8 Steps to Becoming a Data-Driven Organization

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8 Steps to Becoming a Data-Driven Organization

Leadership needs to embrace the fact that bringing change within a company is a challenging and often long process. That's why it's worthwhile to follow this framework.

· Big Data Zone ·
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In today's fast-paced global economy, it is generally understood that companies must become data-driven in order to remain competitive. In fact, a report from McKinsey Global Institute indicates companies that are data-driven — meaning those that can gather, process, and analyze data in real-time as it flows through the enterprise — make better decisions. According to the report, being data-driven results in a 23x greater likelihood of customer acquisition, six times greater likelihood of customer retention, and a 19x greater likelihood of profitability.

But how exactly does a company become data-driven? There are, in fact, many ways to respond to this question. One might consider the use of technology or having a solid strategy for data quality, governance, and access, but perhaps the most important factor in becoming data-driven is having the right leadership to create a culture that places data at the heart of the organization.

Change management is defined by CIO Magazine as "a systematic approach to dealing with change both from the perspective of an organization and the individual." Instituting a culture around putting data at the core of every business function can be very useful in helping your employees and company understand and believe in the importance and use of data to make more informed decisions.

Many of the senior IT leaders at my previous companies used the Kotter change management model developed by John P. Kotter, a widely-recognized authority on leadership and change, to bring about change in their organizations. There are eight phases in Kotter's model and I want to detail how organizations can apply each to become more data-driven themselves.

1. Create a Sense of Urgency

This first, and perhaps most important, phase in Kotter's change management model can be used to demonstrate the benefits of becoming data-driven. The aim of this activity is to demonstrate to stakeholders how the company can use data for greater insights, underscoring some of the opportunity costs of not using data efficiently. This phase can be driven by a team of change agents comprised of senior execs who can tackle some of the organization's most important issues by using data. A simple business intelligence report showing some interesting insights around the various ways that customer service impacts sales could serve as an excellent example. Similarly, running an analysis showing where competitors are in their data journey on the maturity models and comparing that with the organization's own status in its data-driven transformation can also underscore the importance of data.

2. Build a Guiding Coalition

In this phase, the change agent needs a commitment from other leaders in the organization to drive the interest generated during phase one of the process. The bigger the change and the company, the more senior leaders that are needed as part of the change team. While the change agent will need buy-in from business team senior leaders, IT will ultimately need to execute this portion of the strategy. Ideally, the members of these teams are drawn from the departments that will realize the greatest benefit from a data-driven strategy. For example, in a huge consumer products company, having leadership from the supply chain as part of the coalition would be a great win for the change agent.

3. Develop a Vision and Strategy

It's important to define a forward-looking data-driven strategy using clear and concise language. This vision creates a platform that helps the organization work toward a common goal and the framework against which it can begin to develop a strategy. When properly developed and presented, the strategy can help bolster credibility and encourage more leaders to join the coalition, particularly those with any doubts regarding the mission. A typical strategy would use a phased approach, with interim goals and milestones defined. One example of creating a data drive strategy is to create a data center of excellence. This team would handle all data governance, data integration, and analytics initiatives.

4. Communicate the Vision

The vision needs to be communicated continuously and consistently across the organization. Everyone in the organization, from business users to IT developers, needs to understand why the change is happening, what impact it will have on their teams and where are they going with it. If employees understand and believe in the vision, the organization as a whole will be motivated and will strive for the change, ultimately creating more change agents.

5. Empowerment

As a data-driven culture starts to develop within the organization, it is important to identify and tackle any obstacles head on. Leadership needs to ensure the right processes are in place that allow employees to raise concerns about how data is being leveraged and ensure those concerns are reviewed, considered, and addressed if changes do indeed need to be made. This activity also involves giving business workers the right tools that allow IT maintain governance and security over the data, such as self-service data preparation tools.

6. Generate Short-Term Wins

In this phase, a company's cultural change is maintained by savoring the success of winning projects and recognizing the efforts that led to that success. At the outset, a company can identify low-hanging fruit - projects that can be executed without much initial investment. These projects avoid the need for significant upfront monetary or staffing resources and provide the added benefit of shorter life cycles, allowing project managers to more easily define the specific goals and objectives that can help maintain momentum and foster a sense of accomplishment. As these projects become successful, leaders need to keep looking at other opportunities to make data an asset to the company.

7. Keep Track of Lessons Learned and Keep Looking Ahead

By keeping long-term goals in mind and using some of the lessons learned from short-term projects, a company can start implementing some longer-term projects. For example, a company can invest in building a data warehouse and business intelligence (BI) capabilities within the company. A company can also invest in big data technologies and create a data science team. IT will also need to take an even more proactive role in the change management process by helping every employee within the organization understand the value of data quality and data governance. The company should also create more change agents by showcasing successful data-driven projects to other company departments.

8. Institutionalize Change

Clear communication can play a central role in demonstrating how the data-driven changes are directly related to performance improvements of the company. Leaders that champion the change should be placed in roles that allow them to drive the overall data enablement vision, such as a chief data officer (CDO), while other leaders take up new roles and responsibilities. The final stage in a data-driven transformation occurs when the changes become ingrained in the corporate the culture and that shift can then serve as the final platform on which the company can sustain the change.

Leadership needs to embrace the fact that bringing about change within a company is a challenging and often long process. And that's the reason it is worthwhile to follow a framework such as the one outlined above, which suggests a phased approach and gives an opportunity to correct errors along the way and thereby realize the full potential of embracing big data. 

big data, data-driven, roi

Published at DZone with permission of Nitin Kudikala , DZone MVB. See the original article here.

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