(By Maneesha Nanda)
Over the years, the availability of a variety of data points from different sources has revolutionized the way organizations work and solve problems. Big data analytics has become a buzzword, and most organizations are rolling out initiatives to capture, store, and analyze data to turn them into insights. However, turning this unique idea into reality has not been an entirely easy task for organizations.
We are in the midst of the fourth industrial revolution; wherein data is playing a significant role in driving efficiency, productivity, cost benefits, product differentiation, and market access to various industries across segments. Data can become a critical business asset only if there is a well thought out ecosystem established to generate, assimilate, and analyze the data for drawing key insights that can help in taking the right decisions at the right time. Every organization needs to uncover insights hiding in the data if it is going to thrive. While digging into this data is tough, it is doable using the right tools.
While creating a data-driven culture across all aspects of the business has been a popular sentiment, earlier the lack of data has been pushing companies to make decisions based on intuition. This, by itself, has not helped to analyze the efficacy of the strategy implemented. On the other hand, while we have data from various sources at our disposal now, the next challenge is to structure this data and provide data access to the employees. This is where augmented analytics comes into play.
Augmented Analytics
Gartner defines augmented analytics as to the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation, and explanation to augment how people explore and analyze data in analytics and BI platforms. Augmented analytics is more about the process where data is automatically collected from data sources, mined, and analyzed in an unbiased manner. It is then communicated in a way that humans can comprehend easily and use the insights as a feedback loop for the growth of the business. Augmented data catalogs, AI-based data management, auto ML, automated data engineering, automated data governance, and a service-based approach that provides data/insight/KPI/AI as a service bring the scale and speed to the business.
Worldwide, we have noticed that data-driven companies have been more successful and profitable than their counterparts who do not center their decision-making on data. Thus, to empower existing talent, companies are creating a culture of data-driven processes and decision-making. This culture, also termed as DataOps, is a new way of managing data that promotes communication between, and integration of, formerly siloed data, teams, and systems.
Challenges addressed through Augmented Analytics
As more and more businesses become data-driven by enabling people in the organization with access to analytics and insights from data, it brings the culture of data democratization into the organization.
With the adoption of augmented analytics, organizations can have an edge on:
Renewed focus
Through augmented analytics, data is automatically collected, mined, and analyzed – a task that was manually done by IT and data professionals earlier. Earlier, it used to take more time to get insights as the whole process used to involve IT teams creating predefined data models and business intelligence reports, which was a massive challenge for organizations. With the implementation of augmented analytics, these professionals are freed from mundane tasks. They can rely more on AI tools than pre-generated reports and hence focus on strategic matters, special projects, and spend time on deriving insights from data, rather than just managing them.
Removing human biases
Since machines can efficiently analyze myriad data sources and combinations, augmented analytics allows for more in-depth data analysis. With augmented analytics, organizations can remove human bias from the analytics process and provide many accurate, actionable insights. Additionally, a streamlined and automated process of collecting, correlating, cataloging, and finding patterns in data helps in deriving insights faster and a much more efficient manner, compared to the earlier practice of manually doing this.
Democratizing data
In this quest for creating a data-driven company, access to data is of prime importance. Augmented analytics simplifies data analysis, making it easier to get actionable insights. Traditionally, the IT team acts as a gatekeeper, deciding who can have access to which data. In this model, gaining access to data could take weeks or months, and quick decision making based on data took the back seat. This also resulted in the organization losing many opportunities where data could have been used for deriving insights.
Through augmented analytics, people across all departments have access to data, and they can use data-driven insights to take quick decisions for the business. More users can use the power of data science to solve business problems. When such a culture is established, organizations will notice data-driven initiatives being undertaken from the bottom of the company rather than the top.
What lies ahead?
Augmented analytics is the future of data and analytics that enables a platform to automate, analyze, and interpret big data sets while offering suggestions to business leaders. Data scientists are already focusing on automation of data preparation, finding the data pattern, auto-selecting the models, and augmented model management. It is nothing less than a magic wand that puts the power right in the hands of businesses and helps them shift the gears and take the leap. As industries turn to technology and a renewed approach to data, organizations can now get better in handling data with fewer prominent data professionals and empower existing talent with access to data at the same time.
(The author is the Practice Leader – Data Engineering & Analytics at QuEST Global)
