Data product manager has been a popular job in the big data industry in the past two years. Some friends often ask me what a data product manager does. I will briefly tell you how to spend a day as a data product manager. It is a record. Let's read the article, maybe everyone will have a deeper understanding of this profession after reading it.
01
10am: Handling data needs
Go to the company to turn on the computer to deal with the needs of the next week. The types of needs include business data needs or platform data needs. This also reflects that the work direction of the data product manager is actually subdivided. The content of the data requirements of the object is different, and the capability requirements for the data product manager are also different.
When dealing with data requirements, the data product manager should make the following assessments of the requirements:
1. Demand rationality assessment
Regardless of the role of the demander or the type of demand, the data product manager must make a reasonable assessment of the demand. This rationality includes several dimensions: demand income or value, whether statistical key indicators or construction key reports can reflect business changes in a timely manner. From my experience, it is actually very difficult to ask the demander to think clearly about this point, so a data product manager is needed to evaluate or guide the business. This process also has requirements for data product managers, whether they have enough understanding of the business, and whether they can use their professional capabilities to help the business find problems at the data level.
2. Needs prioritization assessment
Different from functional product managers, data product managers face a large number of data needs at the same time, but most companies are extremely tight on data manpower and resources, so they need to prioritize the needs It is a necessary skill, and it also judges the priority in several dimensions: demand impact, business urgency, whether it affects business decisions, etc. These are also the basic skills of product managers.
3. Demand realization plan evaluation
Different from functional products, when data requirements enter the development stage, data product managers need to have the ability to evaluate data requirements and implement related technical solutions. This is why many people think it is difficult for data product managers to change careers, that is, they need certain big data expertise . For example, the demand for building a real-time large screen, what computing engine is used for development to complete real-time calculations, and how often the real-time large screen updates data, etc. All these require the data product manager to understand. The important part of requirements realization is the completion of development. Knowing more about technical knowledge will reduce communication costs and trial-and-error costs for requirements delivery.
4. Demand realization schedule assessment
You may wonder why the data product manager still needs to evaluate the demand schedule. The reason is that it is difficult to avoid error scenarios in the development of data requirements, so it is necessary to reserve time for data verification. At the same time, data development is based on multiple requirements in parallel, so data product managers need to have parallel demand scheduling control and rough estimation, and estimate the expected completion time. In this way, the delivery rhythm of a data requirement can be roughly estimated.
02
11am: Team meeting
In fact, there is nothing to elaborate on during the meeting. In addition to the daily needs, the data product manager also has some project-based needs. The team will synchronize the project progress, current project problems, and whether it will affect the overall progress every week. The content of these tasks is similar to the daily life of a project manager. Or some projects require an internal review plan, such as the design of the data platform prototype and the function optimization plan.
03
2:00 p.m.: Formulate and output buried point specifications
Many companies now require that data product managers must have some experience. Buried points are not unfamiliar to Internet users. As the basis for data reporting and statistics, the importance of buried point design is beyond doubt. Junior data product managers or data product managers of small and medium-sized factories need to spend a lot of energy on design and embedding points every day, because the data infrastructure is not deep for companies of this type of business scale, and it is already rare to ensure the accuracy of data reporting Valuable. Then the data product manager must be fully familiar with data links such as buried point reporting, development implementation, and log storage. After all, the repair cost of buried point problems is very high, so professional things are left to professional people, and the data product manager is responsible. Taking the lead is very necessary.
04
4:00 p.m.: Complete platform optimization design
As a role for front-line business students, I will receive various complaints from business students about the data platform on a daily basis. Collect user feedback and transform it into platform optimization requirements to enable development and enable business at the data level more conveniently. This requires data product managers to have platform design capabilities, some basic specifications for platform construction, application scenarios for commonly used visualization components, and how to isolate data permissions.
05
7:00 pm: Finish remaining work + personal improvement
06
9:00 pm: off work
The time nodes written above are for reference only. This article is written to let everyone have a more intuitive understanding of the daily life of a data product manager. It is aimed at those who ask what a data product manager does? Does a data product manager need to write SQL runs every day? This kind of problem. What the core wants to reflect is that data product managers have high requirements for comprehensive capabilities, skill trees, and skills. It is reflected in not only solving data needs with ease, but also going deep into the business, and thinking about how to use data to empower the business and help the business in light of business development. Get closer to the essence of things faster.