You’ll often hear executives and those in other leadership roles speak about the need for their organizations to be data-driven. They may say things about making more decisions based on data and openly sharing information with colleagues. Managers might even proclaim that they want to make this the “year of data.” But what do leaders really mean when they toss around catchy phrases like “data-driven”?
An organization must do more than gather information to be truly data-driven. Without proper processes, analysis, and data teams, business decisions may be misguided at best. To be data-driven is to use information that’s accessible, meaningful, and relevant enough to impact choices. A data-driven decision process happens when employees have a steady stream of facts they need to choose a direction.
Data teams and group structures play a significant role in providing that stream of information. Execution of strategy, the oversight of procedures, and analytics and interpretations typically rest on the shoulders of data teams. Without them, the move away from gut-based decisions isn’t really possible. This article discusses why businesses need to prioritize data teams and information to become truly data-driven.
Determine Data Goals
To have an effective data-driven process, you need to know why you’re collecting information. Is it to understand who your customers are and their motivations? Maybe you’re trying to get to the bottom of a consistent decline in sales. Or you’re attempting to discover what drives consumer choices in a competitive industry without significant product or service distinctions.
By looking at the problems you want to solve and your existing data pipeline, you can develop a future road map. Perhaps you haven’t identified distinct customer segments and personas through surveys. Maybe you’re not asking the right questions or failing to combine your customer information with other sources. You might also be sending surveys at less-than-ideal steps in the buyer’s journey.
Some of these shortcomings could be because you don’t have a data team that works with different departments. If your goal is to predict who’s most likely to buy from your business, you may need a centralized group. This combined team of data specialists could sync information from marketing surveys, CRM apps, and service and billing databases. The group could then design, pull, and interpret the synced data so it clearly identifies separate customer personas.
Make Data Accessible
Employees need real-time access to relevant and reliable information to make data-driven decisions. But if that data is siloed or isolated and not searchable and dynamic, it can lead to choices that miss the mark. That’s why some companies prefer decentralized data teams that use a shared tool that pulls from multiple information sources.
In decentralized groups, each department has a smaller team of data specialists. Marketing, finance, customer service, and the warehouse all house a data analyst and engineer. Each smaller group concentrates on structuring how their departments gather information and feed it into a centralized application. If marketing wants to identify buying behaviors, that department’s data specialists focus on how to accomplish that with data.
However, since marketing shares its data in a common tool, other departments can find and extract it. The customer service data team exchanges information about churn predictions, upselling trends, and support patterns. Finance and warehouse teams upload reports about payment methods and inventory cycles. Managers can sort through, combine, and work with that information to answer the question of who the company’s customers are.
Conduct Thorough Analysis and Interpretation
Pull up a report in a CRM dashboard, and it can tell you your recent email blast outperformed the last one. Your open rate was 5% higher, and the click-through rate (CTR) increased by 8%. While information like this might indicate a move in the right direction, it can also be misleading. The problem is that reports only show the numbers. What’s missing is the why behind those changes.
To figure out the why, you have to consider information outside those reports and arrive at a likely explanation. Perhaps the email’s higher open rate has nothing to do with the subject line and is completely random. Maybe the open rate is actually lower than industry averages. The bump in open and CTR rates could also show or predict a shift toward a particular product.
Companies that have defined data strategies and processes can leverage analytics to make predictions. Leaders can predict what customers will buy instead of simply knowing who’s most likely to make a purchase. And thorough analysis and interpretation of existing data might lead to new product developments or identifiable shifts in customer characteristics.
Data teams that combine centralized and decentralized structures can work better for businesses in the predictive analytics stage. Each department has a data specialist or scientist that builds forecasting models for that area’s needs. But within the center of all the departments is a data leadership team to provide direction, training, and supervision. This group bridges information between areas like marketing, customer service, and finance.
It’s easy to think that a business is data-driven because leadership touts the idea or employees collect information. However, becoming a data-driven company is more complex than that. To make informed decisions based on facts, your business has to have a data strategy and the right team(s) of experts.
A centralized, decentralized, or combined group also has to design and manage processes that ensure company-wide data access, sharing, and analysis. These structures do much more than bring data into an organization. They make sure decision-makers have the information to determine what’s happening, what will happen, and why.