It is a fine Thursday afternoon and lunch was pretty heavy. As a leader of a team of business analysts, you feel the week is just about to close on a right note. All of sudden, your Boss pings you from nowhere and wants to understand why the sales for the last quarter for an established product line has dipped for a new geography and how can we course correct. There’s respite since you have a team just for this! Then another ping, results are expected in the next hour and are due for a 4 PM executive review. It’s a scramble and suddenly you start appreciating the anxiety a standby passenger feels in line waiting for his turn to check if he made it to the flight!
Does this sound familiar?
If you have never been in similar situations, chances are you work in a wonderful monopolistic company and don’t have to worry about speed to information and actions. You can safely stop reading this post.
What is an analyst expected to do?
In today’s digital world, speed to information and consequent actions make or break companies. One of the pillars of disruption, speed is super critical in certain environments. In today’s world, Analytics teams are not just expected to crunch numbers and produce fancy dashboards but also recommend the next steps to salvage/improve the business situation at hand and quickly. The complications to deliver 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 tuned and improved. Not all analysts are geared up for this.
- Narratives in English: The last thing you want in a read-out is something that sounds like English but is not English, jargon (Unless you work for NASA)! Execs want something they can ingest on the go, is self-explanatory and actionable.
- Budgets: Unless technology is the core offering and your budgets are COGS, lesser mortals are OPEX which is more prone to cuts & hence investments are not easy to manage talent, infrastructure, etc.
- Demand across functions: Almost every function of 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. Of course, they are subject to the above constraints and domain expertise as well!
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’s.
What if the starting point for the analysts is not data assimilation but finding insights, patterns, and anomalies from the data within the larger business context?
The analyst is now suddenly capable of connecting the dots across non-data based sources as well and can focus more time on recommendations to fix/improve the situation versus trying to find out how deep the waters are. Now that would be supercharged performance!
Enter Machine Learning and the World of Automated Insights!
Technology has evolved to combine data and analytics to usher in a new era of large scale, evidence-based insight generation and recommendations—automatically without the need for human intervention (Read Machine Learning, NLP, NLG!) The insights generated are not just factual but causal & predictive as well.
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.
Now let’s revisit the situation described at the start of this article. If they had an automated insight discovery solution, all they had to do was “ask” the right set of questions using chat/voice and get the pertinent answers in minutes. The focus of the analysts would then have been to “fix” the challenge and not just find it!
Data and AI-driven Analytics are transformational, disrupting traditional businesses and models and ushering a new era of breakthrough innovations. Harnessing these evolving technologies will unlock multiple benefits for companies, almost making it unthinkable to go back to old ways of doing business.