Automated Data Analysis 101
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Automated Data Analysis 101

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.

What is an Analyst Expected to Do?

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:

  • Massive data sources
    With data evolution in multiple streams and with ever-growing volume, variety and veracity of data, analyzing every bit of it, or even enough to generate deep insights, is a tall ask. Unstructured data just adds to the list. By the time the data is assimilated, ships have sailed!
  • Complex data modeling for insights
    Businesses are increasingly focusing on the “Why”, and “What if”, rather than “What”. Building sophisticated models are possible (ensemble) but need to be fine-tuned and improved. Not all analysts are geared up for this.
  • Narratives in English
    The last thing you want in a read-out, unless you work for NASA, is a jargon-filled language that is not easy to comprehend. Execs want something they can ingest on the go, which is self-explanatory and actionable.
  • Budgets
    Unless technology is the core offering and your budgets are COGS, and lesser mortals are OPEX, which is more prone to cuts. Hence investments related to talent, infrastructure, etc. are not easy to manage.
  • Demand across functions
    Almost every function within the organization (Marketing, Sales, Supply Chain, HR, Finance, IT, Admin, Operations, etc.) wants to leverage analytics for insights and recommendations to add value to the firm. But even if they are subject to the above constraints and the need for domain expertise.

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.

What is Data Analytics Automation?

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.

Why do Enterprises need Analytics Automation?

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.

  • Speed to Insights
    Automating your data analytics pipeline completely or even partially, accelerates the analytics process greatly. The turnaround time from query to insights is minimized from days to minutes, and even the business intelligence reports are comprehensive and easy to consume. The targeted recommendations and readable narratives powered by augmented analytics promote on-time decisions and better adaptability.
  • Save Resources
    Automating even a part of the analytics process will free up hours of time for data scientists, engineers, architects, and analysts. The analytics solution can handle time-consuming, labor-intensive, and repetitive tasks. The enterprise gets to save on time and money needed for human intervention in analytics initiatives, and even avoid the occasional human error affecting insights.
  • Foster Creativity
    With automation doing most of the heavy lifting, there is a lot of free time for your data wranglers. With easy access to critical insights, they can focus on creative pursuits to improve existing business processes and generate a higher ROI.
  • Enhance Processes
    Manual analytics initiatives usually encompass complicated processes and systems. It’s an intensive process from data discovery and data integration from multiple sources, to coordinating with different departments and the final analysis of the data. Automating data analytics offers you the option of avoiding these complications and eliminating any chance of human error while conducting repetitive tasks. You can enhance the analytics solution if and when there are any errors in the process, and improve your overall business process based on your automated analytics capabilities.

Let’s move on to how you can integrate automation into your analytics initiatives.

Automated Analytics Use Cases

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.

  • Data collection is one of the most resource-intensive processes before you can even think of analysis. From accessing different file formats to extracting data from 3rd party applications and other external sources, data gathering takes time, effort, and experience. Automating this crucial stage allows enterprises to deliver data analysis at a rapid pace. You can even schedule your analytics software to run at regular intervals, updating you with fresh insights from new data.
  • Dashboard construction requires technical knowledge and experience. It also necessitates that enterprises take out time for setting up the dashboard, more time for the analytics process, and only then can the stakeholders make business decisions based on the insights. Automation takes care of all this, by enabling enterprises with data stories powered by interactive dashboards integrated into their analytics process. Where decision-makers can access contextual insights in real-time through effective querying capabilities, helping them adapt business processes and strategies, to optimize efficiency and maximize ROI.
  • Business Intelligence proficiency is not restricted to your dashboard, requiring enterprises to take a deep dive into their data for gathering unique insights. You need to be able to aggregate your data and analyze it based on different dimensions affecting it. The combination of factors being analyzed determines the insights that automated analysis will deliver. An effective BI tool will enable you to customize your aggregation and dimension factors through simple filters, and access the insights post analysis.
  • Machine Learning is what helps augmented analytics tools to learn from historical data, gauge performance, predict future trends, and outperform their human counterparts. But permanent ML models do not suffice with changing market and customer dynamics. Automation steps in, and eliminates the need for manual updating of the model. Automation can construct and update different ML models based on selected parameters and factors that an enterprise wants to incorporate into an analytics study. ML models can also be integrated with algorithms for anomaly detection, which are of great value in financial institutions. Fraudulent or suspicious transactions can be instantly flagged for inspection, with alerts being automatically sent to respective professionals within the company.

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.

Automating Data Analysis with Course5

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.

Yash Bhattacharya


Yash Bhattacharya

Yash has over 5 years of experience developing thought leadership content both in-house and for clients across industries. Mostly working with giants in the technology...

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