The past few years have been a year of rapid growth and turbulence in data applications. Due to the wave of 2B industry upsurge brought about by the investment boom, products around data business emerge in an endless stream, whether it is a general-purpose visualization tool or an analysis product with business attributes.
As an enterprise data solution that once preceded big data, business intelligence BI has gradually brought Internet attributes after experiencing the wave of the Internet, and has been labeled as agile and big data.
Abroad, BARC, a well-known research organization, has surveyed nearly 2,800 users, consultants and suppliers on important BI trends. The results of the 2017 BI Trends Report show that data visualization, self-service BI, and data quality/master data management are considered to be the three most important parts of BI work, and it is the death of BI that is more concerned now.
In China, judging from the nearly 100 enterprise BI systems that FanRuan has participated in the establishment of in the past year, enterprises are also based on data visualization, product self-service and enterprise standardized data management for BI. The author used FanRuan's BI product, FineBI, to do a simple survey on the application of enterprises in the past year. From the application pain points and product concerns, he made an inventory, hoping to give many enterprises some thinking and reference.
Self-service BI analytics is the norm.
In the past, the online BI system of enterprises was often driven by enterprise IT personnel, who helped to establish data models from the perspective of informatization, quickly create data reports, and assist business analysis. Even when transitioning to today's new BI applications, many companies still do this, but the new tools are more efficient to use and easier to analyze. However, gradually we found that many companies, such as banking, retail, Internet e-commerce and other consumer-oriented industries, began to participate in data analysis and gradually took over the overall work. Colleagues are looking for analysis tools, and many companies have even set up data analysis departments or teams. Such a business-driven trend has formed and spread, and self-service BI tools have become more of a "standard" for each person.
Mobile analysis has become a new trend, and applications also pay attention to individuality
BI的作用一方面是协助业务分析，另一方面提供管理者及时有效的数据支撑，降低决策难度。伴随移动OA，移动CRM的出现和流行，移动端的数据分析也逐渐成为新趋势。据调研，67%的CIO以业务领导表示移动BI是一个不错的方式并有意愿尝试。领导需要一个简单的入口，能够看到部门的关键维度汇总分析，甚至对重要数据实时监控并消息推送，找出原因及导致结果的一系列相关因素，以便在最佳时间做出快判断。这往往要求工具查询统计速度更快，响应更及时，BI在技术上迎合了这一点。
BI对数据化管理存在一个反向推动作用
对于众多企业尤其是中小企业，通过基本的分析工具可以完成日常的数据分析工作。但很多应用了BI系统的企业表示，由于BI分析对结果的响应更快，我们发现分析过程中出现的结果偏差往往在于在于数据缺陷、偏差等质量，这是过去所忽略的，背后更是反映了数据规范和管理问题。有这样一个有趣的例子，某企业的销售部门在对某一地区的会员消费进行分析，发现A地区的会员消费率相当得高，是某省会城市的2倍，但会员数量却是该城市的60%（左右），并且这一现象还存在一个上升趋势。后深入调查发现，由于会员消费模式刚推行，一线人员的管理存在漏洞，导致了大量的代刷以及会员价代购现象。某企业IT管理者也曾表示，对于一个认同数据化管理并希望长期致力于这一块发展的企业来讲，BI的应用可以推进企业数据架构的治理和数据质量的梳理工作。很多应用BI或者认同数据化管理的企业也意识到，即使数据展示再有条理再美观，如果数据有缺陷，质量度不高，没有全面的数据集成和质量保障举措，BI的实施都不可能成功。