Text | Sailor Brother
This article comes from: Knowing the column " FanRuan Data Application Research Institute " - data dry goods & information center
According to Gartner's survey results in the past 7 years, 71% of the surveyed enterprises are in the low maturity stage, which is the 1st or 2nd stage in Gartner's five-level BI maturity model (five stages are appended) . Enterprises with low BI maturity are difficult to obtain maximum value from data assets, and the dissemination of BI capabilities is also hindered.
Overall, companies with low BI maturity typically have the following characteristics:
* The IT infrastructure is backward, data chimneys and data islands are common, and there is no or little data governance;
* There is no independent data and analysis center, and reports are usually embedded in business systems such as ERP and OA;
* Use excel to make reports or analysis, repeat work, easy to make mistakes, occupy and waste a lot of human resources;
* There is little interaction and collaboration between the IT department and the business department. The business department complains that the IT department is slow to respond and has no value, and the IT department complains that the business department has too many needs and is volatile;
There are many reasons for the low maturity of BI, such as lack of professional knowledge, lack of experience in strategic planning, limited budget for informatization construction, no data analysis culture within the enterprise, conservative thinking and resistance to innovation Wait. In this regard, Gartner gave suggestions from four perspectives: strategy, people, governance, and technology, as shown in the BI maturity composition chart below, to help enterprises move more efficiently to higher levels maturity.
1. Strategic level: Develop a BI roadmap
Like an IT project roadmap, a BI roadmap should clearly define goals, timelines, key milestones, priorities, resources, business plans, and KPIs, which need to be coordinated by business and IT. A BI roadmap should be iterative and updateable so that IT has enough time and flexibility to apply best practices and react to any changes in business or technology. The author suggests that companies with low BI maturity should start with a small project, take a simple and well-defined business process as a breakthrough, expand the results through small victories one by one, make steady progress, and finally fight a big victory. Success is not the adoption of new technologies and tools, but the actual business value brought about, so that the maturity level of BI capabilities will be improved.
The personnel and management level
1. Build a business-driven "virtual BI team".
Strengthen the connection between business departments and between business departments and between business departments and IT departments, and involve all relevant stakeholders in the discussion, so as to achieve the alignment of processes, technologies, resource investment and goals, so as to bridge the gap between departments and overcome The problem of information asymmetry ensures that everyone works together to achieve strategic goals.
2. Involve business stakeholders in analytical decision-making.
Traditionally, BI tools are more used by senior managers, and it is difficult for middle-level personnel to get support from the IT department in a timely manner. IT departments should include business stakeholders in the analysis discussion, allowing them to analyze and make decisions from different perspectives. Combining business and analysis allows top and bottom to clearly define the driving factors and goals of key businesses, as well as the analysis strategies that support the achievement of goals. Only when combined with specific business analysis can the value of BI analysis be maximized. Many projects were cancelled or ended in failure, mostly because the business reason was no longer valid, or the IT department lacked awareness of the latest changes in the business.
3. Technical level
1、建立可以随着BI成熟度增长而扩展定基础数据治理框架。首先应当全面掌握企业的信息化状况，即创建信息资产的数据清单，明确报表、表单所在的系统位置，以及数据的来源和去向，确定相关数据、报表的权限和用途。其次需要确定应该管理的优先事项，一般的经验是从共享的关键数据开始治理，比如销售、财务模块的数据。一开始，治理方案可能非常基本和有限，也会遇到一些阻力，需要业务部门与IT部门一起克服。企业高层也应当给予支持，将数据治理定义为基本策略，是公司战略必须，而不是单方面的一个事情。随着企业的经验和成熟提升，治理框架可以扩大范围，实现正式的企业信息管理（EIM）或主数据管理（MDM）计划。
2、选择适合企业现状的工具。在评估一个工具时，不仅要考虑成本，特别对于没有IT能力薄弱的企业，还需要考察其他因素：咨询能力，分析功能，易用性，培训和服务支持。除此之外，企业还应当主动利用市场上已经提供的技术、技能和经验，而不是再造轮子。
附：上文提到的Gartner BI成熟度模型分为五个阶段，分别是：
1、不知道阶段（Unaware）
在不知道阶段，企业没有BI信息化，所有分析都是基于excel进行，分析也都是临时的，没有完整的分析策略和体系。该阶段问题也非常明显，报表需要重复制作，多次加工，人力成本高企不下，时效性差，做出来的报表也可能没用，或者数据就是错的却无法校验，种种问题让数据化决策称为空谈。
2、投机阶段（Opportunistic）
在投机阶段，业务部门追求自己的BI和分析计划，自己对数据分析负责。企业内部存在多个业务系统，如CRM、ERP、BPM等，业务系统中多少内置着一些报表和分析页面。每个系统的关联性并不强，数据烟囱和数据孤岛问题一直存在，业务部门很难做整体的、全局的分析，各个部门的数据、指标甚至冲突。为了做报表，做分析，业务部门可能单独采用一些数据集成、中间数据库和分析工具，最终导致企业存在多个分散存储在不同地方的源数据库、BI平台、仪表盘等。虽然这种模式可以满足业务部门的一部分需求，但知识不能共享，优秀项目难以扩大解决其他用途，企业仍然处于规模经济较低的状态。
3、标准化阶段（Standards）
在标准化阶段，人力、流程和技术在企业中协调发展，数据可以支持用户做分析决策，组织开始转向共享服务，确定技术标准。这个阶段，企业还具备有业务部门、IT部门和数据分析师组成的BI能力中心（BICC），以分享知识，提高业务系统或信息使用的一致性。业务部门和IT部门共同建设相关业务的分析、分享系统，至少有一位高级管理人员，成为业务数据分析的专家。技术标准开始出现，包括信息基础建设、数据仓库和BI。企业首次通过改进配合流程和技术标准化，降低BI和分析工作的总体成本，但是，此阶段的BI和分析系统的适应性仍然很低，企业尚未出现规模经济效应，管理者对其他流程的洞察力仍然不足。
4、企业级阶段（Enterprise）
在企业级阶段，企业以绩效为导向，企业运营和分析指标体系已经十分完善，具有一致性和稳定性，首席数据官（CDO）和首席分析官（CAO）在企业的决策流程链条中有着不可替代的作用，高层管理人员对BI建设给予更多的人力和财力支持。此阶段，公司中高低层均可以借助BI和分析系统做报表、做分析。虽然BI和分析系统变的更加高效，但是使用增长，成本依然很高，企业必须确保不同领域都有高水平人才，适当支持每个业务部门对新技术和新模型的应用。
5、变革型阶段（Transformative）
在变革型阶段，BI和分析系统已经成为企业和IT部门共同运作的战略举措，并得到企业最高层次的支持和管理，企业将信息化视为战略资产，利用BI和分析来创造收益、提高运营效率，为客户提供一流的服务。企业绩效指标框架扩展到相关的合作伙伴与客户，所有利益相关者都使用BI和分析系统响应多变的业务分析需求，并进行变革性的决策。在此阶段，BI和分析系统有着非常强大的灵活性，可以让用户进行多样的自助分析、预测性和规范性分析。