6 Data Management Tips to Improve Your Business
Whatever your industry, chances are you have access to important data that could fundamentally change the way you do business. The trick is figuring out how to use it optimally.
As the Chief Information Officer at C&W Services, all delivery and adoption of technology to our 15,000 colleagues is overseen by my team. We’re responsible for making sure the technology deployments we provide are aligned with the organizational goals and industry best practices. As each division of C&W Services defines its annual goals, we weave technology seamlessly through them and present our colleagues with options on how to attack gaps in their processes and analytics.
For many organizations, shifting business requirements and budget realities mean technology deployments must be both strategic and nimble. At C&W Services, we were particularly focused on using technology to improve business processes. As with many organizations, a wealth of data exists in standalone applications, lying idle and waiting to be leveraged. Finding a way to analyze that data across applications had the potential to unlock greater efficiencies.
In our data analytics journey, C&W Services found six key learnings that can help any organization’s transition from merely talking about key metrics to acting on data for strategic decision-making. Along the way, you’ll also help find shortcuts to actionable data within your organization.
Data provides meaningful and valuable insights that equip us with technological decision-making capabilities
1. Start with your data model. Chances are, your organization has several targeted, best-of-breed applications for the functions they support. But what’s the value of this information if it’s stored in disparate systems? To begin the data-management process, it’s important to align your data model throughout all applications within your company to allow correlation of data across your systems. Data visualization can significantly improve decision-making within your company. However, source data cleansing and alignment is a first key step and should not be underestimated.
2. Put a data management process in place. Now that you’ve aligned your data model, it’s important to keep it organized. Put a process or system in place for maintaining the source transaction and master data. Proper data management will enable your organization to build on its data model and confidently augment existing analytics over time. Nothing will kill your data analytics or technology deployments faster than a loss of confidence in the output due to poor data management practices.
3. Make visualization count. When setting up data visualization, keep your end goal in mind. Make sure you have a full understanding of the behavioral changes your dashboards will enable and ensure you have partnered with your key business stakeholders. For example, in facilities management, data can be a game changer in predictive maintenance. In our industry, understanding the leading indicators of maintenance events and visualizing data across a multitude of data inputs can empower maintenance teams to proactively improve maintenance processes and therefore extend the life of machinery or physical assets for our customers. Consider how data visualization can drive similar process improvements for your business.
4. Understanding where data ownership lies. Once you start the journey of cleansing your data, putting the pieces of your data model puzzle together, you inherently open the dialogue of “this data can’t be right, who owns it.” It is critical as an organization that you establish a regular dialogue on ownership of dashboards and their depicted data. Your technology peers don’t own the data nor did they build the metrics on the data. As you transform your organization from one that captures but does not truly rely on the data to one where both master and transactional data are fully transparent, the ownership of data quality and metric definition is a key success factor in growth and process adoption.
5. Build your delivery engine. I like to compare bringing visualization into an organization to taking your child to the toy store of their choice. While you might not know exactly what you’re looking for at the outset, once you start down this path, the ideas among your employees will flourish, and your pipeline of requests will grow at a rapid pace. Releasing these capabilities prematurely can lead to a number of false starts. Before you bring visualization into your organization, make sure you’ve built your engine, established an intake process, developed a prioritization mechanism, and considered any and all security implications.
6. Empower and train your business partners. Visualization should be a true partnership and IT should not be the only one holding the keys to the car. Take the time your organization needs to build its own self-service engine. Determine the boundaries of when your team should operate versus your technology team, and how IT can provide guidance and support. The power of the platform happens when those closest to the process can identify a need and fill it with little ‘wait time’ on your technology team.
The term “Big Data” is already thrown around a lot, but you’re only going to hear it more and more often in coming years as the concept of Big Data fluctuates to adapt to the ever-changing market. In our organization, data provides meaningful and valuable insights that equip us with technological decision-making capabilities that streamline efficiency, track safety data, and improve customer satisfaction. When managed and used correctly, data can be the key to unlocking an insight that allows your company to soar to new heights or expand profits beyond all estimations.