Action Guide for Building a Digital Operation System

The construction of a data-based operation system is a systematic value project that concerns the survival and development of the enterprise. How to build a data-driven operation system? This article introduces the concept, value and positioning, methodology, path selection and action guide of the data interoperation system, and teaches you how to start a new journey of digital transformation for your enterprise.

Previously, on the topic of data-driven operations, we introduced in detail how to use data to drive the operations of products, users, content, activities, and services. It included theoretical analysis and case descriptions, and the content was relatively comprehensive.

However, for enterprises, how to start the journey of digital operations? Just like traveling, you need to plan your itinerary in advance about which places to go, which places not to go, and which stop to go to first.

Therefore, for enterprises, how to build a data-based operation system that suits their own situation requires a navigation map or an action guide. Today, the topic we are discussing is: To build a data-driven operation system, what kind of action route navigation map does an enterprise need?

Similar to before, I start with the basic concepts. I will first explain what I understand as a data-driven operation system, then analyze why a data-driven operation system is needed, and then provide a methodological framework and action guide for building a data-driven operation system.

1. My understanding of the data-based operation system

Personally, I think that a data-based operation system refers to a set of thinking patterns, technical routes, and action patterns that use data means and methods to structure and systematically solve various problems in business operation scenarios.

It includes four aspects: data system, operation system, method system and organizational system.

Personally, I understand that the reason why the data-driven operation system is composed of four parts: data, operation, methods and organization is determined by their respective responsibilities.

The data system solves the problem of data foundation. The operation system guides us on what to do, the method system provides us with the technology and methods needed to do these things, and the organizational system tells us how to equip the corresponding professionals to complete these things.

Data, operations, methods and organization are integrated into one, forming a data-driven operations system that supports the digital transformation of enterprises.

2. Positioning and value of the data-based operation system

For enterprises, the positioning of the data-based operation system can be summarized as: the command system for enterprise operations and the backbone of smart operations.

As the command system for enterprise operations, the data-driven operations system can comprehensively manage the application of data in product optimization, user growth and other aspects. We plan strategically and win battles from afar, guiding enterprises to optimize and improve their products, content, etc.

As the backbone of smart operations, the data-driven operations system can ensure the implementation of applications in terms of human resources, technical platforms, etc., and is the core force in promoting the intelligent upgrade of operations.

The data-based operation system is the concentrated embodiment of the digital transformation of enterprises. Its value in the process of enterprise transformation is reflected in four aspects:

1. Unlocking the value of data

The data-driven operation system integrates data into scenarios such as products, users, and activities, and uses data as the key to solve existing problems in these scenarios. These problems actually form a channel for releasing the value of data.

2. Make decision-making more scientific

The data-driven operation system has completely changed the operational decision-making model of enterprises, so that enterprises no longer rely on guesswork, experience or brainstorming to make operational decisions; instead, they analyze problems and output decisions through data measurement, verification and analysis. Such decisions are scientific and reliable and can stand the test of practice.

3. Make decision-making more efficient

With the help of data technology, the company's decision-making process can be shortened, allowing the company to accurately predict the market situation and quickly adjust its marketing strategy.

Data can help companies seize business opportunities and respond to user needs more flexibly and quickly, allowing companies to gain a first-mover advantage in the fierce market competition.

4. Make operations smarter

The application of data in areas such as user operation and product operation is the highlight of the data-based operation system. Under the catalytic effect of data, product operation, user operation, etc. will become more "smart".

Data-driven operations actually mean that data equips operations with an intelligent brain. With a higher IQ, the operations of the enterprise will be more intelligent.

3. Methodology for building a digital operation system

Regarding the construction of a data-driven operation system, the author summarizes it as a "four-dimensional cultivation method", that is, starting from the four dimensions of data, operation, methods and organization, and gradually improving the capability map of the data-driven operation system.

1. Data system: solving the problem of data itself

With the goal of building a data middle platform, completing the "collection, storage, communication, management and use" of data is the technical foundation of the data-based operation system.

The construction of a data middle platform is itself a complex systematic project, but it can still be built according to the thinking and routines of product development.

