes, to help with segregation and creating data sets for targeted analysis. Parameters such as postal codes can be used to analyze customer behavior from specific regions and optimize marketing strategies accordingly. Personal information such as email IDs or phone numbers can be automatically isolated by the tool to analyze a specific customer’s buying patterns, and marketers can offer a more personalized experience based on insights gathered. Moreover, augmented analysis tools can effectively carry out analysis from tables of data, whether they are in PDF or text format, eliminating any need for converting or formatting.
As mentioned above, there is a steady influx of various types of data from across channels and touchpoints. Augmented Analytics solutions are geared toward unstructured data analytics where the system acts on verbatim information from natural language and texts, and the software analyzes the different data sets for relevant insights, without any need for further configuration. The business intelligence platforms are also equipped with anomaly detection capabilities. The software identifies any data that deviates from the selected data set, or from historical data. There can be Point Anomalies, where there has been a single deviation from the norm; or Pattern Anomalies, where there are similar data points following the same trend.
Automated selection of data from clustering, forecasting, and related statistical algorithms, helps in prescribing the most fruitful routes that can be taken to maximize ROI. AI technology helps to surface insights that even experienced personnel may not be able to identify. These capabilities can look beyond the surface and identify the reasons behind certain data points, explaining the reason behind why an analyst is encountering an outlier in a data set. End users can quickly access these insights with augmented technologies, and not depend on the coding or calculations.
Allowing ML algorithms to carry out time and resource-intensive data prep tasks eliminates manual cleanup and chances of human error affecting the outcome. Augmented technologies can locate, group, and index data based on common characteristics, pronunciation of words, and common features, removing the need for manual searches and the tedious task of updating every individual data point.
One of the prime features of augmented tools is the capability to share AI-backed recommendations with end-users. The recommendations can range from data discovery and preparation to analysis and insight sharing. The system may recommend that data sources be merged during the preparation stage, or suggest ways to break down the data into rows and columns, making it more easily consumable and making analysis more efficient.
This capability of these analytics solutions enables users to type in any query to gather insights from data, and not have to write a single line of code. Using intelligent search, the system converts text into queries and looks into the context and intent behind the user’s search. This removes the necessity to understand the data and enhances the efficiency of intelligence gathering. NLG delivers insights from data visualizations in plain language, helping users understand the story being told through the data, without needing the expertise to interpret the data.
As a department within Natural Language Processing, sentiment analysis is aimed at identifying opinions expressed by customers. The data to be analyzed can be in the form of text, voice recordings, images, and other forms of customer feedback and comments. Automation and advanced analytics capabilities help in identifying the sentiment expressed by the customer, but the real challenge lies in emotion detection. Even as humans we sometimes miss out on the true emotion behind a comment, due to nuances of language. AI has taken up the challenge of identifying the sentiment, and the respective customer’s emotions; for a stronger connection with the customer and improved CX.
Tribal knowledge gets passed between personnel within an organization, helping with carrying out analytics. But if analysts have to keep reaching out to other personnel who have bits of information they are looking for, the knowledge divided slows down the process and increases the chances of error and bias. Augmented technologies streamline the entire process by leveraging the knowledge base that it continually develops over time. The evolving technology can tap into previous insights and address current data based on the knowledge gained. ML and NLG tech are crucial elements to sifting through unstructured data, finding insights, and learning from the experience to better perform the next analysis. Moreover, analysts can target specific data and find exact insights, through efficient natural language querying capabilities of the augmented tools. Decision-makers can simply search for the insights they need, and also answer the “why” behind the intelligence gathered. But bridging the gap between man and machine doesn’t come without its challenges.
When it comes to AI and ML technology, the focus still remains on the core technology and does not consider how end users can benefit from using the tech. When regular users don’t understand the technology and are not even guided through the process, they end up not trusting the tools. The value the tools can bring are not realized and people holding onto the age-old misconception that machines are going to replace human in their workplace, leading to augmented technologies not being adopted.
End users can have unrealistic expectations when they invest in augmented technology and expect results. This can easily be avoided by getting a better understanding of the technology before the investment is made so that there can be a strategy in place for optimal usage, that can benefit the enterprise.
When it concerns search intent, the solutions are not yet capable of completely understanding the end user’s intent with limited context. Analytics still requires the human element and domain expertise, to gain true value from the insights.
Technology can definitely aid with analyses and insight gathering. But if the users handling the business intelligence are not proficient at understanding data and making critical judgments based on the same, the wrong recommendations and routes can waste precious resources of the enterprise. Data literacy is important, even if the end-user is not an experienced analyst.
This also implies that the enterprise needs to prioritize efficient data curating and management because the augmented solution will only be as effective as the quality of data it has received. Data governance practices need to be of a high standard, of high-quality business intelligence is to be expected from the solution.
The technology being deployed for analytics needs to be transparent and explainable. “Black Box” AI tools do not offer visibility, and the BI is often unreliable because nobody can verify its legitimacy. The tools being used needs to offer a clear view into how and why it has reached a certain conclusion from a particular data set so that a logical process can be followed throughout the organization. Without transparency, there can be no trust. And an enterprise needs to be able to trust the source of its BI.
Augmented tools are the next step when it concerns an organization’s digital transformation. Adoption of these BI tools and new processes paves the way for a self-service environment where every end-user can query their data, gather insights in real-time, and make effective improvements to their business function.
Don’t bite off more than you can chew and end up spending more than your ROI. Test out your AI analytics investments, get the process rolling in one department, and once you are successfully enhancing your operations based on insights; consider scaling up your analytics investment across the enterprise.
Nothing beats a good education, and an educated end user will deliver the most value from the data being analyzed. Data literacy builds creativity as well as practical skills, and analytical proficiency aids in making the right judgment calls and taking the right decisions. A regular user won’t have the expertise to maximize the impact of the insights gained, but educating them enables you to grow your enterprise from within and secure the future.
The augmented solutions are integrated into workflows and end-users have a lot of access to specific insights they are searching for. But you need to promote communication and collaboration between regular end users and experienced analysts and domain experts so that the regular users can better understand the data they are working on, succeed, and better adapt the technology to their operations.
The increased adoption of augmented technologies is the key to transforming analytics efforts within organizations and embracing the future of business intelligence. The tools completely revolutionize every element from data discovery and preparation to interpretation and visualization. It opens up new prospects and avenues to expand the business, make improvements and enhance operations. Moreover, it promotes self-service analytics, enabling decision-makers throughout the organization to make data-based decisions that elevate the organization as a whole.
Course5 Discovery is an AI-Powered Augmented Analytics solution that enables the CIO organization to monetize the cross-functional data in the enterprise data lake on the cloud. It delivers speed-to-insights and faster decision-making by facilitating curated and actionable insights via multiple consumption mediums such as chat, voice, enterprise BI platforms, Excel, PPT, WhatsApp, Email, and other collaborative platforms.
Enabling your organization with the benefits of automated insights, utilizing machine learning and natural language for querying and exploration also doesn’t just make it easier for users or customers to consume, analyze and act on data – it gives you a competitive advantage and eliminates the risk of being left behind.
Many businesses are only now starting on their augmented analytics journey, even though this technology has been available and accessible on a number of modern BI platforms. Those who have invested in augmented have achieved significant benefits by embracing automation into their end-to-end analytics workflows. Not only have these organizations established themselves as trailblazers (when implemented successfully), but they’ve also secured an early advantage in future-proofing their analytics strategy.
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