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The advent of Augmented Analytics marked a significant milestone in the fast-evolving sphere of data analytics. It wasn't just about crunching numbers or creating visual data plots; it was about elevating the entire process to a plane where machines could aid humans in generating deeper insights with less effort. The transition from traditional to augmented analytics is akin to moving from manual calculations to leveraging calculators, saving time, resources, and expediting decision-making. This change has been nothing short of revolutionary, especially in a world where data is burgeoning at an exponential rate.
According to Gartner, augmented analytics is "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".
It helps to automate major analytics processes and streamline how you prepare data, find insights and share findings from those insights. It's a game changer for data-intensive businesses, as it also allows company-wide interaction with data with little expert analytics experience required. This article will explore the evolution of augmented analytics, the advantages over the traditional style, benefits, use cases and how to evaluate the tools.
Generating these insights from data is time-consuming, and with more data becoming unstructured, there had to be a faster way to get these insights and maintain business competitiveness. AI and machine learning are two emerging technologies that have shaped different industries, including the data analytics sector.
The use of AI in data analysis gave birth to augmented analytics, which was first mentioned in Gartner's research published in 2017. With the integration of AI and machine learning capabilities, the data analytics process moved from a manual to a self-service approach. In self-service, data management is still manual, but the human interactions are supported by natural language queries, one-click statistics and recommended visualization based on data type.
Recently, generative AI tools like ChatGPT have created a new opportunity, allowing businesses to democratize their analytics process. With this innovation, companies can augment analytics processes and extract insight from data using natural language.
They can also identify root causes of changes in business performance by asking straight questions, and augmented analytics tools like Kyligence explain deeper insights. Generally, augmented analytics looks for patterns in your data and extracts meaningful insights without the involvement of human scientists. This process enables businesses to automate repeat activities like data labeling, cleaning and more, save time, and deliver the products to market faster with the insight generated.
Traditional analytics leverages manual processes and human intervention at every data analysis stage. This method requires data experts to collect, prepare, build models, and analyze and interpret results from data.
In contrast, augmented analytics uses advanced technologies like machine learning and artificial intelligence to automate and enhance the analytic process. This method enables users with less expertise to access insights and recommendations from data.
Other difference between traditional methods and augmented data analytics methods includes:
Businesses can benefit from integrating augmented analytics in the data-driven process. Some benefits include:
With augmented analytics tools, businesses can go beyond the what and find out why. Since these tools have AI and ML capabilities, you can dive deeper into your business performance and identify the root causes of specific results. For example, if your sales for the first quarter were down by 11%, you can chat with an augmented data analysis tool to find out why. With this capability, you can get a better view, understand where the issue comes from, and make better data-driven decisions.
With augmented data analysis, you can create a function to automate the data preparation and other processes. This approach frees up time for data analysts to focus on other vital activities. Also, it eliminates the need to hire extra experts or pay overtime bonuses to team members when you have a data-intensive project. You save time, improve productivity, and support company-wide collaboration, ultimately improving product development processes. In the long run, it's more revenue due to delivering efficient services at record time.
Typically, analyzing data requires some training and experience as a data analyst. The requirements create a dependency on the data team and slow down company-wide access to insights. However, you can democratize data access with augmented analytics, allowing marketing, sales, development and other non-data analysis departments to extract insights easily. Users can write queries in plain English instead of SQL queries and get responses about different data performances in natural language. This means the results are easy to understand, and you can ask for a simplified explanation for better understanding.
With augmented analytics, development teams can quickly identify relationships between data from different sources. They can pull insights from these relationships, significantly accelerating the product development cycle. Compared to traditional modeling methods, the augmented analytics approach is faster and allows you to test multiple modeling techniques. Similarly, it equips you with the flexibility to ask more questions about development data than would have been impossible with traditional methods.
Augmented analytics tools have cases in many industries like retail, digital marketing, restaurant operations, etc.
In online retail businesses, augmented analytics tools like Kyligence help you understand customer wants and needs. Access to this information enables you to pivot quickly to address the users' pain points by optimizing marketing strategies. You can also use it to analyze customer behavior data and campaign performance and improve inventory management.
Augmented analytics also has implementations in the restaurant operations industry. It enables you to transform operations with intelligent, AI-driven sales and performance insights. Using a tool like Kyligence, you can spot new trends and untapped potential and translate those insights into actionable growth and marketing strategies. This information helps to ensure your stores reach their potential sales and maintain competitive advantage. You can also identify key performance metrics, such as restaurant capacity, break down goals and key metrics to identify the root causes of business change. These insights help you minimize waste, meet customer demands, and reinvest in the right goals.
Marketing data involves many touch points like campaigns, email marketing, and website analytics. With digital marketing augmented analytics tools, you can make informed, metrics-driven decisions without needing a data specialist. These tools allow you to delve deep into relevant insights without losing track of your marketing and sales data. With much data coming from different touchpoints, it's often impossible to track or identify specific trends' causes. But with AI analytics, you can place why behind the downtrend, which helps you optimize your marketing strategy for better results.
According to Veneta's research, here are some important aspects and factors to consider when evaluating augmented analytics tools
The need for analytics has gone beyond the surface and requires deeper insights quickly. Traditional methods fail at this task, but with Augmented analytics solutions, businesses can go beyond the what and understand the "why" behind data performance. Start to analyze data deeper and generate meaningful insights for growth with Kyligence. Sign up for free to experience personalized data analytics solutions powered by AI.
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