The Benefits and Challenges of Augmented Analytics
Augmented analytics uses AI/ML techniques to automate the tedious manual data preparation processes, insight data discovery, and sharing. It also automates data science tasks such as ML model development, management, and deployment. Traditional BI tools have supported basic capabilities for joining, manipulating, and transforming the structured data. While augmented data preparation streamlines processes for data profiling, manage quality, cleaning data, modeling, and labeling metadata in a manner that supports reuse and governance.
Augmented Analytics includes Natural Language Processing (NLP) and conversational analytics, which enables less technical experts such as citizen data scientists to interact with the data and insights to give recommendations for the business. By 2020, 50% of analytical queries will automatically be generated via NLP or voice. This ease of access promises many benefits, but the organization faces several challenges in making it work well in practice.
Benefits of Augmented Analytics
1. Faster Data Preparation
Data Scientists and data engineers spent around 80% of their time on manual data preparation. Augmented Data Preparation uses AI/ML automation to bring data together from disparate sources much faster. Algorithms replace manual processes and automate the data cleaning process in a fraction of time. It is also used to detect schemas, joins, repetitive transformations, and automatically recommend associations between disparate data sources, enhancing productivity and efficiency.
2. Improved Data Literacy
As organizations continue to collect a massive amount of data, it is essential that everyone, regardless of expert analytical skills, can gain value from the data. Democratizing AI across the data value chain promotes data literacy by automatically surfacing and explaining insights using natural language, making a recommendation, and empowering all users to confidently take action. This assists in fostering a data-led culture that benefits the organization for the long term.
3. Reduced Analytical Bias
Analysts always make assumptions for finding answers when they do not know where to start. Often those assumptions could lead to the use of specific data to support it. Augmented analytics can help minimize potential bias by performing automated analysis across a large dataset of statistical importance. It reduces the risk of missing important insights.
4. Reduced Time to Insights
With augmented analytics, instead of an analyst manually testing all the combinations of variables in the data, ML algorithms for detecting correlations, segments, clusters, outliers, and relationships are automatically applied to the data. Only the most statistically relevant results are presented quickly via smart visualizations, which are optimized for the user’s interpretation and action.
5. Democratization of Analytics
Augmented Analytics will democratize insights from analytics, including AI to all business roles. It will make data science and AI/ML model building accessible to new citizen data science roles such as business analysts, developers, and others, etc., while making expert data scientists more productive and collaborative and freeing them for high-value tasks.
Companies that have implemented augmented analytics has seen positive business outcomes such as an increase in decision making by 51%, an increase in efficiency by 50%, and an increase in effectiveness by 48%. Although not all companies have realized these benefits yet, over 90% of decision-makers expect to realize these benefits as analytics capabilities continue to improve.
Challenges of Augmented Analytics
Augmented Analytics is the next wave of disruption in the data and analytics market and must be adopted to build and sustain a competitive advantage. But it requires the right balance and alignment of strategy, people, process, data, and technology components. However, efforts to incorporate it will likely encounter resistance as follows:
One of the adoption challenges faced by the organization is the outcome associated with it. The organization expects favorable and quick results. But augmented analytics has a lifecycle of its own. It uses AI techniques to do any form of computation, analytics, and automation. Once implemented, it will take time to mature, improve its analysis and outcomes over a period. The other potentially damaging challenges are as follows:
- The prevalent “black box” image of augmented analytics is not transparent in terms of decision making.
- There is a lack of trust in the displayed recommendation and insights unless a specific explanation is provided.
- There is a dependency on legacy analytics platforms that act as a barrier to adopt Augmented Analytics.
- There is a threat to job security because of automation in every aspect of analytical operations and the revamping of workloads and work processes.
- There is a resistance from the business leaders to adapt to changing scenarios and rely more on intuition and traditional decision-making practices.
- There is a misconception that augmented analytics maturity is linearly progressive and implemented once a robust foundation is established.
Another important challenge is the need to emphasize AI and analytics governance and collaboration between analysts and data scientists. The adoption of Augmented Analytics will require existing organizational models to evolve and support a growing footprint of citizen data scientists embedded within the business units. It will not only yield sizable returns but, if left unchecked, will also have adverse effects. Therefore, data and analytics leaders must outline the rules to govern the use of analytic content and insights created as outputs of augmented analytics as well as data science tasks to ensure the accuracy, validity, and bias levels of findings and recommendations.
The innovative functionalities of augmented analytics demonstrate a variety of applications across all sectors of the economy and promise to transform the digital landscape by streamlining business operations and increasing access to useful data.
Is your organization planning to adopt Augmented Analytics? If yes, then what are the challenges your organization is facing? Feel free to share your thoughts in the comments section.
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Payal is a Product Marketing Specialist at Subex, who covers Artificial Intelligence and its application around Generative AI. In her current role, she focuses on Telecom challenges with AI and its potential solutions to these challenges. She is a postgraduate in management from Symbiosis Institute of Digital and Telecom Management, with analytics as her majors, and has prior engineering experience in the Telecom industry. She enjoys reading and authoring content at the intersection of analytics and technology.
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