The Data-Driven Company – How Those Responsible Do Justice To The Importance Of Data

The new oil, the new gold, the raw material of the 21st century – when it comes to data, no superlative seems too big. Most of the information itself has no value. Those responsible have to collect, sort, process, check, structure, aggregate, and label if necessary. Competency in data processing is one of the critical competencies for companies, given the increasing importance of AI processes.

This requires – even if the popular opinion suggests otherwise – not just many data and AI applications then find something meaningful. First, you need a solid database. Only on this basis do companies gain new knowledge and offer new services. This requires organizational and cultural change. Companies must approach data processes with the same seriousness and the same conscientiousness as production, sales, or human resources processes. Only then do AI applications show their strengths.

The following explanations deal with how companies develop processes, structures, and competencies that do justice to this importance of data.

From The Triumph Of The Digital

Depending on the industry, business model, age, or culture, companies have different levels of development when it comes to handling data. For characterization, three levels of importance of data can be distinguished. These cannot be strictly separated from one another. The transitions from one level to the next are fluid. The division helps decision-makers to understand the position of their own company better.

Digital Process Step

• Indicator: Data is recorded and requested digitally; there are no media disruptions
• Consequence: The ERP or CRM applications already described are used in this phase. The predominant flow of data is digital. Contract data, product catalogs, or customer data are available in the systems and can be queried and edited.

Digital Value Chain

• Indicator: data are an integral part of management
• Consequence: Evaluation tools such as business intelligence applications analyze the data recorded in the IT system. They give decision-makers an overview of the development of key performance indicators. On this basis, that responsible plan and make decisions. Data has thus become an integral part of the corporate culture.

Data-Driven Business Model

• Indicator: Data and its utilization are the basis for services and products
• Consequence: Companies examine the potential of all available data for developing new business models and services. The data processing process chain is mainly automated. AI processes support the analysis and evaluation of the data basis.

Many companies – mainly from traditional industries – can be assigned to the “digital process step” level. But across all branches of the economy, a development towards a “data-driven business model” can be observed. There are two reasons for this: On the one hand, more and more consumers expect the seamless integration of products and services into their living environments. The car independently arranges a maintenance appointment. Or the sports shoe that measures and evaluates speed and distance. Such offers are unthinkable without digital technologies. On the other hand, more and more companies recognize the potential of closely linking the real with the digital world.

Naturally, some companies have a more challenging time with this dominance of data than others. Anyone who operates search engines, social media platforms, or music streaming services knows nothing other than data and algorithms. But anyone who builds machines from scratch has built up their skills precisely around this type of building. Data plays a role in this world – but not the headquarters. Until now, IT was used to keep existing processes running. Or to make it more efficient. But the fact that data analysis is a decisive innovation factor has yet to reach many institutions and many responsible persons. However, if you miss this point,

How can companies approach this challenge? What does a blueprint for organization, technology, and processes look like? How should those responsible design the change process? The structure of a data platform provides answers to these questions.

Data Platform – The Foundation On Which Data-Driven Companies Are Built

A data platform is a conglomerate of different technologies, processes, and functionalities that enable data to be made usable in the company. It describes the arrangement and interlinking of processes and technologies so that, in the end, new, data-based services or offers are created.

The basis for the development is a functional architecture that, detached from the specific implementation options, defines all available framework parameters and accordingly serves as the architecture and organizational basis.

Possible data sources dock below. The first task of the platform is to integrate and catalog data from different sources in different formats – whether structured, unstructured, or poly structured – so that they are visible and thus available and “orderable.” Data acquisition is the initial step in the provision of data. It allows these to be processed further in the data platform.

The integration layer transfers this data to data distribution. This means that companies can use them directly for data-driven services (for example, real-time data usage) if required. Additionally and alternatively, the data should also flow into the data provision. Its job is to save, sort, and process data. It is essential that companies keep all data in their raw form and only provide the necessary adjustments centrally for all subsequent steps. It is necessary to carefully plan the further processing of the data, for example, by curating. Information that is removed once is no longer available for other purposes. This aspect should not be underestimated in terms of complexity and importance.

The provision of data only serves to support data-driven applications, services, and products. Behind this are the individual data services that are provided on the platform. These can be forecasts with API access, data mart scenarios including visualization, or the provision of curated information to support operational business processes. This is where data and logic create new services – another crucial difference between a modern data platform and pure data warehouse systems.

To develop AI models, data is inevitable. There is a dedicated functional area for this: the “Data Lab.” The data scientists are testing new data-driven applications in laboratory mode and are developing further DDPs (Data-Driven Products) step by step. The implementation and scaling of the corresponding use cases always occur in a decentralized form to exploit the full potential and not create bottlenecks.

Users and customers benefit from the services, often integrated into existing applications and interfaces. The described value creation from data is accompanied by governance functions and a data culture that ensure the correct data handling. This includes, for example, operations such as logging, rights management, or the idea of ​​offering new services with the help of data. In addition, fault tolerance is an inherent part of the culture, as only by testing the use cases with the data can one recognize whether the corresponding use case is promising or not.

Conclusion

Data platforms are the instrument with which those responsible achieve the goal of the data-driven company. A platform built in this way is the nucleus of data processing. Processes are structured here in a similar way to an industrial production process. The reliable processes contribute to automation. Developers do not have to plan and set up the data flow from scratch for each application. You know where which data is accumulated in which format with which quality, and how it is ultimately used and presented. In this way, you can implement the necessary data provision for AI development projects faster and more reliably. A data factory can be set up in the final expansion stage that continuously creates new value-adding data services.

The result: a significantly reduced time-to-market. This and the multiple uses of data management for different DDPs is the economic advantage. It is an essential tool on the way to becoming a data-driven company.