What is Baselining?

In every M project, it is necessary to create a detailed concept for the planned separation or integration of a part of the company. However, before one can determine in a concept how the implementation should specifically take place, the question arises as to what exactly is being separated or merged. This is where baselining comes in. It involves (in digatus’ focus) a query of the IT used by the affected part of the company for a comprehensive inventory of all areas relevant to the project. This includes, in particular, IT infrastructures and IT applications as well as licenses and contracts.

Baselining is to be distinguished from due diligence. This is a defined phase in the overarching M process, which involves an initial assessment of the company to be bought or sold. Here, too, the initial IT situation is analyzed, but due diligence is more of a risk assessment aimed at determining the impact on the selling or purchase price. Baselining specifically involves recording the inventory that is necessary to set up a plan for separation or integration.

How Does Baselining Usually Work?

How baselining is conducted generally varies depending on the structure and size of a company. However, three basic process steps can be defined:

Baselining in Three Steps

1. Data Collection

In the first step, all information is gathered. Initially, existing data sets are used, such as application databases. Often, however, these information sources are not yet sufficient or incomplete. Therefore, the IT department is subsequently questioned to obtain more detailed and specific information. The emphasis is on capturing the status quo: What is actually being used today, apart from future plans? To lend the necessary authenticity to the purely technical data sources and to secure further planning, the business must ultimately be consulted. The users of the systems and those affected by the M activity have the final say.

2. Quality Assurance and Assessment

After the information collection, quality assurance takes place and the data is put into context and evaluated. The data set is cleaned of errors, supplemented with missing information, and the existing data is evaluated and consolidated so that further planning is based on a relevant data foundation.

3. Data Preparation and Communication

In the next step, the collected data is prepared and clearly presented, often in graphical form. This makes it clear, for example, how many applications are present and how they are distributed across business departments. This provides a targeted overview of the current situation, which can then be communicated to stakeholders.

What Challenges Need to Be Overcome?

The baselining phase is also subject to the usual time pressure in projects, and depending on the size of the company, it can take up to eight weeks. Especially in large corporations, not all necessary contacts are always known, and the existing knowledge is distributed among many heads. Therefore, it is necessary to first capture all decentralized data and then merge it.

Existing data sets are often very technically oriented and tailored to the existing organization, which is why their relevance to the project should be questioned. In a carve-out scenario, it is therefore difficult to identify which data exactly belongs to the affected part of the company. Additionally, with existing data sets, the question arises of how well they have been maintained – in large corporations, it is more likely that extensive data sets are available.

IT departments naturally know which applications and systems they operate. However, they don’t necessarily know how these are actively used and implemented in practice. Another factor is the direct procurement of IT by the business. The IT department may therefore only be aware of a portion of the affected devices and applications.

The inquiry within the business is made more difficult by often lacking IT expertise. This frequently results in imprecise and incomplete answers that are not aligned with the desired goal. Often, the answers overlap as many employees use the same applications.

The biggest challenge in baselining is reconciling the inquiry with ongoing operations while minimizing disruption. Data collection is a time-consuming activity for which the affected business area usually has neither time nor capacity. Consequently, the general feedback quality tends to be rather low if employees are simply given a (usually very extensive) questionnaire to fill out independently. Additionally, there is the increasing global distribution of employees with growing organizational size.

Our Solution Approaches for These Challenges

Process Optimization

We have learned that baselining represents a significant additional burden for all involved, which is why it’s important to keep the effort as low as possible. Therefore, we rely on a prioritized approach to obtain all relevant information for the next step as quickly as possible.

Instead of the usual waterfall method, we use an iterative approach for an efficient project flow and build the baselining from coarse to fine. In this process, we concretize the collected information in several steps. This allows planning to take place parallel to baselining. Moreover, at any given time, only the currently necessary questions for the next steps are asked to minimize the burden on the business and further accelerate planning.

Utilizing Existing Data Sets

It is important and indeed sensible to use existing data sets and incorporate them into the baselining process. However, they only serve as a basis for the subsequent inquiry in the affected departments. On this basis, the first step is to verify the collected information and then supplement it accordingly with feedback.

Optimizing Feedback Quality

Feedback quality can be improved by transforming the knowledge inquiry of employees into a guided process. By making the inquiry more personal in the form of interviews or as a question-and-answer game, there is an opportunity to address employees more individually and thus obtain targeted answers. In our experience, it is also important to meet users where they currently stand and adjust the questions accordingly.

Increasing Efficiency Through Tool Use

The use of intelligent tools can significantly increase efficiency in baselining. The considerable effort required for consolidating decentralized data can be substantially reduced, enabling additional rounds of inquiry and thus facilitating the iterative approach.