6 Steps Manufacturers Should Take to Optimize and Monetize their Company's Data
Your company's data should be one of your most valuable assets. But... let's be real. Unless your company is light years ahead of most, your datasets are probably fragmented, polluted, and underutilized.
Here's some comfort:
First, you're not alone. Poor data quality and management are probably among the most widespread issues our team sees in the world of Industry 4.0.
Second, there is hope. No matter how messy, incomplete, or scattered your data is, there are clear-cut steps you can take to improve it.
Let’s start with the root of the problem. When we put garbage in, garbage comes out.
Sloppy or incomplete user input, misaligned third-party integrations, and poorly defined data governance policies can pollute datasets to the point of obsolescence. And that amounts to a huge waste of resources.
In fact, an article in the Harvard Business Review recently pointed out that poor data quality is enemy number one preventing the widespread and profitable use of machine learning. And one study found that business and data analysts end up spending 50 to 80 percent of their time cleaning up datasets.
Given that these individuals are typically highly qualified --- and highly paid ---- experts, do you really want them spending their time fixing typos and deleting duplicates?
Many of these problems can be prevented by taking a strategic approach to data management. As with any other critical aspect of your business, this will require a comprehensive and well-executed plan supported by resources and a commitment from leadership.
Step 1: Build a Data Management Team
Start by assembling a cross-functional data management team. This should include your technology specialists, but it shouldn’t be limited to the IT department. Your goal is to empower people at all levels of the company to use technology to work smarter and more collaboratively. Try to have members of most major departments represented, and be sure to include a C-level sponsor who will own this initiative and drive buy-in and action.
Step 2: Evaluate your Business Processes
Start by taking a comprehensive look at your business processes. Here are some key questions to ask (and answer):
What does your data look like?
What data is generated through your company's day-to-day activities?
Is the information you're capturing necessary?
Does it have other applications?
Where is it stored?
Where are the gaps?
Where are your blind spots? What critical information are you lacking?
In what instances would additional insight improve operations?
Could data facilitate greater alignment between departments? For example, would it be helpful for customer service to have insight into the production timeline? Can marketing track the outcomes of leads passed off to sales?
What are your barriers?
At what stages of your processes is entering or maintaining data difficult?
Where are there too many clicks?
When is it too easy to forget to update fields or create records?
Does the system freeze or take too long for your users?
Is the type of information requested unclear?
When is it much easier for users to resort to paper?
Is it easy for users to enter data where they work? For example, if your sales team is on the road most of the time, do they have to wait until they get back to the office to update the system?
Where are your offline stockpiles? (Every organization has them.)
What processes are still being managed exclusively on paper?
Who's using spreadsheets saved to their desktop instead of storing information in the cloud where it can be backed up and accessed by others who need it?
What information is isolated at individual offices or branches? Should it be accessible across the company?
Step 3: Build Data Use and Management Into Your Operations and Workflows
Once you’ve assessed your systems and processes, consider how they can be adjusted to make better, smarter use of your data.
Can you mine data created as a result of your workflows to better understand your company, your customers, or your team? Can you build API connections between services to streamline information sharing?
If your company has implemented an enterprise-level business platform, merging your data within a single system can give you a more comprehensive picture of your business. But be judicious when deciding what data to capture or integrate. The last thing you want to do is bog down or muddy up the very tool that is supposed to offer you agility and clarity.
Next, brainstorm ways your company can effectively use the data you’re mining and analyzing, and weave these practices into your day-to-day operations.
One of the best strategies I’ve seen is incorporating dashboards into team meetings. By creating dashboards showing progress toward KPI’s, you can both keep your meetings on track and offer valuable insight toward the work that matters most to your organization. Plus, when employees know the data they’re entering will be reviewed and discussed publicly by their colleagues, you’d better believe they care about data quality.
