The importance of data integration in product portfolio management for a holistic view

This article offers a practical approach to optimizing data integration in the context of product management. It highlights how companies can strategically begin integrating relevant data sources related to their products in order to develop a holistic view of their product portfolio.

In today's business world, data-driven decision-making is gaining increasing importance, as technological advances, especially in artificial intelligence (AI), accelerate this shift. In the face of growing uncertainties and crises, it is crucial for companies to act quickly and precisely. However, many companies are lagging behind because informed decisions depend on the availability and integration of relevant data - data that fully describe the product. Product portfolio management plays a key role here, enabling companies to make agile, market-oriented decisions around their products. To be prepared for future technological challenges, companies must continuously optimize their data and system structures. 

The problem of increasing system complexity

The problem of increasing system complexity presents a significant challenge for companies, especially in times when fast and precise decisions are necessary. Over the years, many companies have developed heterogeneous system landscapes, where each department relies on its own tools, platforms, and data sources. This fragmentation leads to "data silos", where information remains isolated and is not shared across departmental boundaries. This not only makes accessing relevant data more difficult but also significantly slows down decision-making processes.

For a product manager, who is in constant communication with various stakeholders, this complexity becomes a serious obstacle. The development department, for example, focuses on technical specifications and product plans, while sales analyzes sales figures, and controlling monitors costs and profitability. These departments often work with entirely different systems and use different metrics to process their information. These isolated information sources make it difficult to obtain a comprehensive view of the product and its performance.

The result is that decision-making is delayed because data must first be laboriously gathered and processed from various departments. Additionally, there is a risk of inconsistencies and errors, as data is interpreted and processed differently across departments. The lack of a centralized overview of all relevant information hampers the ability to make strategic decisions about the product portfolio. Without a clear, consolidated view of sales data, cost structures, and technical developments, there is no foundation for making informed decisions and ensuring competitiveness.

The growing data demand in the product lifecycle

The requirements for the data needed for successful product management are steadily increasing. The product lifecycle is becoming more complex, and with it, the amount and variety of information that must be taken into account is growing. From the product idea to market launch and the end of the lifecycle - different data sources are required at each stage to support decision-making.

This variety of data, ranging from technical specifications, market analyses, and sales results to customer feedback and profitability metrics, must be efficiently utilized to ensure a product's success. However, if companies continue to rely on isolated, unconnected systems, they hinder data flow and significantly complicate decision-making in product management. Data silos prevent access to critical information and lead to a fragmented view of the product portfolio.

In product management, this connectivity is especially important since products form the backbone of business success. When relevant information about products is scattered across different departments and difficult to access, the overview of the current portfolio is missing. This not only hampers the assessment of the current market situation but also complicates the development of forward-looking product strategies. Therefore, companies must connect their systems to ensure a holistic view of the business and improve the efficiency and accuracy of their decisions. Only in this way can they successfully manage the increasing product complexity throughout the lifecycle.

Products as the basis for integration

A key aspect of making better decisions is understanding the product itself. Who buys the product, under what conditions, and what does this mean for the internal business? This not only involves sales but also suppliers, materials, resources, and necessary services. A deep understanding of product performance is crucial because it allows companies to better manage their internal processes and use resources more efficiently.

When companies shift their perspective and view their business from the standpoint of their products, they more quickly identify dependencies and opportunities. Products drive the business - from raw materials to the end customer - and influence all facets of the company. Success, therefore, largely depends on how well the underlying data is connected.

Is your company fully connected yet?

At this point, the question arises: Has your company already established a seamless connection of all relevant data sources related to your products? Are all departments able to access the same, up-to-date information to make informed decisions? This is especially crucial in the area of product portfolio management. If not, it’s time to take action to lay the foundation for data-driven decisions.

How to begin - A pragmatic approach

So how can a company start on the path to becoming fully interconnected? Here are some steps that product management should take within the framework of product portfolio management:

1. Identification and localization of relevant data sources: The first step is to identify and locate all relevant data sources. Stakeholders from all departments should be involved to understand where data silos are forming and which systems are being used. In many companies, important information resides in systems such as CRM (Customer Relationship Management), ERP (Enterprise Resource Planning), CPQ (Configure Price Quote), or PIM (Product Information Management), as well as in simple Excel spreadsheets. A complete overview of these sources forms the foundation for the next phase of integration.
2. Deepening and analyzing data sources: After all relevant data sources have been identified and located, the next step is to aggregate the information to gain a clear overview of the existing data and its origins. The following aspects should be considered:
   
