It is a regular Thursday afternoon, and lunch was pretty heavy. As the leader of a team of business analysts, you feel the week is about to close on just the right note. All of sudden, your boss pings you out of nowhere and wants to understand why the sales of an established product line have dipped over the last quarter, and how we can course correct.
There’s some respite since you have a team just for this. But then, another message informs you that results are expected within the next hour and are due for a 4 PM executive review. It’s a scramble, and suddenly you start appreciating the value of time and the need for automated analytics capabilities.
In this article we’ll be discussing the importance of automating data analysis, the advantages it offers, and the role of analysts in a digital ecosystem fostering automation.
In today’s digital world, speed to information is one of the pillars of disruption. Speed is super critical in certain environments, and the consequent actions may make or break a company. But analytics teams attempting to deliver insights at speed, are not just expected to crunch numbers and produce fancy dashboards. They must also recommend the next steps to salvage/improve the business situation at hand, and do so quickly. The complications of delivering this could be categorized as follows:
So, if we expect analysts to perform the entire spectrum of activities listed above, the output would just be directional insights and probably sub-prime actionable. This is where automated data analytics saves the day, by performing multiple functions simultaneously, and mitigating all the constraints related to manual data analysis.
Automated data analysis is the process of using computers and algorithms to drive big-data analytics with minimal or no human intervention. Enterprises can choose to deploy solutions that partially or completely automate the analytics process, bringing the data to life.
Technology has evolved to combine data and analytics to usher in a new era of large-scale, evidence-based insight generation and recommendations. Machine Learning, Natural Language Processing (NLP), and Natural language Generation (NLG) technologies are eliminating the need for human intervention; and the insights generated are not just factual, but causal, predictive, and prescriptive.
All we need to do is to assimilate the “more-often-isolated” datasets, build an analytical layer for machine learning algorithms and provide a democratization platform (Chat, Voice) to the end user.
The analyst is now suddenly capable of connecting the dots across non-data-based sources, as well access critical insights from internal and external data sources. The analyst can also focus more time on recommendations to fix or improve the situation, versus trying to find out how deep the waters are.
If analysts have an automated insights discovery solution, all they have to do is “ask” the right set of questions using textual querying or voice commands, and get the pertinent answers in minutes. The focus of the analysts would then have to be on “fixing” the challenge based on the insights, and not just finding it.
Data and AI-driven Analytics are transformational, disrupting traditional business models and ushering in a new era of breakthrough innovations. Harnessing these evolving technologies will unlock multiple benefits for enterprises, making it almost unthinkable to go back to old ways of doing business.
A few of the primary benefits of data analytics automation are as follows.
Let’s move on to how you can integrate automation into your analytics initiatives.
Enterprises can either implement their data analytics with partial or full automation, depending on their requirements and the analytics team structure. With partial automation, the data team would still need to write code and carry out tasks related to analytics. With end-to-end automation, the BI solution would be conducting the entire analytics process and presenting the data team with insights. Stakeholders would then have to make business decisions based on the insights gathered.
Full automation completely eliminates the need for human intervention. The software leverages AI, ML, and NLG technologies to not just automate the augmented analytics process from data discovery to real-time insights, but automate the decision-making as well. If an enterprise is experiencing low inventory levels at a retail outlet, automation will place an order with the distribution center, and even inform production units if market trends suggest a rise in demand for a particular product.
But how much of, and which part of your analytics process you want to automate is entirely dependent on you and your business requirements. Your data pipeline can benefit from automation at every stage.
Modern businesses generate petabytes of data every single day. Being able to effectively analyze the data and act on the insights, is at the heart of a successful business.
Data answers questions, confirm things that you know and reveals new insights you were unaware of. And automated data analysis is the key to timely and accurate data analysis, and informed business decisions.
“Automated” is the ultimate competitive advantage.
Course5 Discovery is an AI-powered Augmented Analytics solution, geared toward promoting an insights-first culture that values data-driven decision-making.
Discovery enables automated data extraction and management of meta-data using a network of inbound data connectors.
The Machine Learning knowledge base keeps improvising insight generations, recommendations, and publishing, with a centralized Search Index that keeps refreshing for faster querying.
The solution is rounded off with an automated narrative generation service publishing precise and contextual insights, which is the order of the day.
Contact us for a free run. And if you’re so inclined, we will help you integrate Discovery into your analytics ecosystem and transform your capabilities with automated actionable insights.
Note: This article was recently updated to focus the content around automated analytics, and account for the evolution in technology since the time of original publication.
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