The efficacy of your analytics capabilities has likely been one of the casualties of the pandemic. Even if you have years of historical consumer data flowing through mature models that maintain data accuracy, it’s possible your predictive abilities to scope business opportunities has suffered a setback.
Who could have imagined the impact of a global pandemic on data analytics?
The pandemic was the catalyst for rapidly changed consumer behavior, disrupted supply lines, digital acceleration, and created huge variations in demand across geographic regions. Worse, in many cases it shifted brand preferences and added local product availability or shorter delivery times to consumer’s purchase decisions. The lesson learned: You can’t predict the unpredictable!
We find three ways to reduce unpredictability, uncertainty, and risk—and not just by relying on a gut feeling. So, what’s required?
Three ways to get your data ready for anything
1.Get more data
The first thing that comes to mind is that you might need more data—and not just for the sake of it. But when there are unexpected changes or results, the tendency is to want to dig into the data to find out more. And when the data’s unavailable, that’s hard to do.
For example, consider retail data. Common practice is to aggregate data at the store level—inventories, gross sales, traffic, and so on; but when the pandemic hit, it would have been good to know where customers lived and how badly affected those areas were. At different times, it may have been only a subset of customers that was affected, impacting single stores. At other times, it might have affected entire regions, encompassing several stores.
If the above scenario feels familiar, here are some questions to ask yourself: What were the shortages, overages, and challenges faced by those locations? Could better demand planning based on more granular data describing the localized impact have made a difference?
Continuing with this example: Consumer goods manufacturers who were able to obtain data about product sales by region, from their retailers, were able to aggregate and share back data on what products were selling where. This benefited both the retailer and the manufacturer. For example, some retailers were able to see that demand in East Coast regions was very different from that from West Coast regions and adjust their orders accordingly. Better inventory planning from retailers, meant that goods were more likely to be sold than sit on shelves and that stockouts were less frequent, all benefiting the manufacturer. There’s no doubt that having more data on supply chain partners capabilities and inventories could also have been valuable.
The more data, from various parts of your business, the better. In times like these, you want trustworthy insights that will help you predict the most opportunity for your organization.
2. Train on data skills
Another factor that’s becoming increasingly important is data literacy. How data literate are your business users? Business users—the marketers, customer service reps, and sales reps in the room—often bring a different lens to analytics and reporting and, having them trained on data skills can pay significant dividends.
Think about it this way: Being so close to customers, marketers, customer service reps, and sales reps all have a unique perspective on data analytics. Oftentimes, this can help steer predictions in ways that extrapolation of historical trends cannot, and their market knowledge can help better pair more predictable parameters that might normally be considered uncorrelated. As you get more sophisticated, they can also help determine if data collected for one purpose can be used for another.
With the focus on data literacy and higher volumes of data, comes the need for better data skills. Historically, data scientists have done the analysis and business users review the reports. Now, organizations are realizing the shortage in data skills and are improving the training.
Data literacy training programs ensure data is more relevant across the organization, while improving collaboration and morale. Learning new skills can boost job satisfaction and self-esteem, and when mundane tasks can be automated, eliminate the tedium. Improving data literacy skills across your organization will pay dividends in increased collaboration and understanding, leading to less risk and uncertainty.
3. Ensure data accuracy
A critical area for improvement is data accuracy. Study after study show not only that a good portion of data—particularly customer data—is inaccurate but that organizations lack trust in their data. Lack of trust leads to decisions that are based off gut feeling and not data. While experience is highly important, this past year has proven that change can happen quickly, and poor decision can be made based on outdated trends and biases. How effective is that?
The journey to data quality maturity takes time, however there are steps you can take now to set yourself up for success. When evaluating your data strategy, consider these best practices:
Capture and maintain data accuracy at all times.
Deal with issues at the source, whenever possible.
Hire a chief data officer and establish data ownership across the business.
Monitor data quality, accuracy, and completeness continuously.
Remember that data is an ongoing process that should be everyone’s responsibility.
Some types of data decay over time and must be refreshed. That’s why you want to ensure you have data processes in place to uphold data quality control. This is important to build trust in your data assets. When you do this, improvements can be made as the scope and nature of your organization’s data adapts overtime.
What's the role of technology?
Technology is a major part of the overall data strategy, but it needs people, processes, and the necessary skills for optimal results. Integrating data validation solutions or a data quality platform can automate data processes and improve accuracy. Pair this with artificial intelligence (ai) and machine learning, you can improve predictability and speed, which can help you respond to the post-pandemic world and uphold trust in your data assets.
The right data technology, with the right capabilities, can also help improve business agility—which is a challenge for many organizations. We define business agility as the ability to make and act on business decisions quickly. Having an agile way to collect data, process it, check for accuracy, and analyze the insights, will lead you to faster and better decision-making.
This is where artificial intelligence and robotic process automation comes into play. This gives you the ability to not only speed up manual processes, but to also ensure consistency so that the results and the data produced can be trusted. Planning for agility will enable you to make rapid changes to your business operations when you need to reduce risk and uncertainty, and when unforeseen events and changes in market conditions necessitate swift responses.
The future will be data driven. Those who plan for that future will become more efficient and effective. We’ve already seen the impact that digital natives can have—Amazon, Google, Facebook—and the list continues to increase as more fintechs, regtechs, insurtechs, and others emerge, creating market change and disruption. Our own research shows that companies with a high level of data management maturity are often more profitable and experience higher rates of growth.
Read how business leaders have changed their data perception and usage—and how they believe data will help them weather the next year.
