5 requirements to build strong data culture

You may not notice but nearly all businesses in the world are increasingly interested in investing more in data analysis, big data, and especially implementing projects. AI relates to its field of activity. Business leaders annually report that they are trying to transform their business organization model into a data-based culture. However, statistics show that only about a third of executives dare to claim that they have actually succeeded.

This is completely not too difficult to understand, because in fact all changes in enterprise size need time, as well as the determination and synchronization of the entire system, especially when the change culture is a complex effort, and the shift to a true cultural base based on data needs to have a long-term detailed plan and determination. Let's take a look at the 5 aspects that business executives need to be really mindful if they want to realize the process of transforming their system model into a culture based on real data.

1. What data has been collected from major technology companies from users?

Authorize

Business executives can begin to take solid steps on the path to a powerful data culture by empowering departments and development teams within the company. This not only helps businesses to seize opportunities but also catalysts for all changes. Many studies have shown that empowerment, or in other words, to share the power of employees has greatly contributed to deepening the responsibility of each individual in the work he or she undertakes, At the same time, it is also the main motivation in the issue of innovation thinking.

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1. Mix and combine in multicloud - the future of cloud computing

Empowerment here can be understood simply because employees are allowed to test their new ideas freely, despite failures many times - as long as they have to learn, draw experience. from mistakes to make a great contribution to the company in the future. A simple example can be mentioned as the case of Apple in conquering the smartphone market by giving itself a path, which is the development of iOS 'exclusive' operating system. In order to find success for iOS, Apple must capture how people interact with their services to make changes accordingly. In addition, such changes require the process of testing, analyzing, evaluating results, and repeating. Basically, this process means that in order to achieve success, you must accept defeat and learn from failure.

Environment

After empowerment will have to be the business environment. This is because an enterprise administrator can empower his groups of employees to innovate data, but if he cannot create an environment that includes transparency and giving allowing people to discuss and share organizational data on a periodic basis, the 'silly' efforts of administrators will quickly go to dead ends. 5 requirements to build strong data culture Picture 2

1. DDoS is ranked as the top threat for businesses in 2018

Building an environment conducive to data culture is not so complicated, focus on channels and update (updates). For example, on channels, you might be surprised at the performance of Slack dedicated to data. Basically, Slack is a 'chat room' for all employees in the company and since then, it encourages informal conversations and collaborations around new data and ideas. In terms of updates, sharing top and key data in team meetings can provide information as well as the vision needed to consider what should or should not be done. For example, if there is an increase in the Customer Satisfaction Score (CSAT) recorded last week, the business administrator as well as the relevant groups should discuss it during the meeting. weekly, review basic data and try to find a correlation with what made the change that week, determine whether it originated from a newly released feature or a marketing campaign. put into operation so that it can be maintained or changed in a reasonable way.

Tool

Of course an effective data conversion process will have to involve reviewing and using the tools carefully. The key to taking advantage of the tools to build a stronger data culture is to find tools so that it can be utilized by parts of the entire organization to draw deep insights. colors of their own, while helping to monitor product behavior more effectively.

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1. Why will marketing data director be a very important job in 2019?

Digital behavioral analysis tools such as Amplitude and Mixpanel can allow teams of employees to look at the behavior of online customers in the product or field they manage, from which to learn from and experience More precise strategies. For example: Insurance companies can use behavioral analysis tools to understand the inconsistencies and irrationalities in customer insurance management system as well as customer complaints submission system. . Determining the conflicting points in each process will help facilitate managers in empowering groups to optimize the goal completion rate, as well as finding potential customers. In addition, detailed information from these tools can also respond to product development cycles and help teams build new products and features with significantly improved efficiency.

Procedure

In fact, even if the digital tools are suitable, you can't go far without powerful processes to bind and link the appropriate parts together. When it comes to setting up data-based culture to continuously improve the user and product experience, creating processes that allow teams to see how products and behavioral data are tied With user research, such as surveys and interviews are essential. In other words, when a group discovers valuable insights in their analysis, they will be able to supplement that understanding by studying users to gain a more qualitative understanding of the process. is determined. Similarly, qualitative insights from user research or customer feedback can be verified and better understood through quantitative behavioral analyzes.

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1. OneDrive is the most used cloud storage service in the enterprise

For example, a telecommunications company may conduct surveys at the end of each service conversation with its customers, whether over the phone or conversational AI, in order to receive feedback on the guest experience. line. In this response, the company can accept comments like, 'The customer support part is annoying. I couldn't find the answer I was looking for '.

With such insight, the telecom company can dig deeper into the problem by using behavioral analysis data to identify processes related to users expressing frustration, and from That builds the analysis list. By identifying the conflicting points encountered for this user group, product and marketing departments can make changes that make the support more intuitive, experiment with many other versions. each other and even consider ways to present new information.

Data

Data is the last factor in creating a strong data culture. Groups must be organized around appropriate data to evaluate success and opportunity for improvement on an ongoing basis. They should evaluate their success on specific initiatives as well as overall customer satisfaction scores. Meanwhile, organizations must also arrange KPIs in accordance with revenue management issues as well as measures around customer value. For example, a travel company might want to create and align KPIs according to the data of satisfied travelers per week (WSTs), which represents the calculations of the number of bookings (revenue) in it. Tourists also rated the experience (value) positively.

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1. Do not work hard, work smart!

In general, if executives want their data-based cultural changes to be implemented successfully, they need to take a comprehensive approach, beyond the data tools themselves. These changes are not a simple effort, but in the current era of disruption, they are essential - and worth the effort.