Planning and Data Strategy – How Compatible Are They?

Utilize Existent Investments in Modern Data Architectures and Data Governance Structures

Almost every company has launched initiatives for digitization and transformation. Typically, these initiatives very soon recognize that existent processes, systems and organizations impose limits on ambitious goals. Data strategy serves to overcome these limitations.

Within the framework of data strategies, operational and scheduling systems & processes are analyzed in order to identify deficits and create a concept which better meets present and future requirements.

To implement this vision, one or more initiatives are typically launched:

• Introduction of additional systems to manage current and future growth
• Adaptation of existent BI and data-warehouse systems to meet current needs as well as legal and regulatory requirements
• Development and expansion of analytical platforms in order to implement operational-analytical use cases, and to reveal new correlations as well as additional optimization potential with the help of statistical methods
• Introduction of a data governance organization
• Introduction of data-quality management initiatives

These investments are used to establish skills which are of great interest to controlling at the company.

How Does Controlling Benefit From Planning, Budgeting and Forecasting?

Controlling can benefit directly and indirectly from these changes:

• Data-governance organization can be used to address data-quality requirements of controlling across departments
• The quality of data fields used to derive characteristics of relevance to controlling can be actively improved via data-quality management
• A data warehouse serves for access to harmonized, quality-assured data, often a better option than isolated data extracts
• Specific know-how which is being established increasingly in analytics departments can be made accessible to controlling for forecasts
• Statistical forecasting models adapted for controlling provide valuable data for sub-plans, and enable good automation for plans with a short horizon
• Highly detailed data history opens up new possibilities in planning
• This can serve for establishing simulations controlled via parameters, or allocating planned values based on the available degree of detail

Created as a result are opportunities to successively enhance the maturity of planning, introduce new data-driven planning approaches and increase organizational efficiency during planning.

‍

A Standardized Process Model Assists Implementation

But what to do if the "damage has already been done" by omitting planning. This is where a standardized procedural model can help.

1. An inventory is used to register and consolidate the requirements of stakeholders. Also useful here is a simultaneous system assessment to identify and evaluate any existent planning solutions and potential source systems.
2. In a planning step, the process envisioned for the future is described. A distinction must be made here between the long-term target process and the specific expansion stage intended next in each case. In this step, the flow of key figures within planning should be described as precisely as possible.
3. To determine future architecture, integration of the planning platform into the overall architecture must be defined. The aim is to create transparency and a common understanding of core data flows.
4. It is only in the fourth step that the topic of software selection becomes relevant. Based on the previous steps, core requirements for a future software solution clearly emerge now. In this step, it is essential to assess whether software products already established in the company can be used.
5. A roadmap helps visualize and roughly organize step-by-step optimization of planning. A coordinated draft of a roadmap facilitates orientation for the various departments and the management.

The intensity with which these five steps are carried out depends heavily on the respective circumstances in the company and should be clarified in advance.

‍

‍