Companies are driving ahead with data transformation. But so far, the results are mixed: Few companies have achieved their goal of becoming a data-driven organization. So, where are they going off track? First, it’s important to define what it means to be data-driven. Data-driven organizations not only collect data, they collect the right data and use it to inform all decisions made across the business. These organizations know how to mine data, extract insights, and put learnings to work to deliver business results. And they enjoy higher revenues, better customer retention, and more operational efficiency than their peers.
What’s holding so many companies back? It often comes down to treating data as an “IT-only” activity, with business executives who assume IT will lead and execute the steps toward becoming data-driven. But data transformation extends far beyond IT – and this siloed approach is steering organizations toward failure.
Although it’s true that data operations are highly technical and IT is crucial to their success, a successful data strategy requires input and effort from the entire organization – and those at the very top must lead by example. For the organization to become truly data-driven, everyone from sales to marketing to the C-suite needs their hands on the steering wheel.
Data Is Fueled by Technology and People
Enterprise data grew at a steady, moderate pace for much of the 20th century. That changed about a decade ago with a rapid acceleration in the volume, velocity, and veracity of data. In fact, the average organization sees its data volumes grow by 63% each month – and nearly six in 10 organizations say they can’t keep up.
As enterprise data threatens to spiral out of control, companies struggle to realize the full potential of their data operations. Instead of using data to derive actionable insights, spur innovation, and boost the bottom line, organizations are stuck sorting out data silos, swamps, security issues, and broken processes.
Within many organizations, immature data operations lead to a mess of spreadsheets, conflicting numbers, and unwieldy data governance practices. When these issues stack up, people feel like they can’t trust their organization’s data – or the teams who use it to make decisions. Suddenly, your data becomes a liability rather than an asset.
Advanced technologies can help teams solve many of these challenges and empower organizations to more effectively and efficiently manage their data. For example, data governance tools can understand data, label it appropriately, and apply policies to ensure quality standards, proper data usage and access, and compliance with regulatory requirements. Likewise, data quality software enables automated data profiling, cleansing, and enrichment that improves the accuracy, completeness, and consistency of data.
However, it takes more than just technology solutions for organizations to enhance their data practices and processes. It also takes people. The most mature data operations not only provide access to clean and high-quality data, but also maintain well-defined processes and KPIs for employees to understand how they can use data to make informed decisions – and why it’s important for every part of the organization to do so. The combination of technology and the right investment in employees can make the difference between data operations that fall flat and those that drive newfound efficiency and business value.
Rules of the Road Toward a More Data-Driven Business
Organizations that want to become data-driven need to break away from the “IT-only” mindset and embrace a more collaborative approach. It starts at the top – and executives must embody data-driven principles to cement data as a core business function.
But what does it look like for the C-suite to put real data behind the decisions they make and how can executives show their work and prove decision-making is data-driven to others? More importantly, how do organizations work with IT to provide clean, actionable data to every business unit, so they can make better decisions?
Three considerations as you reignite your data journey:
1. Take a shorter route to kickstart success: Instead of trying to tackle large-scale data projects that span your entire organization, it’s often more effective to start with smaller projects and build on your success over time. Identify specific areas where data can help, apply focused solutions, and share your success to demonstrate the value of data-driven decision-making and gain support for larger, more complex projects in the future.
This often takes the form of focusing on one department or business unit. Find an internal organization that is already interested in pursuing data-driven success and leverage its experience as a template to roll out these changes throughout your organization. Use this process to assess technologies, understand the changes in the business and organization, and serve as a training ground for internal resources to become experts in what it takes to become data-driven. Leveraging systems integrator partners in this first phase is a good way to help ensure its success and that the knowledge transfer is successful.
By breaking the journey down into smaller, more manageable pieces, you gradually build and refine your data capabilities, adjusting and improving as you roll out data initiatives across the enterprise. Starting small also enables teams to grow more comfortable applying it to their decision-making and better communicate and educate others in the organization.
2. Embody data-driven leadership: The most challenging aspect of implementing data-driven decision-making is organizational change. That’s why C-suite leaders and executives must lead by example and embrace a culture of data-driven decision-making from the top down.
For example, when addressing a group of employees, leaders should talk about how data informs decisions. All collaboration should be data-driven, with decisions based on the best data available. In addition, leaders should empower employees to make data-driven decisions by offering training and upskilling, access to high-quality data, and a clear understanding of the importance of data to the organization’s success. Leaders must understand that data-driven decision-making is not just a tagline, but a mission-critical practice that requires a fundamental change in the way decisions are made.
3. Take advantage of AI: Advancements in language model machines (LMMs) and generative AI offer new, exciting possibilities for data-driven organizations. These tools have the potential to transform the way organizations collect, analyze, and use data to make informed decisions. In particular, LMMs can be used to enhance the ease of access and understanding of data by layering data on top of language models, while generative AI can help automate and streamline your decision-making processes by generating new data-driven insights.
Embracing these emerging technologies enables your organization to extract insights from large and complex datasets more quickly and accurately, allowing teams to make better decisions and respond more quickly to changing market conditions. Additionally, LLMs and generative AI can help you identify improvements and innovation that may have been missed using traditional data analysis methods. Data-driven leaders who act early to incorporate LLMs and generative AI into their data strategies gain a competitive edge and will be better positioned for success in the future.
From improved decision-making to new opportunities for growth and innovation, enterprise data fuels every part of the modern business – and it takes every part of the organization working together to accelerate data initiatives. Most importantly, C-suite leaders need to recognize they are in the driver’s seat and have full control over their data strategy’s direction.
With the right technology and leadership in place, companies not only ready themselves for data initiatives today, but get on the road toward long-term success.