Practice summary to help you understand what data products
"Every day people talking about big data, data products speak, speaking platform, but always felt very vague concept, the less will only face more disoriented, so boldly go forward!"
A lack of summary, the concept of fuzzy data products
Today want to return to nature, to write data to understand the product. A reader from me asked me a question: product definition data whether there is a consensus?
We are beginning to see that, in fact, I really do not know how to answer this question seems simple, but in reality very vague, what is the data product? Inside my mind immediately influx of a lot of content, usually do excel reports, daily look at the weather forecast, micro-channel index, film scores, and so on. But I still can not find a general language to explain what is data products.
Recently turned a lot of information, see a lot of good understanding, direct output is always used to feel my heart is not at ease, much deliberation decided to combine their own time is not long career writing for understanding data products.
Second, practical experience and understanding of product definition data
7 years, with data about where the jobs are, now looking back, my job change is based on the company's rapid growth in demand for data systematization urgent requirement is also based on this background, the company opened a systematic project data, I was fortunate to join this project, and we became the first company data product manager at the end of the project. So far, I have only been exposed to the product data of the word, while big data related words have begun to enter my eyes.
Before job transfer data product manager, my job can be summarized as data analysts, data analysts to data from the product manager for the post, on the nature of the work I do is the biggest change from manual data people into the data system of people . For ease of understanding, I drew a change in my work and the timeline of the linkages between, see below:
Data Analyst post stage, the output of the daily tracking reports, doing business analysis reports and sales forecasting is the primary task of job duties, the tools used mainly to excel, to the phase of the data product manager, data analyst at a major demand-side data product manager.
But the final output are based on data carrier, is used by the user-driven business operations. So, I think the data can be understood as the product data as raw material for the play value of the data after processing the data to help make business decisions in the form of a product.
这样看，我作为数据分析师岗时手工做的报表或者PPT都可以称为数据产品，但是由于当时公司体量不大，处理的数据量相对较小，还没有数据产品的概念，所以这个词汇并没有成为常态的词汇（当然我只是说的我们公司以及行业）；
随着公司体量的变化——线上线下店铺变多、会员数量增长、供应管理业务复杂度增强等，业务系统数据日益增多，通过数据分析结果驱动业绩增长及业务管理的需求刻不容缓，大数据这个词也渗入到其中，数据需求系统化实施则应运而生，于是就有了数据产品、数据产品经理。
再回过头来总结一下，数据产品的概念是基于大数据应运而生，且是针对系统化实施的数据载体。所以现阶段，数据产品的定义可以把范围缩小一点——以数据为原料，自动化产出数据载体的一种产品形式，以我们公司来看，就是指我们现在建的大数据平台（移动端、PC端都有），从大数据平台上产出的报表、看板、自助分析工具等我们称为不同的数据应用，当然也是数据产品。
三、清楚服务对象，深化数据产品认知
在理解了数据产品的基础概念后，我们再来看下数据产品的服务对象，按服务群体的不同我觉得可以这样划分：
1）企业自建型，服务对象是企业员工。譬如我们公司的大数据平台，组建数据平台研发团队，自主开发创建前端对用户平台。
2）对标企业商业产品型，服务对象是企业，当然企业购买之后同样服务于企业员工。以我们公司为例，数据团队组建的前两年，我们都是直接买的数据分析工具——SAP BO（这就是我最开始接触的数据产品，准确来说是BI工具，即商业智能系统），后面我们又买了神策数据用来分析新零售这一块的业务（主要是对于微信、APP、外卖等的分析）；
当然还有很多譬如Tableau、U-app等。
3) direct services to the public product type . For example RUTHERFORD NBA database, micro-channel index, Baidu index.
Different types of data products to serve different groups of people, but basically the same goal: maximize the value of data to help users solve problems or make business decisions.
Fourth, understanding data platform architecture to understand the data the product development process
Data product and application development process is basically the same: to obtain basic data, process data, output data platform, with the advent of the era of big data, more and more companies increasingly attach importance to the application data, build data platform; data platform for building, The basic architecture is as follows:
As data product manager for system implementation process is essential knowledge as a data product manager. The figure is the most basic data platform framework to facilitate the implementation process for all data products have a better understanding, I will do a more detailed follow-up resolution for the construction of big data platform.
Five multi-point summary
By the above, we do not know for cognitive data products have not become clearer. in conclusion:
1) macroeconomic data products can be understood as a data carrier can play a data value;
2) With the arrival of the era of big data, data products and systematic implementation of the application was born, and it comes daily data products more defined as the automation can play a data carrier data value;
3) systematic implementation of product data, to build a data platform is essential;
The above is my understanding data products for different industries different groups have different cognitive, I hope I can bring a little help resolve for everyone.
The End, and you want to grow together!
Sui Suinian:
I want to write content we can bring help, but we hope to have more tolerance for me, correct me welcome everyone to everyone to learn.
Author: Wang Tu, micro-channel public number: Data Product Manager Advanced Way (ID: DATAPMLZ) and you want to explore advanced data product manager of the road!
This article published on everyone is a product manager from the @ Wang Tu original. Without permission prohibited reproduced