Next up, tackle the paper and spreadsheets. If you’re still conducting any part of your business on these, your team needs to develop a plan to bring this process into the 21st century. Once you’ve stopped the bleeding and have implemented a digital replacement, work on transferring legacy processes and information into your new system(s) as needed.
And finally, look at everything on a small screen. Chances are, your employees are logging quite a few of their work hours on a mobile device. Your data capture and retrieval processes should be mobile-friendly. Otherwise, you’re making it unnecessarily hard on your team… and that leads to sloppy, missed or incomplete data input and lower levels of user adoption.
Step 4: Promote User Adoption
Let’s start with what success looks like here. Organizations that have achieved meaningful user adoption make data maintenance, analysis, and use a common part of day-to-day workflows. Everyone in the organization understands that maintaining data integrity is a core responsibility of his or her job. And individuals across the company know how to use data to work smarter.
The keys to reaching this level of success are to communicate the benefits, provide training, and hold people accountable.
Ensuring your team knows how to maintain and use data is only one half of the equation. You must also communicate why it’s important. Be sure to share outcomes and insight made possible through data use so that employees understand the benefits of their work. Conversely, illustrate the pitfalls or problems created through sloppy data entry or management.
Data management and information literacy should be integrated into your company’s training initiatives. To keep these issues top-of-mind, I’d recommend holding at least two webinars, lunch and learns, or workshops on these topics per year. Additionally, you should supplement these sessions with regular informational updates. Sharing a weekly tip or article will remind team members of your company’s focus on data use and quality and help them continue growing their skills.
Finally, as with any other aspect of your company’s operations, you must be prepared to provide oversight and hold your team accountable. Make it clear that team members are expected to follow the new processes and use data resources collaboratively. For example, if you’ve decided to use Salesforce to track and fulfill customer service requests, then no one should be bypassing the system by sending unlogged emails to customer service reps.
As you would with any other area of responsibility, set clear standards and goals for data use for each employee. Monitor performance toward meeting these benchmarks and incorporate these metrics into regular feedback or review sessions.
Finally and most importantly, ensure that users who create data hygiene problems are responsible for cleaning them up.
Step 5: Create a Plan for Data Maintenance and Governance
If process alignment and user adoption are your goals, your organization’s data maintenance and governance plan is your “how to” manual for accomplishing them. Your data management team should be responsible for developing a practical, actionable roadmap for preserving data quality, encouraging user adoption, ensuring responsible and safe data use, and promoting ongoing alignment between your business goals and your data management practices.
This guide should be specific. At a minimum, it should outline clear roles and responsibilities for data hygiene, security, and strategy and provide detailed instructions for how users should enter, interact with and use data within your organization. For example, who is responsible for performing routine data hygiene efforts? How often and how do they do it? Who holds your team accountable for maintaining data quality? What are the repercussions for failing to adhere to data standards?
The guide should also outline your data security model, which should be designed to ensure that team members have access to exactly as much data as they need to successfully do their jobs (and no more). It should also cover database design specifications and incorporate tools such as automation, validation rules, help text, and data dictionaries to limit the instance of user error and uncertainty when entering or data or building integrations.
Step 6: Establish Data Management as a Strategic Investment
If all of this sounds like a full-time job, it is. That’s why manufacturing companies that are serious about digital transformation are hiring staff members with titles like “business analyst,” “data scientist,” and even “chief data officer.” They know that tackling these issues will take a great deal of time, attention, and resources.
However, the costs of poor data management can be devastating. At best, your investments in modern technology and cloud-based platforms will be diminished. At worst, poor data management can lead to inaccurate pictures of your business’s overall health, lost inventory or assets, costly mistakes, poor customer service, or missed opportunities to capitalize on or react to emerging trends like AI and IoT.
Data and analytics will continue to serve as major drivers for digital transformation. Companies that are able to effectively leverage the mountain of data modern technologies can produce and capture will have a crucial competitive advantage in the marketplace. Those that don’t may lose big.