Design, deliver, and run enterprise blockchain workloads quickly and easily.
Five things CIOs must consider about data for a successful digital transformation
CIOs charged with managing digitization projects need to discover how to leverage their company's wealth of data. You want to accelerate business objectives and advance competitiveness at lower costs—but don't you always?
Want to make a big change in your organization? Focus on the data. Digital transformation needs the right data at the right time. That means CIOs leading such efforts need people who have expertise in quantitative digital thinking. These experts need to be guided to understand the measurements: what, where, and when-to. And with that understanding, they need to design data schemes that support new initiatives at the right times.
Here are five issues CIOs must address to achieve a successful storage transformation.
1. Dive into the data
Data may be readily available, but that doesn’t mean you can easily put it to use in pursuit of new initiatives. That kind of effort requires skills beyond those of most IT practitioners. In other words, yes, you need to hire more people.
Data scientists are part of the equation. They have the skills necessary for managing and exploring data to meet the needs of new initiatives. To bring data into the mix of tools used for digital transformation, this is a good starting point. But an additional range of expertise is needed to develop a full plan that goes beyond exploring existing data. You also need to position the transformation for business growth and the ability to exploit new opportunities.
2. Develop convincing narratives
Digital effectiveness skill sets include deep understanding of how data can be used and narrative context. The story needs quantitative digital thinking to explain the message using data as the underlying evidence supporting the story line and its conclusions. Sanofi Chief Data Officer Milind Kamkolkar explained his perspective on storytelling at the MIT Chief Data Officer and Information Quality Symposium, noting, “Key aspects of data analytics—such as consumption data and the actual utility and actionability of data insights—are best served by 'data journalism.'”
In other words, find someone who can turn data sets into stories.
Additionally, the story needs to be more than fact-based explanations of what the data reveals. It needs to be built on the established facts, and it has to make use of the storyteller’s abilities to imagine future states of the business that don’t yet exist but could be created under certain circumstances. The bridge between the prediction and a future reality is based on the data supporting the digital transformation, and the story must include enough realistic information about the data required to achieve that future state. Storytelling data scientists need to have the journalistic propensity for pursuing unanswered questions and proposing previously unimagined answers. But they also need to possess the skills of the data scientist to temper a free imagination so their vision remains realistic.
3. Consider all of the data
The volume of accumulated data is increasing as we add new devices and applications. As data usage expands, enterprises are finding new ways to use the data sets. But even with advanced analytical tools, only a small percentage of data is being used beyond its original intent. The Guardian reports that “just 3 percent of all data is currently tagged and ready for manipulation, and only one-sixth of this—0.5 percent—is used for analysis.” While a good way to get started is to revisit data that the users are comfortable with, it’s the CIO's job to explore unused datastores and bridge the various siloed data using advanced tools to make digital associations among seemingly unrelated sets of information.
4. Design for transformation
Once the digital storyteller creates a realistic vision, the next step is to create a data design that supports the plan's transition and execution. This stage focuses on building the design of the data to be collected, planning how to use existing sets of data, and designing an appropriate user experience (UX) that drives the collection of data in ways that facilitate analysis. The CIO needs to enlist another set of skills to accomplish this part of the mission: a UX designer who can translate the story about the future of the business into both a set of data and the applications that collect and collate that data.
5. Evaluate and revise
Digital data transformation is an iterative process. Analyze the usefulness of the various pools of data and the front-end tools (applications). Review the design to assess what data is collected and what data is missing. Look at the UX to understand how users are interacting with the results. What draws their attention? It's your team's aim to ensure the data collection and interpretation meets their needs. The data narrative author and the UX designer should work together closely, with the CIO cheering them on.
Digital transformation is complex, requiring changes in data, storage, and acquisition. CIOs who take a long, detailed view of the process can deliver successful change to their enterprise. A clearly defined path, accurate data analysis, and the understanding that the transformation is a collaborative process provides the best results.
This article/content was written by the individual writer identified and does not necessarily reflect the view of Hewlett Packard Enterprise Company.