With so much complexity and change in the marketplace, organizations worldwide are leveraging opportunities to make better predictions, identify solutions and take strategic, proactive steps forward – which means that they increasingly depend on big data.

In their quest for organizational resilience, however, companies find that numbers aren’t necessarily the secret to success. People are.

More specifically, teams of people well-versed in cutting-edge analytics techniques offer organizations a smoother path forward. To that end, organizations should focus on securing top talent and building robust and high-performing analytics teams who can collaborate, innovate, and drive improvement and better decision-making. But where do they start? Here are a few top tips to consider.

1. Look for and encourage lifelong learning

With the current speed of change, today's top tools may be old news tomorrow, meaning that organizations should prioritize what people know and how they know it. This is not to say that specific hard skills and certifications aren’t valuable. They are. However, these can date themselves quickly – as new models, packages, languages and applications come out regularly. For this reason, organizations must prioritize talent who commit to lifelong learning.

This also means that current analytics professionals need time baked into their roles to research, learn and explore new things. To stay relevant, they need to commit to learning throughout their career and learning new skills each year. This doesn’t require much time – just 5-10% – but it returns excellent benefits to the company. On the flip side, organizations that don’t support and encourage this kind of continuous learning risk their analytics capabilities becoming outdated.

2. Conduct scouting missions

For these same reasons, organizations should build more formal mechanisms for investing and reviewing new capabilities. While it’s important to encourage continuous learning and carve out time for employees to learn new skills independently, it takes more than this to stay relevant. The nature of agile and continuous integration and continuous delivery (CI/CD) methodologies is such that new capabilities are being introduced at never-before-seen rates – which means that analytics teams must devote time to looking forward.

And for most organizations, it’s unrealistic to expect head-down employees to simultaneously have their eyes on the horizon for what’s coming – especially considering global staffing shortages and a lack of talent. Having scouting missions to investigate and review new capabilities allows analytics teams to quickly identify new features to address complex challenges and enhance current implementations.

3. Put the right tools in the toolbox

To maximize the expertise and talent of their analysts, organizations need to offer them the best tools available. Without the right tools, analysts will be inundated with tedious, time-consuming tasks that take them away from doing their best work.

So, to get the most value out of analysts – and to demonstrate their value to the organization in a way that improves morale and encourages them to stay – make sure they have what they need. Ensure that your organization invests in enterprise-quality software designed to take the hassle out of data science and can free time up for analysts to deliver valuable insights.

4. Be wary of self-interests influencing strategic direction

While offering high-quality tools to analysts is important, investing in the right ones is equally important. With the proliferation of new, shiny tools and techniques, it’s an instinct for organizations to defer to analysts – especially those with strong opinions. And, though it’s essential to trust employees and rely on their expertise, organizations should be careful not to allow a select few people to drive initiatives motivated by personal comfort or preference –  as some decisions, such as tool or vendor selection, can have enterprise implications.

Organizations should first prepare a clearly defined set of objectives, regulatory requirements and desired outcomes to avoid short-sighted or counterproductive decisions. These can serve as a litmus test when new technologies and approaches are considered to help organizations make wise, strategic investments and decisions.

5. Cultivate a collaborative work culture

Again, strong analytics teams – not just individuals – are the secret to organizational success. Time and time again, organizations find that employees do their best work when they work together, and this is especially true for data professionals. Analysts thrive in a quickly evolving field when they have opportunities to learn from one another.

So, take a long, hard look at organizational culture. Are managers creating unhealthy competition among employees? This creates environments where employees keep their best ideas to themselves – rather than providing spaces for employees to collaborate and grow. To foster a positive, collaborative atmosphere, managers should encourage open communication and actively promote a culture of trust. This can be achieved by regularly holding team meetings and encouraging employees to share their ideas, thoughts and concerns.

Additionally, managers should provide opportunities for employees to work together on projects and celebrate their collaboration. Fostering a knowledge-sharing culture can create an environment that promotes innovation and creativity, increasing the organization's morale, productivity and profitability.

Learn more about analytics training programs from SAS to keep your team's skills sharp – and your competitive edge sharper.

James Waite and Catherine Truxillo made contributions to this article


About Author

Catherine (Cat) Truxillo

Director of Analytical Education, SAS

Catherine Truxillo, Ph.D. has written or co-written SAS training courses for advanced statistical methods, including: multivariate statistics, linear and generalized linear mixed models, multilevel models, structural equation models, imputation methods for missing data, statistical process control, design and analysis of experiments, and cluster analysis. She also teaches courses on leadership and communication in data science.

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