Data Science Strategy: Memahami Change Management di Data Science

Understanding Change Management in Data Science In a study done by PricewaterhouseCoopers (PwC) and Iron Mountain, 1,800 senior business leaders in North America and Europe at midsize companies and enterprise-level organizations responded to a survey which showed that only a small percentage of the companies actually considered themselves to have effective data management practices. The study found that although 75 percent of business leaders from companies of all sizes, locations, and sectors feel that they are “making the most of their information assets,” in reality, only a minor portion seem to be strategically ­ approaching these major changes in the right way. Overall, as much as 43 percent of company leaders answered that they “obtain little tangible benefit from their information,” and 23 percent “derive no benefit whatsoever,” according to the study. So, what are companies doing wrong? One lesson to draw from the survey is that investing in the technology to become data driven is only the beginning. To ensure success, companies must do much more than focus on the tools needed to manage the data. Data science transfor- mation deals with sophisticated and interconnected data, small as well as big data sets, which impacts a whole range of business operations and has implications on people, cultures, organizations, processes, and skill sets in data science. The glue that connects and holds all these elements all together is the people. And the key is to get people motivated. This can be achieved in many ways, but using data to communicate relevant examples and proof points in combination with firm leadership is a good way to start. Strong leadership to drive the change includes not only the line management support but is also very much dependent on strong leaders and change drivers who can generate trust that the change will bring results. Without these dedicated change drivers across the company, it does not matter if you have the perfect plan — this type of totally transformative change will not happen, at least not to its full extent. In data science, the methods and techniques used for everything from knowing how to capture and process data to building models and deriving insights continue to evolve, creating a constant need to manage change. This change is also happening in areas such as regulatory practices, security, and privacy, continually altering the base and framework for how to approach data science. For a data science strategy to succeed, organizations need to understand and accept the fact that the skill sets needed to handle different aspects of data science will continue to change. To manage this continual change, you have to have an open mind and 52 PART 1 Optimizing Your Data Science Investmentbe willing to leverage and explore new technologies and methodologies as they become available. In practice, this means that individuals need to adopt a data-driven mindset and a commitment to lifelong learning as an extension of their work if they ever hope to manage change. Only when you actively use data to explore new avenues and solve real problems can you justify the data science investment. Defining a relevant and applicable process for change management should be a joint organizational effort, approached through brainstorming and idea refine- ment. Usually, agreeing that change is needed is easier than deciding how change should be approached.