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On data value with SAP data

If you wish to increase SAP data value, you need to first determine how to measure the value of data.

Antti Lyytikäinen / November 30, 2022

In the past, bringing SAP ECC table data to data lakes has been a typical course of action in many companies. However, the cryptic data that resides in those tables requires the help of an armada of SAP deep experts, if your plan is to use the data for something more meaningful.

S/4HANA changes this, and not only through tech. In this blog, we focus on how to increase the value of SAP data. If you wish to increase SAP data value, you need to first determine how to measure the value of data. We believe that the value of data can easily be calculated with the following formula:

Data Value = Data Quality x (Data Utilization + Data Impact)

What is our thinking behind this formula? Let’s first focus on the components in the formula: data quality, data utilization and data impact.

Data Quality

This component consists of sub-components such as:

  • Data consistency: Are your people, processes and source systems producing data with the defined content (dimensions & attributes)? If the answer is no, this is a core area that you should focus on in your S/4HANA business transformation.
  • Data reliability: Can you trust and understand the data, does it match with the source systems? Are the business rules and calculations correct and up to date?
  • Data reconciliation: Do you understand how the figures have been calculated, and do you understand what the values mean? Are the necessary data users capable of validating it?
  • Data traceability: Are you able to backtrack to all necessary source data through the end-to-end data value chain (e.g., ESG-policy requirements)?

Data Utilization

This component consists of sub-components such as:

  • Data accessibility: Do you know where your data is? Is it possible to access it feasibly?
  • Data security: Do the defined access control and security policies cover data throughout all data pipelines? Do they match e.g., the same authorizations as in the ERP, so that there are no unintended audit breaches when ERP data is taken outside and under different authorization concepts?
  • Data culture: Is utilizing data part of your corporate culture? What is your business utilizing data for, what are the spearheads, what are all the different kinds of requirements that are far beyond just analytics as it’s all about the data pipelines in the enterprise architecture?
  • Data saturation: Have you been promoting and marketing your data solutions in your organization? Are people aware of your data solutions?

Data Impact

This component consists of sub-components such as:

  • Decision making: Do you base your decision-making on data, or is it based more on instinct and gut feeling (business wisdom/experience)?
  • Vision and strategy: Do you take any actions based on the data? Is the data helping you to improve your processes and create concrete business benefits?
  • Measuring: Are you able to measure the impact that data has in your business? Do you have a process for this?
  • People: Are people happy to use data and do they trust the data? Does it make their workday easier?

How to determine and increase Data Value

As we all remember from our math lessons, something multiplied with zero is equal to zero. Thus, if any of the components in the formula is zero, is the value of your data zero?

The answer is not that simple… In general, if data quality is causing headaches, it also means that there will be a lack of trust in data. If you improve your data quality, you will make it more valuable, and data usage will also increase. Then again, if you have world-class data, but nobody is using it, how valuable is it? Or if you have world-class data and it is utilized actively, but it has no proven impact on decision-making? Does this mean that your data is just fancy-looking eye candy?

Even if you somehow end up in a situation where the formula gives you a zero result, there is still reason to believe that actually, your data is an asset that will give the expected value in the future.

Maybe you just need to mould it into something informative and valuable with priorities and a roadmap built for data along with your S/4HANA journey, APIs, integrations, data lakes and wherever you need to take your SAP data to. Just remember to have a data architect role incorporated in your S/4HANA programs, so your architect can assist in planning this S/4HANA database and outward structure. And remember to put a lot of thought into how you can make the life of employees easier with real-time embedded analytics tied to user-designed workbenches and not just Fiori as the new GUI.

The way to really improve the result of this formula is not tech, and most definitely not data lakes or DWs or dashboards. The key is taking the data requirements from a business perspective as a holistic topic within each process area of the S/4HANA design along with all of the non-functional areas that cover the whole architecture. Make sure you have a data architect to coordinate the work and link these efforts to the data management processes you may already have or are building.

In essence, you need to ensure much closer cooperation between the data experts and SAP experts. You need to shift responsibilities and blow up any siloed thinking or pro-tech thinking. Your primary focus should be on making sense and benefiting the users. Rest assured, this can certainly be achieved when investing in S/4HANA for SAP data. If you follow the line of thinking that we presented above, you can improve your master data and data quality and implement data management processes at the same time as you build S/4HANA. It is the best time to do it - it gets harder if it’s an afterthought.


Antti Lyytikäinen
SAP Analytics Team Lead
Sam Holmström
Lead Analytics Architect

The author has 20+ years of experience from data projects within different industries. The projects are covering various data solutions that are designed and implemented in diverse landscapes both technology- and lifecycle-wise, leading to a comprehensive understanding of how to drive information from data.


Antti Lyytikäinen

SAP Analytics Team Lead

Sam Holmström

Lead Analytics Architect

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