If you’ve ever driven through dense fog, you know how disorienting low visibility can be. Your car’s headlights help a little, but you still can’t be sure of where you are. When you’re unable to see much of your surroundings, you rely on limited cues and information. The split-second decisions you make to drive safely to your destination aren’t based on everything that exists behind the fog. You have to slow down and depend on what little you can make out in front of you.
A similar process happens when you don’t have a lot of visibility into your business operations. Things are humming along, but marketing activities might be out of sync with the company’s supply chain. The marketing department is promoting products the sales force doesn’t have, and customers are getting upset. Clients are walking into stores expecting products to be on the shelves and going home empty-handed.
Scenarios like these can hurt a company by eroding trust with customers and employees. Had visibility measures been in place, marketing and sales would have had access to real-time inventory figures. Any advertising and sales activities would have been redirected to other products that were in stock. Without business process visibility, it’s difficult to align initiatives and activities with companywide goals. This article outlines ways to gain a clearer vision of business operations.
1. Increase Data Reliability and Efficiency
Modern organizations gather data from diverse sources, including suppliers, customers, and employees. Companies also compile information from every transaction, asset, and network activity. Most of those records go into one or more repositories, including data warehouses, data lakes, business intelligence applications or customer management systems.
Although staff members rely on this type of information to make strategic and everyday decisions, it can become unreliable. As data traverses through various repositories the quality may diminish and lead to incorrect analysis and evaluation. An inefficient data pipeline puts undue burdens on both data teams that are responsible for enterprise data and the employees that consume, or use that data to make business decisions.
Data observability solutions help catch and eliminate errors in data quality and reliability. These platforms can identify transactional data that isn’t flowing to all departments or bottlenecks that are slowing things down. With the artificial intelligence behind observability solutions, you can reduce the need for manual data quality coding, and automate data quality rules.
2. Develop a Staffing Plan to Optimize Employee Workloads
In theory, most leaders hire for the optimal number of positions they need to carry out a department’s objectives. Directors or department heads identify staffing requirements by demand or volume, skill or capacity gaps, and projected resources. They take into account turnover projections and expected vacancies due to internal promotions.
However, quite a few employees experience situations where human resources fall short of demand. Workers are either stretched thin because there aren’t enough people, or they’re juggling unrealistic workloads. A front-line supervisor who normally manages a team of five direct reports now has 12. And a recent surprise layoff means a graphic designer and copywriter are suddenly sharing web design and management responsibilities.
While some oversights are bound to happen in volatile labor markets, not having a staffing plan in place exposes vulnerabilities. You might have employees carrying out duties they’re not properly trained for. Customer service and quality levels could suffer, and operational efficiency may decline rapidly. By consolidating labor market and internal data with goals and functional needs, a company can avoid staffing and skill gaps.
Call centers, for instance, use historical and projected call volume data to determine scheduling needs. Everything from shift lengths and overlaps to break periods is optimized within apps that analyze available information.
HR and department directors can take a similar approach by evaluating what activities the company needs people to accomplish. Leaders should then identify what skills are necessary to complete those activities within a reasonable daily schedule.
3. Address Misalignment of Marketing and Sales Activities
As in the example earlier, marketing communications that don’t match what a business can deliver will cause frustration. Studies and statistics show that e-commerce consumers tend to grow most frustrated with inconsistent brand messaging. If your website says one thing and your online store reps say something different, you could lose potential sales. Disappointed customers may decline to make repeat purchases or eventually cancel services.
Inconsistent messaging could include preorder forms that promise a promotion that never comes through. It can also arise from a brand voice that changes from one commercial to another. Marketing that switches between emphasizing a low-price strategy and touting premium, white-glove service could come across as erratic or chaotic.
When no one really knows a company’s brand identity, marketing and sales activities become disjointed at best. Comparing what sales staff are doing and hearing in the field with marketing’s efforts can help identify some of these disconnects. While it’s marketing’s job to create awareness, there must be agreement on who the targeted customer is.
There should also be a strategic consistency around what makes the brand unique. Marketing can’t promote personalized customer service that goes the extra mile if sales staff aren’t trained to deliver. Likewise, ads shouldn’t highlight service capabilities that customers experience as inconsistent or report problems with.
Gaining a clearer picture of an organization’s day-to-day activities involves combining information and employee perspectives. Taking in several cues from inside and outside the business helps determine what’s happening where.
Process visibility comes from examining those cues in areas like information pipelines, employee workloads, and marketing and sales activities. By treating individual data sources like puzzle pieces, you can begin to see how and where to make them fit.