   
   1. Review the identified systems (e.g. CRM, ERP, CPQ, PIM) and Excel spreadsheets for their contents. What types of data are being captured? Are there data points that exist in multiple systems? This analysis helps to understand data diversity and identify potential overlaps.
   2. Document the source of the information. For Excel exports, note which systems the data was extracted from. This allows for a better understanding of the data flows within the company and helps identify which systems provide the most current and accurate information.
   3. Determine which data source provides the most reliable and up-to-date information for specific data areas. For example, the ERP system might be the best source for financial data, while the CRM system could be the most reliable for customer information. A clearly defined "single source of truth" is essential to avoid inconsistencies in data processing and analysis.
   4. Identify data that exists in multiple systems or Excel spreadsheets. Take note of where these data points overlap and analyze why these duplicates occur. This may be due to different departments, manual data entry, or a lack of synchronization between systems.
   5. Review data quality to ensure that the existing data is current, complete, and accurate. Conduct a quality analysis to include only high-quality data in the analysis process, as low-quality data can lead to poor decisions and distorted results. Determine which data is most important for product development and performance evaluation, involving stakeholders from various departments to consider all relevant perspectives.
3. Creating a semantic model: A clearly structured data model is essential for the efficient organization and management of information. It reflects the structure of the relevant data by defining and categorizing the relationships between different data elements. By visually representing the data, team members and stakeholders can more easily understand and utilize the information.
    
   • The model serves as the foundation for future decisions and analyses by ensuring that all relevant data is accurately captured and accessible. It helps avoid redundant data and enables consistent data use throughout the organization.
   • When developing the data model, the requirements of the various departments and stakeholders should first be identified. This is followed by defining the data structure, including entities, attributes, and relationships. Finally, it is important to regularly review and adjust the model to ensure that it meets changing business requirements.
   • A well-thought-out semantic model improves data integrity, optimizes analysis processes, and supports informed decision-making in product development and performance evaluation.
4. Selection of appropriate systems: Choosing the right systems is crucial, especially considering the size of the product portfolio. For analyzing and managing large volumes of data, simple tools like Excel are often insufficient. Instead, modern visualization tools like Power BI and Tableau offer a robust solution to gain an initial overview of the data. These tools enable comprehensive data analyses and the creation of visual reports that provide valuable insights into portfolio performance.
    
   • Getting started with Power BI and Tableau: These tools are ideal for beginners as they offer user-friendly interfaces and powerful features that allow users to effectively analyze data and identify trends. However, they are not sufficient for in-depth analysis and optimization of the product portfolio.
   • Advanced solutions: For companies aiming for actual optimization of their product portfolio, specialized tools like MYNR’s solution are required. MYNR provides functions for targeted optimization of the product portfolio that go beyond the basic analytical capabilities of Power BI and Tableau. It supports decision-making through integrated data analyses and enables a detailed examination of product performance using its Product Mining technology, thus developing solid foundations for strategic actions.
   • While Power BI and Tableau are useful entry-level tools, companies with truly large portfolios that wish to sustainably optimize their product portfolio should invest in specialized solutions. Such solutions are necessary to make the required data analyses and strategic decisions based on solid evidence.
5. Iterative work and process integration: Start by developing an initial dashboard in the area of product portfolio management to gain an overview of the key metrics and performance indicators. This first dashboard should include basic information about the products, their performance, and market shares. By gathering experiences in using and analyzing the dashboard, you can actively solicit feedback from stakeholders, identify weaknesses, and gradually optimize the dashboard. In the long term, this should lead to the establishment of a solid process for managing product portfolios that is embedded in the corporate strategy. This is the only way to ensure that the company continuously benefits from a connected system landscape.

Reading recommendation: Product portfolio management with Product Mining

Connectivity is the key to success in product portfolio management

The integration of data is not just a technical issue; it is a strategic imperative for companies that want to succeed in a data-driven world. Executives and managers must actively promote this topic to ensure that their companies remain competitive in the future. Moreover, connectivity is an essential component for building effective, strategic portfolio management. Only through a connected data landscape can informed decisions be made and sustainable, forward-looking strategies developed. AI and other technologies will increasingly rely on solid data foundations in the future. (learn more: GenAI in Portfolio Management (German)) Those who do not establish this early risk falling behind in a dynamic and competitive environment.

For more insights into building a future-proof portfolio management system, we recommend the article Modern Product Portfolio Analysis for Data-Driven Portfolio Management. In it, you will learn how to establish modern data structures that enable the development of an efficient strategic portfolio management approach, allowing for informed decision-making and keeping your company on a long-term path to success.

Start today by connecting your systems - the digital transformation is not waiting!