Why Data Matters?

Lots of data, competitive pressure, and many opportunities in data-based enterprise applications are forcing companies to invest heavily in data science initiatives. With so many well-known software vendors and IT professionals in the market, change doesn't have to be complicated, does it?

Unfortunately, for many ambitious business leaders, the primary problem is data-driven change when it comes to culture, not technology. According to NewVantage Partners' Big Data and AI Executive Survey 2020, only 26.8% of organizations surveyed say they are successful in building a data culture.

In most cases, employees are reluctant to change their mindset and adapt to a different workflow. Establishing a strong digital culture and changing organizational processes is what companies need to focus on when it comes to data-based change.

Let's look at the four steps organizations can take to create a data-based culture.

Start from the top

First, the role of the CEO in any change cannot be overestimated. If the Director-General is not fully immersed in the idea of placing data at the center, all measures taken to facilitate cultural transmission are likely to fail. For every employee, the CEO is an indicator of how important, real and valuable "all that nonsense" is. If the CEO doesn't understand the potential of data science, a combination of lessons and learning will help. Under the leadership of the Chief Data Officer or even a specialist named from the left, top management training is a necessary step towards smooth cultural change.

The C-suite must then manage internal information campaigns. It is important for business leaders to tailor their approach and attitude to communicating ideas to their employees, setting the value of data processes and bringing data to the forefront during meetings and presentations. However, it is important to avoid ambiguity when evaluating data. Thoughts about the data passed on by managers should be easy to remember and understand even the smallest technically proficient staff.

Manage educational programs wisely

As you would expect, training programs should be at the heart of data-driven change. The often overlooked lack of a relevant training program may be the only reason why employees lose contact. Without the relevant knowledge and skills, they cannot understand exactly why change is happening and, most importantly, what opportunities it offers. When done properly, training programs are a distractor and a builder of confidence. The most important thing here is to keep in mind that these programs should be as attractive as possible. "Learning to do" has proven to be the shortest way to improve on the often complex concepts of data science. With this approach, each department can create training programs that are defined by the current skills of their employees and exactly how these people can use the data to their advantage.

For example, project managers may be tasked with creating a problem that can be solved through data analysis. Instead, the marketing team can develop strategies based on data analysis findings in their specific area. By conducting such simulation exercises, each department will gain hands-on experience and better understand how data can improve decision-making in its domain.

Another decisive factor in the success of educational programs is their time. For example, the basic skills needed to understand data-based decision-making, such as self-service BI requirements, can be acquired before a major change is made. In-depth analysis and decision-making training should capture the space of all employees before these tasks become critical to the company's operations.

Democracy is data

The basic requirement that data carries value is availability. In a McKinsey report, "Why Data Culture Matters," many leading C-level executives in established companies have similar experiences with data democratization - faster data access, more confidence, and employee enthusiasm. Without universal access to data within an organization, data loses much of its potential.

However, in the context of change, providing access to data for everyone in the business can be a difficult task because it requires data reorganization, which is a constantly tedious and slow process. To overcome this problem, companies can access specific datasets with the most appropriate metrics that match current goals.

Another important aspect of data democratization is the focus on removing communication barriers between data scientists and business executives. Sokol's common remark in many business structures is that their data science departments are isolated from the decision-making center. Although I do not want to end the endless debate about 'centralization vs. decentralization', hybrid models are better off in this context.

Make the data normal

In fact, any cultural shift is a matter of behavior change. In many cases, employee behavior is defined by their environment and the tools they use. Therefore, it is important for the organization to remove warnings that relate to old decision-making patterns. As mentioned, information sharing and collaboration are essential to successful data-driven change.

It is therefore logical to reorganize existing offices in a way that supports networking. Finally, any tool or template that supports old decision making and is at odds with the data-based approach should be reviewed and removed or modified. New KPIs should also be set wisely. At this stage, it is important not only to examine how positive the results of data-based projects are, but also to reward all ongoing data-based actions and initiatives.

Lots of data, competitive pressure, and many opportunities in data-based enterprise applications are forcing companies to invest heavily in data science initiatives. With so many well-known software vendors and IT professionals in the market, change doesn't have to be complicated, does it?

Unfortunately, for many ambitious business leaders, the primary problem is data-driven change when it comes to culture, not technology. According to NewVantage Partners' Big Data and AI Executive Survey 2020, only 26.8% of organizations surveyed say they are successful in building a data culture.

In most cases, employees are reluctant to change their mindset and adapt to a different workflow. Establishing a strong digital culture and changing organizational processes is what companies need to focus on when it comes to data-based change.