Enterprise-wide data standardization toward Data-driven management

Yoshiaki Ito Director, MIR Co. Ltd.

Yoshiaki Ito Director, MIR Co. Ltd.

Data-driven management is to lead to swift management decision-making and actions by quickly detecting signs of changes in the business environment from internal and external data. The key to achieving this is how to collect and utilize highly accurate data in a timely manner.

In reality, however, with the exception of some advanced companies, we rarely see the cases in which data visualization and utilization are progressing company-wide. In fact, each company holds a lot of source data, from financial data and transaction data to such unstructured data as voice of customer. Although there are cases where dashboards are built, they are often limited to some organizations and areas.

Why is it not possible to visualize and utilize data company-wide as originally planned? The main reason for this is the failure to share the importance of data and promote data standardization activities involving the business sector.

The following are the factors that hinder the progress of data visualization in enterprises.

1. Code "granularity" differs between organizations and systems

2. The data is not entered correctly based on the rules, and the "accuracy" is low.

3. The necessary data cannot be obtained in a timely manner, and the " timeliness" is low.

4. Some data is managed by Excel, etc., and "comprehensiveness" cannot be ensured.

In order to eliminate these obstacles, it is essential to understand the actual state of data management that is managed separately by organizations and systems, and to promote standardization across organizations.

In promoting such activities, I would strongly recommend to set up a dedicated data team. In a project that builds a dashboard in line with the introduction of ERP, there are cases where the definition of code and master is entrusted to each area team such as sales, purchasing, and accounting. In such a case, in the subsequent phase, problems such as lack of necessary data in the dashboard and inconsistency between the data are likely to occur.

Therefore, we MIR, together with the client, setup a data team from the grand design phase of the project. We take an approach so that the purpose of data utilization is clarified, the data necessary for its realization and the code that is the axis of analysis are extracted, and the standardization policy is defined across the team.

For example, suppose that one of the purposes of data utilization is to "understand the budget achievement prospects for global accounts and to be able to discuss necessary actions in regions and business divisions with large deviation from the target."

In that case, the necessary data is not just the sales data of ERP accounting, and it is necessary to collect the budget data of the business division and the sales opportunity outlook as source data.

In addition, we will clarify the axes and hierarchies that we want to analyze, such as accounts, regions, and organizations, and specify the standardization policy of the code that indicates them company-wide.

Unification is not just about deciding the code numbering system. As a standardization policy, MIR has set the following five themes to be examined, and proceeds to define and consensus-build for major codes such as customers, items, and organizations.

1. Meaning / name of the code

2. Code numbering unit (granularity) and hierarchical structure

3. Code system / number of digits

4. Code numbering / master management process / organization

5. Code numbering / master management system

In fact, when you say "account" or "customer", there are various names and types besides the code numbering structure which differs by organization or system. There are cases where the customer code included in the sales opportunity information, the customer code included in the sales order, and the customer code of the sales revenue differ not only in the code system but also in the numbering granularity, depending on the organization or system.

In addition, various types of customer codes are used depending on the commercial relation and distribution, such as the customer code as the orderer, the customer code as the delivery destination, and the customer code as the end user.

When it comes to an "account," what does it mean, such as "a corporate group of end users who use their products and services," rather than an orderer or delivery destination such as an agency? It is necessary to define it as a standardization policy considering hierarchy and granularity.

Whether it is source data or code that is the axis of analysis, it is an activity that crosses business areas, so such activities cannot be carried out by teams in each area with one hand. Data teams must take the initiative and coordinate with relevant teams and internal departments.

Nothing can be achieved by simply introducing SAP or building a dashboard in response to the cosmetic concept of "data-driven management."

As mentioned above, it is necessary to clarify the purpose of data utilization and be prepared to steadily promote the standardization of source data and analysis axis code.

Enterprise-wide data standardization toward Data-driven management

Financial

Data Management

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