Watts S. Humphrey stated more than two decades ago that “Every Business is a Software Business”. It’s about time to rephrase this statement. Whether a company is a traditional brick-and-mortar retailer, a software firm, or a service provider, it generates and collects data from various sources, such as customer interactions, sales transactions, and operational processes.
From Software Business to Data Business
Because in today’s digital age, data is an essential resource for making informed business decisions, achieving operational efficiency and customer satisfaction. This means that data management, usage, analysis, and interpretation will become increasingly critical for businesses to remain competitive and relevant. AI offers new business models and disrupts existing products. This decade is about data.
Natural Language Processing currently disrupts the way we search on the internet and gives Mircosoft a new shot for its Bing search. The question of the potential for the use of data has arrived in society at large: Generative AI, such as OpenAI’s recently published chatbot ChatGPT and text-to-image generator Dall-E, are fueling the public debate in the beginning of 2023. Companies of all sizes are exploring what the possibilities of artificial intelligence and machine learning mean for their own business model. This competitive pressure and societal metrification have long since reached the stage where it is said: “Every Company is a Data Company.”
Data as Competitive Advantage
We see a gap that is widening: Companies that are able to exploit the value of data and those that continue to struggle with technological and organisational change and that lose touch. The Massachusetts Institute of Technology (MIT) calls this “The New Digitial Divide”, describing the gap that exists between companies that know how to use data and manage to leverage and those companies that struggle.
Companies only differ in the degree to which data influences their business model and how much data use represents a strategic competitive advantage over their competitors. Companies such as Netflix, Uber or Spotify have managed to create a competitive advantage with data as a central part of their product (so-called data products) and disrupt their own business model. Data-driven companies manage to deliver their core service more efficiently in terms of costs and sustainability issues. Predictive applications are becoming increasingly important.
Customer Centricity is Key
This raises the question of how far companies are prepared for this era of data use. From my experience, I can say: young companies and start-ups have less difficulty (see Netflix etc.). They do not carry “historical debt” (e.g., outdated data infrastructure and applications), but they establish their business model around their customers, align their organisation to this from the start and support this with modern state-of-the-art technologies. They have the advantage of starting on a green field. Traditional companies, on the other hand, struggle with data silos, outdated data architectures and an organisation that makes it difficult to use data across the organisation and share it between business units.
Democratization of Data
Therefore, we at diconium strongly believe that a new basis for data management is needed, where data responsibility is decentralized in the (functional) teams, in order to meet the “new” requirements regarding data usage and its potential.
Each organizational unit is responsible for its generated data and via common standards this data is made available to other parts of the company for use (“data-as-product”). This creates collaboration and exchange. The Data Management concept Data Mesh plays a central role in this process, focusing on the interplay between organizations and responsibility, product thinking and data architectures.