Scarcity isn’t a concept that’s frequently linked to data these days. Big data, data warehouses, fast data pipelines - we’ve reached a point where most companies capture far more data than they can use. And that’s the rub - with the investment we’re making in collecting, transforming, and archiving structured data, everyone needs access to a dedicated analyst team.
But almost nobody does. And that’s a problem.
Putting enterprise data to work – whether diagnosing changes in conversion rates, analyzing customer response to marketing campaigns, or optimizing product launches – is a challenge few organizations are equipped to handle. The process requires specialized skills, training, and time. Today, most companies tackle this problem with teams of analysts who manually slice, dice, and report on the data that continues to arrive in the warehouse.
The problem with this is that even the best teams can’t scale in parallel with the amount of data we’re collecting. And what most teams quickly learn is that there’s never enough capacity to address even a quarter of the questions they’re asked. For example, a recent McKinsey survey found that fewer than 20% of companies worldwide have achieved “advanced analytics at scale.” The challenge lies in the day-to-day. Most analysts spend over 40% of their time exploring data and only 13% of their day producing insights and presentation used by the business (source: Bob Hayes, Business Over Broadway).
This is not to say that these expert analysts are starting to deliver diminishing returns to their organizations. Rather, the challenge is that with today’s tools, our ability to scale data analysis is capped by our individual time and energy - the effort required to answer every incremental question is the same. And when the amount of data we collect and the questions we can ask of it are growing exponentially, the gap between what we want to know and what we can discover grows by the hour.
In an excellent HBR article published late last year (What Great Data Analysts Do — and Why Every Organization Needs Them), Cassie Kozyrkov, Chief Decision Scientist at Google, explores the three primary disciplines in data science. She makes an excellent argument that between machine learning, statistics, and analytics, the most valuable skill for any organization is analytics.
There are three reasons why analytics tops the priority list - speed, breadth, and storytelling skills. Says Kozyrkov, “Speed is [an analyst’s] highest virtue, closely followed by the ability to identify potentially useful gems. A mastery of visual presentation of information helps, too: beautiful and effective plots allow the mind to extract information faster, which pays off in time-to-potential-insights.”
We believe that it’s possible to augment the speed of even the fastest teams of analysts and help them reach more of the business, examine more data on a daily basis, and inform more operational decisions. With better diagnostic platforms, these expert analysts can quickly answer more questions in a shorter period of time and catch up with the tides of data flowing into the warehouses.
When analysts are spending less time doing rote work and more time thinking creatively, finding rogue stories in the data, they can regain their place of prominence in the world of data science. And, by equipping their business partners with more self-service analysis tools, they can help everyone become an analyst.
Sisu is dedicated to helping analyst teams support every business unit and answer every question - and even some that haven’t been asked yet. With this goal in mind, we’ve built the fastest diagnostic platform for structured data: giving each of our customers the power and confidence that comes from having a dedicated analyst team.
Is your company informing every decision with the power of a dedicated analyst team? If not, schedule a demo today and see how Sisu can help.