As shown in the following figure, the "five-step method" can be used to develop the data center:

Of course, not all companies are suitable for building a data middle platform. The data middle platform is the technical foundation for the digital transformation of enterprises. However, it is recommended that companies that are not suitable for building a data middle platform in the short term consider building a DMP (data management platform).

The reason why the data middle platform is described separately is to use leading companies in the industry with more business lines as typical cases, so as to facilitate a comprehensive introduction of their own data-based operation systems.

The subsequent operation system, method system, and organizational system are also explained based on the premise that the enterprise "needs to build a data middle platform."

2. Operation system: answering the question of “what to do”

Driven by data and driven by business needs, we combined the problems in five major operational scenarios, such as product operations and user operations, to sort out specific work tasks; calculate the workload of each task; formulate detailed time plans and action plans, and draw up a task map.

Design monitoring indicators based on task progress, use task dashboards to monitor the status and implementation progress of related matters, and set early warning rules to adjust task development strategies in a timely manner.

3. Methodology: Answering the question of “how to do it”

On the basis of the task map, we further refine the theoretical basis and technical methods required for each matter, and gradually improve the operational specifications to form the best operational practices and accumulate them in the business knowledge base.

4. Organizational system: answering the question of “who will do it?”

Build a suitable organizational structure based on the actual staffing situation of the enterprise and the requirements of the data-based operation system.

The personnel system generally includes three major parts, namely the data product and application team oriented towards operations, the team oriented towards the construction of the enterprise data middle platform, and the platform technology support team.

The data product and application teams need to establish a joint working mechanism with relevant operation staff in the fields of user operation, product operation, content operation, etc.

4. Path selection and action guide for building an enterprise data-based operation system

1. Basic principles to be followed in building a data-based operation system

When building a data-based operation system, enterprises are advised to follow three principles:

1. Adapt to local conditions: Based on the company's own situation, tailor-make appropriate strategies and output data-based operation system construction plans that are in line with the current situation. Do not seek too much, but do what you can;
2. Internal and external integration: When diagnosing problems or identifying shortcomings, look at both the internal (front-end + back-end) and external (users + benchmarking) perspectives, conduct comprehensive analysis from multiple angles, and identify key issues;
3. Value-oriented: Starting from the ultimate business value, consider the construction path of the data-based operation system in reverse. When formulating specific action plans, tasks with greater value have higher priority.

2. Path selection and action guide for building a data-based operation system

Based on the two dimensions of data infrastructure capabilities and the urgency of operational issues, a two-dimensional analysis matrix is ​​established to divide enterprises into four types, scattered in the corresponding four quadrants.

Enterprises can develop more specific and feasible action plans based on their quadrant position.

1) Type 1 companies: strong data foundation and less pressing operational issues

Using advanced companies as benchmarks, analyze the gaps between us and benchmark companies in terms of data middle platform and operations. Identify the top 10 most prominent data and operations issues, evaluate improvement priorities, develop improvement strategies and plans, and advance in an orderly manner.

2) Type 2 companies: weak data foundation and less pressing operational issues

Priority will be given to filling gaps in data capabilities, with an emphasis on addressing the top 10 data issues. In the process of solving data problems, we should appropriately combine the key points that operations work should focus on improving, focusing on improving data capabilities and supplemented by operational optimization.

3) The third type of enterprise: weak data foundation and urgent operational issues

Prioritize solving the top 10 problems in operational work and appropriately fill in the gaps in data capabilities. The focus is on operational optimization, supplemented by improving data capabilities.

4) The fourth type of enterprise: strong data foundation, urgent operational issues

Sort out the TOP10 problems in operational work and sort out the TOP5 problems in basic data capabilities. Find the intersection of the two in the operational scenario, start from the intersection, and release the value of data in the application.

V. Conclusion

So far, from data values ​​to data-driven operations in five areas: products, users, content, activities, and services, the author has written a series of articles, systematically expounding the theory and practice of building a data-based operation system.

In short, the construction of a data-based operation system is a systematic value project that concerns the survival and development of the enterprise.

Enterprises should keep pace with the times and follow the trend, use data as the sail, the "four-dimensional cultivation method" as the boat, and the action guide for building a data-based operation system as navigation. Start a new journey of digital transformation for enterprises, and find new paths and models to use data weapons to solve enterprise development problems.

Author: Huang Xiaogang

Source: Big Data Product Design and Operation