5 Ways to Advance your Career as a Data-Driven Business Leader
Careers in data analysis are expected to rise in the next decade. As the U.S. Bureau of Labor Statistics reports, the job outlook for operations research analysts (the broad classification of data analysts) will increase by 25 percent, a rate well above average.
Whether you pursue a career as a data analyst or not, business leaders need to understand the fundamentals of data to be competitive in today’s environment. If the past few years have been any indication of what’s to come, data and analytics will continue to be critical in creating high-performing organizations.
Gerald Brown – a lecturer in process improvement and supply chain at Tufts Gordon Institute who teaches in the Business Analytics for Managers MasterTrack® Certificate offered through Coursera – says that modern data analytics means running more than just regressions.
“The organizations I work with need people who can move up the chain from data to information to knowledge to wisdom and explain their conclusions to people who think Python is just a scary snake,” Gerald said.
Here are five ways to stand out and push your career forward as a data-driven business leader:
1. Find out what kinds of data and metrics matter to stakeholders
Think about all the different functions that comprise an organization. From finance to marketing, legal, HR, and beyond, members of each functional area are coming at problems and challenges from their own perspectives, often tied to the work they do day-to-day.
This is where data is powerful. When used correctly, data can inspire stakeholders and rally support for a cause. Marketing might be concerned with changing consumer preferences, for example, whereas finance and accounting may be concerned with profitability. Data-driven business leaders drill down to find the relevant data-informed insights for each audience.
2. Reflect on past data to uncover trends
Data is in abundance. Yet, it’s often not looked at enough. Data-driven business leaders put in the work to uncover historical data and analyze it for trends and insights. Is there seasonality in demand for a consumer product? Did larger, socio-economic factors impact business performance? You may make new revelations that will shape the trajectory of your organization.
3. Provide the necessary context for your data to have meaning
Alone, data may have little-to-no meaning. Maybe a historical analysis of sales data suggests you need 55,000 units of a product to meet next year’s projected demand. But what if your organization is understaffed? What if the cost of goods is also expected to increase? Any analysis you do will need the proper context to be actionable and meaningful to stakeholders. Good leaders have data. The best leaders find out how it fits into the big picture.
4. Make sure your data is accessible and regularly updated
You and your colleagues are likely inundated with items competing for your attention all day. Be sure to regularly go over data-informed insights and make course corrections along the way. Pro tip: Explore dashboards or other tools that synthesize collected data. This will reduce the effort it takes to keep everyone updated and ensure the latest information is readily available.
5. Stay on top of the latest trends
Data-related skills are in high demand as each industry unlocks new possibilities with them. As the field becomes more and more popular, the latest insights and best practices are constantly changing. The data-driven business leaders who find the most career advancement proactively stay on top of what’s new.
Consider joining a professional organization. Network with other data-driven business leaders. Take courses like the aforementioned Tufts University Business Analytics for Managers MasterTrack® Certificate, designed by leading industry-expert Tufts faculty.
An instructor in the certificate, Kishore Pochampally, PhD, a Tufts Gordon Institute lecturer says, "Data is needed to make almost every decision in today’s business world, and knowledge of the quantitative techniques (kMeans clustering, kNN classification, Naive Bayes classification, regression, time series analysis, and linear programming) that you will learn in this program can open doors for you in the fast-growing field of data analytics/data science."