Today’s data evangelists spread the good news about how data can positively affect businesses and how important it is to incorporate data into the decision-making process. Well, that sounds great, but what does that mean, and how does that translate into action? What is data-driven decision-making? What does a company do if they're interested in becoming a data-driven business?
This article explores data-driven decision-making, including concepts like design thinking, data sharing, best practices, and tips for becoming an effective data-driven business.
Data Informs, But How Does That Make Managers Better at Decision Making?
We know that, on a personal level, the more information we have when faced with a decision, the better that decision will be. This also applies to larger-scale situations such as business strategies and plans. Many businesses have discovered that the greater the access to reliable, relevant data, the greater the chance of creating a successful program and making the best decisions. If managers have curated data, they can make informed data-driven decisions to mitigate risks and realize the ROI they are trying to achieve.
Merging Soft Skills With Data Analytics
“Soft” skills are those unquantifiable, fluid skills that are valuable in the workplace, academia, or even in interpersonal relationships. They include traits like:
- Conflict management
- Critical thinking
- Stress management
- Time management
So how do we combine the above soft skills with essential data analytics tools? Computational leadership science (CLS) is one possible way. CLS is the next logical step from computational social science, which uses data science tools and data processing to analyze data about people and relationships.
CLS uses those computational social science tools to improve leadership via network analysis, Artificial Intelligence, simulations, and other digital approaches. First, however, managers and other company leaders must set an example by embracing the technology and applying it to daily operations, championing the new data culture.
Many soft skills come from what can best be described as gut feelings, instincts, and natural tendencies. Leaders can use the actionable information derived from data analytics to bolster and add more weight to those soft skills. For instance, a manager with good communication skills would play a valuable role in disseminating information to the appropriate teams or individuals.
Additionally, a manager who excels at solving problems can turn to the reliable, curated information that analytics provides and acquire a greater understanding of the issue and what resources they can tap to solve the problem.
But this is not limited to just the leaders and managers. The people in charge can foster a more significant partnership between soft skills and data by recognizing and rewarding team members who effectively use data. This collaboration improves morale and performance among the team and shows how valuable data and analytics are in today's competitive business world.
Managers can also initiate programs (e.g., open houses, forums, and educational initiatives) to show people how to fuse data analytics and soft skills to create teams with better market-building capability. And speaking of teams, effective managers should form cross-functional teams that bring together people with different skills and backgrounds (business, data analytics, computer science) to encourage innovative thinking and diversity.
Models and Best Practices of a Data-Driven Decision Maker
Let’s look at seven of the best attributes of an effective decision-maker in today’s digital world.
- Creating and fostering a data culture: Decision-makers should give data teams the latitude and room they need to be creative as they process and analyze massive amounts of data. This practice means recruiting team members with skills and different perspectives.
- Combining business and science: These leaders can integrate science and business; this includes merging new kinds of data analytics with existing systems and business structures.
- Moving beyond established organizational limits: The key to success for today's data-oriented projects is encouraging collaboration between different groups. As a result, data is no longer the sole province of the company's IT people.
- Respecting and understanding data’s game changing potential: A business can’t exist without its customers, and good leaders know that service is not the goal, but merely a means to achieve the plan, providing customers with an excellent experience.
- Expediting data speed: A good data leader helps data move quickly and efficiently around the business. Decision-makers must ensure that information is secure, easily accessible, and of the highest quality.
- Having guts: Heavy competition, economic uncertainty, and the flood of information available today give businesses the temptation to become risk-averse. However, the practical decision-maker needs to have the nerve and not be reluctant to launch head-first into Big Data.
- Knowing the right questions to ask: Okay, Big Data has the answers to your questions, but there's an art to knowing which questions to ask. Decision-makers need clarity and focus so they can identify the appropriate questions to ask.
Can’t-Miss Tips for Becoming an Effective Data-Driven Manager
Now that we've established what constitutes best practices for decision-makers, here are some tips to help leaders become effective data-oriented managers.
- Encourage, enable, and support upskilling efforts among your employees: Unfortunately, not everyone in the organization has the same level of data literacy. Leaders can make a more learning-friendly environment by creating learning opportunities, democratizing data access, and ensuring that employees have the time to take advantage of the upskilling opportunities.
- Make sure that data gets embedded in all sorts of decision-making: Good data-oriented managers can’t just talk a good game; they must show how they place data everywhere in the organization. A good leader clarifies the organization’s purposes and outcomes and how the data culture encourages success.
- Show everyone that it’s in their best interests to embrace change: It's easy for people to recite the "but this is how we've ALWAYS done this!" mantra. Instead, use the ADKAR model, which embraces the idea that organizations don’t change; people do. ADKAR is an acronym representing the five elements necessary for individuals to change:
- Awareness that change is needed.
- Desire to be a part of and support the change.
- Knowledge of how to put the changes into practice.
- Ability to implement the behaviors and skills needed/wanted for the changes.
- Reinforcement to keep the changes in place.
- Offer DIY opportunities to your employees. Ensure your employees have access to quality, curated data presented in a form they can easily understand and use. You can foster these do-it-yourself opportunities with a suitable data governance platform and self-service data insight tools.
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