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Data and analytics have become critical for navigating an organization’s strategic decisions. We have been talking about data being the new oil and the propeller for digital transformation for quite some time now. But the data alone is more or less useless – it is just numbers, figures, and some charts, which do not tell anything.
For organizations to be able to develop, implement, and propagate well-informed and carefully-designed strategies, they need insights and not just plain numbers. The numbers need to tell a story about what has been, what is, and what will most probably be.
The insights that the numbers offer constitute the function of business intelligence, which organizations have been using for years to determine their next course of action. However traditionally, business intelligence involved extensive manual churning of data to derive valuable information. Today, with the rapid increase in the scale and volume of data-generating elements, from IoT devices to smart phones, traditional business intelligence is demanding a swift pivot to a more efficient and less labor-intensive process. This is where Augmented Analytics comes in.
Gartner 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. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management and deployment.”
Let us understand how Augmented Analytics, with the help of smart next-gen technologies like Artificial Intelligence, Machine Learning, and Natural Language Processing, is changing the face of data-driven business intelligence.
Limitations of traditional Business Intelligence
Traditional Business Intelligence process comprised two parts – data collection and data analytics.
The process required employment of highly-skilled professionals with rich technical expertise to collect data from multiple, disparate sources, and then apply analytical scheming to the collected information to produce business-critical information. The main drawbacks of this process are:
- Firstly, there was a shortage of the right resources who have the desired skillset and expertise to efficiently perform data mining & analysis.
- As the entire process was manual, it was highly prone to errors. Erroneous results may lead to expensive and embarrassing mistakes that could cost an organization its reputation, customers, and brand value.
- Manual data analysis being extremely time-consuming extends the duration between the initiation and completion of the process. By the time the analysis is completed, the data becomes outdated and insights render to be redundant.
Need for a smarter business intelligence solution
Organizations could afford to spend several months on mining a particular data set in the pre-digital era. But now, when about 2 MB of data is being generated every second for every person on earth, business intelligence solutions need to get faster and smarter. The modern business intelligence solutions are required to:
- Accelerate collection of data from the disparate sources such as cloud storage, IoT sensors, in-house data bases, and software applications in real time
- Verify and validate the authenticity, relevance, completeness, and correctness of gathered data
- Perform deep mining and analytics on the collected data to produce the desired information and insights
- Offer the derived insights in a quickly-scannable and legible format such as dashboards
AI in analytics
AI is what essentially augments the modern business intelligence solutions. But its role does not limit to automation and acceleration of the traditional manual process. The augmentation part involves making the business intelligence solutions smarter such that the analytics process is integrated with predictive and decision-making capabilities.
With simple automation, the business intelligence process is only sped up. With AI-driven automation, business intelligence solutions become capable of autonomously driving the data ingestion, data integration, and data analytics process. Further, based on the historical data and observed patterns and behaviors, the AI-driven business intelligence solutions can bestow predict capabilities to an organization for anticipating future course of action proactively. In combination with Machine Learning algorithms and Natural Language Processing techniques, AI can hold the hands of manual resources to help them comprehend the value of the insights. With increasing sophistication in AI technology, we can expect it to completely take over the manual processes by carefully identifying the types of queries needed to extract the required information to offering key inputs on the steps that human resources should take for deriving maximum value out of the obtained insights.
To sum up
The implementation of technologies like AI, ML, and NLP enable an organization to democratize data across its business verticals. Democratization of data and augmented analytics offer quick and easy access to valuable insights for accelerating decision making.
Cigniti leverages its experience of having tested large scale data warehousing and business intelligence applications to offer a host of Big Data testing services and solutions such as BI application Usability Testing. Cigniti’s open source big data testing tools help evaluate the reporting app for end-user’s adaptability and continuously review the observations with user & dev group, as a part of our Agile and DevOps testing. Cigniti Testlets offer point solutions for all the problems that a new age Big Data application would have to be go through before being certified with QA levels that match industry standards.
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