The use of analytics has become ubiquitous in our day-to-day lives. Whether through monetization or measurement, it’s integral to creating and identifying value in our business.
Despite this, the complexity and volume of data that every business accumulates constitute a significant challenge for decision-makers. It can be particularly challenging for larger companies to manage data as it is a constant and always-growing concern.
In the modern world, the ability to track, identify, understand, and act on what is most important – and inherently know what to do – is increasingly difficult to accomplish manually, thus leading to decisions that are less data-driven and more intuitive.
Solutions such as augmented analytics can help us keep up with the fast-growing data we need for operational and long-term use.
Let’s explore augmented data discovery further in this article.
What is Augmented Analytics?
As defined by Gartner, augmented analytics accelerates and improves data analytics by using machine learning (ML), artificial intelligence (AI), and natural language processing (NLP) technologies (like natural language generation, natural language query, etc.).
This newest wave of BI technology enhances and builds on older methods of analysis to enable all types of users to gain access to insights, even those with little or no technical know-how.
A data analyst, or citizen data scientist, can extract more granular insights with augmented analytics in minutes than a professional data scientist would be able to with a traditional BI solution.
Important Components of Augmented Analytics
In the arena of analytics, augmented analytics includes several emerging, evolving, and established techniques, which include, but are not limited to:
Augmented data preparation:
This is usually accomplished through advances in data preparation or, more commonly, through ETL (extract, transfer, load). This can be achieved by using algorithms to detect schemas, joins, and profiling, as well as by automating data transformations.
Often referred to as automated business monitoring, this allows the continuous execution of analysis in the background. As a result, the manual data discovery process is automated, and the relevant changes in the data can be surfaced immediately (for example – trend change), accelerating the time to insight significantly. It is particularly beneficial to analyze large datasets with high dimensionality using augmented analytics.
Natural language generation (NLG) and natural language processing (NLP):
Natural language has become a prominent feature in modern BI platforms. Often, it makes complex information more accessible to understand and less intimidating by automatically creating rich descriptions based on insights found in your data. As well as reading and interpreting text and voice, these technologies provide the user with a more natural experience with data.
This can be achieved by using computer-generated visualizations, calculations, and variance analyses and is typically triggered by the user asking a question. Users could click on a spike on the chart to explain why the spike occurred or compare the spike to another period, for example. Analyses, calculations, and charts are generated automatically with machine-assisted insight.
A deeper analysis may have previously been completed through filters on a dashboard (if one was available) or by generating new queries to answer more profound questions.
Augmented Analytics VS Automation
Automation is a common element in augmented analytics solutions, but it is important to differentiate between automating tasks, as many technologies do, instead of automating the decisions that analytics inform. Automating data-driven decision-making eliminates human expertise, whereas augmentation provides a method for underlying technology to guide users to discover insights they might not have otherwise found.
Domain knowledge has always been important for analysis, but the augmentation of analytics powered by AI and machine learning makes this skill set even more valuable. Humans are often confronted with gaps in context, and it is up to them to fill them in and utilize the insight gained from analysis to develop the best solution.
How Does Augmented Analytics Work?
Augmented analytics utilizes machine learning, natural language processing, and artificial intelligence, just as other forms of BI do.
Preparing your data for analysis and querying is what you do to make it ready. Data preparation includes data collection, cleaning, connections, and validation. This usually requires the help of developers and data scientists.
Nonetheless, by adding augmented analytics tools to your data warehouses and cloud platforms, you can automate data preparation and simplify integrating with all of your data sources – including data warehouses like Amazon Redshift, web services such as Amazon S3, and analytics platforms such as Google Analytics.
Data cleaning and dataset unification are handled automatically when you add data (and metadata). As a result, your data scientists, data engineers, and developers can focus their efforts on developing new analyses that will deepen insights.
In the data analytics process, insight discovery is a process in which an algorithm uses a predefined model to analyze and evaluate data to find answers to questions like quarterly revenue or customer acquisition rates. However, because models traditionally are developed manually by data scientists, insights may not be as specific as they could be.
Using augmented analytics, insights can be discovered more easily and more thoroughly. You can ask questions with natural language and voice input rather than typing hyper-specific keywords, and machine learning algorithms can analyze your data (regardless of how many rows are in it) to find detailed, targeted insights about your question.
Providing insights to the end-user through reports or visualizations is called insights sharing. Data reports and charts must be manually created for business users to interpret and use. Typically, this requires IT teams to generate data reports and charts by hand.
Augmented analytics and human effort can significantly reduce the time to insights. A dashboard on an augmented analytics platform gives you instant insights using natural language generation.
As well as providing direct answers to natural language queries, these insights also provide a rationale for the answer. As a result, your decision-makers can carefully examine all relevant factors before making a final decision and can effectively share knowledge across your organization for improved results.
Top Benefits of Incorporating Augmented Data Discovery
Agility: Quicker access to insight
By reducing the search area, surfacing relevant data to the right person at the right time, and suggesting fruitful paths for analysis, AI-powered augmentation can accelerate the search for insights.
By tracking user behavior broadly, systems can provide smarter defaults and recommend actions that can be tuned and personalized over time. Answering data questions faster allows people to concentrate on more strategic tasks and spend less time searching for insights into data.
Accuracy: Provide a more detailed picture
Machines are incredibly efficient as they do repetitive work and calculations all the time. Augmented analytics can utilize artificial intelligence and machine learning to examine every nook and cranny of a situation, allowing users to make the most informed decisions. Users will be able to make decisions without confirmation bias.
Streamlined Results: Transform data swamps into data lakes
Several companies have used cloud storage and technologies to store data sets for future reference. A lack of a clearly defined goal, data management architecture or governance strategy can easily lead to these data sets growing out of control. The data pool turns into a swamp of data instead of a lake. You can make algorithmic sense of unorganized data swamps by applying AI and augmented data discovery.
Efficiency: Eliminate technical barriers
Enhanced analytics reduces the burdens associated with data profiling and data preparation for preparing reports. A number of business users are able to discover data and gain insights from the data with the help of augmented data discovery, even when they are not aware of the relationships between different data elements.
In adding augmented data discovery, multiple tables could automatically be joined together to produce a report. For instance, if the customer identification table in one system is named JOH ID, but in another system, it is named J ID, then augmented data discovery can help join these two tables.
What Lies Ahead
The volume and frequency of data creation pose significant challenges for data management, but they also provide opportunities for developing insightful analytics. Using smart data discovery methods, users can find highly contextualized results tailored to their needs.
With smart data discovery, Business Intelligence is on the verge of revolution. The current data discovery and visualization tools will get smarter, requiring minimal technical expertise to move users from questions to insights rapidly.
Providing appropriate guidance in this regard increases information gathering, improves decision making, simplifies analysis, and ultimately leads to proactive insight.
Commercial battlegrounds in the digital age are always in the hands of those who can extract valuable insight from the plethora of data now available at their fingertips, fostering growth and evolution.