The most successful organizations put data at the heart of everything they do. Whether creating a forecast or hiring the right people, it’s the foundation of better business decision-making. And, when engaging your employees effectively, it can boost your entire company’s teamwork and efficiency. The challenge is in knowing where to start.
As data becomes more ubiquitous, it demands that a company diversify its standards for insights, reporting, and accuracy. Because of this, having users be a part of the overall plan from the start will give businesses an edge over the competition. This requires fostering a culture for being data-driven as a whole.
While others are stuck giving their IT departments responsibility over all data, you can perform faster and with more accuracy by enacting these concepts throughout your organization.
Start with Your Goals in Mind
With so much data being created, it’s important not to get overwhelmed. Businesses need to work out what it is they want to do with data before they start building a culture around it. It’s only from a position of clarity, transparency, and understanding that data can be used effectively.
Firms should start by looking at what they already monitor and consider how they can improve and expand upon it. It could be tracking important KPIs or creating a departmental budget. Whatever the plan, ensure it fits each department’s goals so they can be turned into actionable tasks.
Ways to Build Data into Your Company Culture for More Employee Engagement
Once you have a vision for data collection and use, it’s time to start involving stakeholders. It might be that reports are run on a managerial level, but it’s essential to build from the ground up. You should therefore try to engage staff throughout the organization.
These are some of the most effective ways to embed data-driven decision-making into your business:
1. Involve Users Early
The challenges of getting accurate and qualitative data are well known. To combat this, businesses need to involve the right end-users or assign data stewards at an early stage of the data funnel.
Nobody is better placed to select, review, validate, and approve operational data than the managers responsible for their activity and staff of that exact business line. They understand what’s really happening on the ground and can help to improve the accuracy of an organization’s data inputs.
As they become more responsible for data quality, these end-users will also develop a greater understanding of how it is processed and what resulting outputs mean. This will also have a positive effect on other important management reports that use these data sets. Inputting accurate and useful data will become the norm, and so too will using the outputs constructively.
2. Focus on Accessibility
Organizations should be investing in training to help end-users understand data, but they also need to make it accessible in the first place. Core stakeholders need to know what it is, how it’s processed or transformed, and where they can find it.
Traditionally it’s been the job of IT professionals to act as the gatekeepers and administrators of business records. However, as data becomes increasingly relevant throughout a business’s entire organizational structure, it’s no longer sufficient to place its management in the hands of an (already busy) few.
End-users need to be able to be able to access and even amend data when and where they need it. This throws up new issues concerning integrity and accuracy, so it’s necessary to track data on its journey through an organization. Any solution must therefore display a ‘chain of custody’ from the point of input, through processing, and onto its final destination in a report, forecast, or boardroom discussion.
This gives end-users the confidence needed to rely on data, safe in the knowledge that it’s accurate and traceable. It also makes it easier and more convenient for stakeholders to access data, encouraging them to use it in their day-to-day work.
3. Encourage Engagement with Smart Workflows
Implementing the right policies and processes can encourage stakeholders to input accurate data that feeds into the rest of the business. This is particularly important for companies that operate from multiple locations and use data across numerous functions.
By engineering the way data flows through organizations, it’s possible to actively encourage engagement from users. One example is the requirement for one regional office to enter data before the next can access and/or enter the information they need.
This approach requires end-users to take ownership of data and can help them to understand that their input does not exist in isolation. Instead, the collection, processing, and use of data becomes an exercise in collaboration.
4. Incentivize Engagement with Useful Outputs
It’s not enough to merely engage users with data through process. Incentivization can then play a major role. Input obligations and ownership may capture a user’s attention for some time, but the whole engagement can be boosted even further by providing useful information back.
Validating tranches of data is not a particularly rewarding task in itself. But, if helpful statistics (like KPIs used for their projects) and outputs are provided in the same system, users can fulfil their data management obligations whilst getting something back.
It all plays into the narrative that good data in equals good insights and useful information out. By introducing incentives that make it easier for users to fulfil their functions, you can motivate them to engage with the process.
Finding the Right Launch Platform
Data volumes are growing exponentially and the speed with which decisions must be made is also increasing. Now more than ever, businesses need to become data-driven – and to do that it’s necessary to engage with the stakeholders who actually create and use the data.
By providing a verifiable single source of data, Cohelion’s platform makes it easier for users of all levels and functions to confidently engage with information. Workflows and solutions are built to encourage ground-level users to take responsibility for data inputs, and the entire interface is designed for business users rather than IT professionals.
It all makes for a cohesive way to quickly and reliably offer data to all types of users across an organization. From this elevated position, data-driven decisions are far easier to make.