Conducting Post-COVID-19 Analytics with Limited Data
The past year left companies with a dearth of reliable information on their customers, but there are ways to make the most of what you do have.
In these post-COVID-19 times, your analytics program may be running into a serious issue — highly limited data. Think about it. Customer behavior during the lockdown was obviously markedly different from what it looked like pre-2020. As we emerge from the lockdown, is it possible to model customer behavior on this data? If not, we can’t simply go back to using pre-lockdown data. Customer behavior has changed markedly in the last 18 months; the data we have may no longer reflect current behavior.
That leaves companies with a very short span of data that might be relevant. How do we conduct analytics with such limitations? For companies — many of which are already struggling due to lost revenue during the pandemic — the stakes are high for getting it right. Analytics-driven insights based on irrelevant data could have serious repercussions, resulting in poor marketing, customer service, or product development.
To be clear, there are no easy answers to this problem. However, there are ways to maximize the resources at hand and, even in what you might call a “data dearth,” still derive keen insights. Here are a few things your organization can consider.
Shorten the Data Analytics Life Cycle
When your data is limited, your ability to iterate quickly is key. You may not have insight on long-term trends, so look for short-term trends instead. Get smaller answers quickly based on what you know now, and continually adjust as new insights become available.
To do this, you can’t afford to wait the standard three months for analytics insight. You need answers now. Here are some tactics that can help in shortening the life cycle.
Provide full visibility into the process. When a business user poses a question to the analytics team, make sure that they’re not waiting in the dark for the answer. Give them full visibility into the project’s progress. This not only holds the analytics team accountable, but also allows a stronger partnership between analytics and business that allows everyone to exchange knowledge. You will inevitably find that business users have information that helps guide the analytics process beyond what they may have offered upfront.
Additionally, automate as much data prep as possible. Currently data scientists and analysts spend an inordinate amount of time preparing data for analytics. This might include removing bad data, making sure syntax is consistent, and identifying outliers, among other tasks. Leverage your available data-prep automation tools to shorten this process.
Make Sure You Have Access to All Relevant Data
When you’re already dealing with limited data, the last thing you want to do is let any potential valuable information go to waste. This is often the case when an organization’s data is highly fragmented and siloed. Here are two ways to remedy the problem..
First, combine data sets from federated sources. Data sets by themselves may not have the value you need, but when they’re combined with attributes from other data sources — internal or external — the answers you need may be found. For example, a company that sells ice cream through grocery outlets and restaurants might have data on their internal sales for the past two months. Simply analyzing these sales might produce deceptive results, however, because the data may lack context. How do we know, for instance, what really caused a dip in sales in mid-June?