Analytical data is abundant in modern companies. Customer information, marketing results, competitors’ price changes and innovations—the amount and diversity of data businesses accumulate enable them to make well-informed decisions and generate incredibly accurate predictions. In fact, failing to leverage big data in 2021 threatens to leave companies lagging behind rapidly growing startups in their industries.
Unlike traditional businesses, a data-driven organization relies on big data to guide its activity. While it presents a significant change in familiar procedures, promoting data-driven culture will help your employees develop new approaches to their duties and embrace advancing technologies faster.
The Benefits of leveraging Big Data
Before we get down to specifics, let’s take a brief look at the overall impact of data on an average company. The key benefits of data-driven decision making for businesses in numerous industries include these opportunities:
- design new core products;
- extend your already existing business;
- predict the demand for a particular product line;
- use dynamic pricing based on clients’ profiles and competitors’ prices;
- study customer behaviors and personalize marketing;
- improve your targeting;
- identify and prevent fraud and misconduct;
- automate minor tasks for employees.
Which industries use data-driven culture?
Manufacturing
Since quality and performance monitoring is a data-driven process, the manufacturing industry can benefit from preventive maintenance, control over storage conditions, product quality control, and reduced costs due to better stocking and route optimization.
Energy
A data-driven decision in the energy industry can be incredibly effective at meeting the projected energy needs for a specific purpose. Using big data can also help various organizations detect energy leaks and thus optimize their energy consumption, enable preventive maintenance of the system, and cut down repair costs.
Banking
Data-driven applications in banking help older organizations compete with young fintech startups. These solutions mainly aim to digitalize banking services, provide better security, employ predictive analytics to determine future demand, identify suspicious user activity, and examine a customer’s credit history.
Healthcare
In healthcare, data-driven culture enables professionals to provide more accessible and high-quality care remotely as well as in hospitals. A data-driven approach helps assess the condition of each patient, predict the progression of certain diseases, find the best treatment options, and keep patient information conveniently organized.
How to establish a data-driven culture?
Building a data-driven business is not just about using a specific kind of software; it’s a complex shift in operations that can be difficult to embrace. In the next seven tips, we’ll take a look at how you can weave data culture into your company gradually and effectively.
1. Ensure transparent communication
The first step to take when growing a new sophisticated data ecosystem at your company is to communicate the intentions to all employees. Most of the time, significant changes in operations seem overwhelming. By revealing not just the new demands but your vision and expectations of such innovations, you encourage employees to directly participate in improving the company’s services, internal processes, and working conditions.
Another crucial step is to start practicing what you preach. Make sure that higher executives at your company lead by example and habitually rely on concrete data during meetings and discussions. When the transformation starts at the top, employees find it easier to follow suit and adopt the data-driven approach, since it allows them to bring more valuable contributions and keep up with their supervisors.
2. Fuse analytics and operations
One of the most persistent challenges that corporate analytics faces today is a myriad of gaps between departments. Companies have data scattered across databases, and analytics teams frequently struggle to fish out the correct information or context for their work. This, in turn, makes it difficult to derive valuable forecasts and actionable insights.
Breaking your data science team out of this isolation may be one of the most important steps in data analysis at your organization. One way to accomplish it suggests rotating employees or creating new roles within respective departments. It allows the analytics team to work closely with developers, sales personnel, and marketers to get a better grip on the company’s products and business goals, and it also gives data scientists access to the exact information they need.
Another solution is to gather the most critical data for each department and structure it into separate layers, giving your analysts access to what their current projects demand. This way, you can slowly reorganize your databases to be more convenient but avoid stalling the company’s activity in the process.
3. Start with simple but working innovations
Data-driven development is an area where slow improvement can go a long way towards securing a smooth integration process. Start with basic but functional prototypes when you try out new ideas. Focus on how cost-efficient they are and whether they’re viable to maintain. Once you are confident the idea is worthy of further development, you can begin scaling the system up and adding new pieces of functionality and complexity.
4. Use performance metrics
Business metrics for data-driven companies are a vital instrument of performance control. After identifying what data to use for each specific objective and how to leverage it, an enterprise needs to evaluate the output of this data processing. By designing or using established metrics, you can improve analytical mechanisms, reach better results, and inspire employees by demonstrating the quantitative products of their efforts.
5. Focus on consistency
Although analytical metrics and other tools are supposed to enhance companies, the lack of consistency can render them not merely useless but harmful. Strive to keep your tech stack and policies even across the organization. This way, all models and software will be in agreement, and your employees will be able to speak the same language.
You may also discover that adding new programming languages and customizations to the company’s toolkit is easier when you already have a stable system with clear standards.
6. Create new training programs
Corporate education should tackle several challenges and insecurities employees always face during major company transformations. Namely, your training programs should
- teach employees to operate new big data and analytics solutions;
- coach them on what a data-driven culture is, how to approach data-driven decision making, and what benefits this paradigm has in the long run;
- include basic coding skills in the fundamental training; and
- deliver more specific niche courses only when they are needed for a particular project or task; this way, memories won’t fade with the lack of use.
7. Highlight the benefits for employees
Adopting data driven technology is hard, and it’s important to let your staff know not only what customer benefits the transformation offers but how it can aid employees. Emphasize that a data-driven automation framework can help complete work faster, avoid reworking, organize and fetch data more effectively, and automate minor but time-consuming tasks.
Big data is a habit
Data-driven statistics is one part of an advanced toolkit that businesses in 2021 can use to get an edge on their markets. Overall, leveraging data is a comprehensive and all-encompassing endeavor that requires significant transformations in the functioning and culture of enterprises.
Considering the continuous pressure to stay on top and the associated difficulties of allocating resources to innovations, it’s essential for businesses to gradually make data-driven decision making and make it second nature to all employees.
