In his blog, Werner Vogels, chief technology officer of Amazon.com, looks into the art of rethinking the production of data.
Strategically, IT doesn’t matter. That was the provocative thesis of a much-talked-about article from 2003 in the Harvard Business Review by the US publicist Nicolas Carr. Back then, companies spent more than half of their entire investment for their IT, in a non-differentiating way. In a world in which tools are equally accessible for every company, they wouldn’t offer any competitive advantage — so went the argument.
The author recommended steering investments toward strategically relevant resources instead. In the years that followed, many companies outsourced their IT activities because they no longer regarded them as being part of the core business.
A new age
Nearly 15 years later, the situation has changed. In today’s era of global digitalization there are many examples that show that IT does matter. Developments like cloud computing, the internet of things, artificial intelligence, and machine learning are proving that IT has (again) become a strategic business driver. This is transforming the way companies offer products and services to their customers today.
Take the example of industrial manufacturing: in prototyping, drafts for technologically complex products are no longer physically produced; rather, their characteristics can be tested in a purely virtual fashion at every location across the globe by using simulations. The German startup SimScale makes use of this trend.
The founders had noticed that in many companies, product designers worked in a very detached manner from the rest of production. The SimScale platform can be accessed through a normal web browser. In this way, designers are part of an ecosystem in which the functionalities of simulations, data and people come together, enabling them to develop better products faster.
Value-added services are also playing an increasingly important role for both companies and their customers. For example, Kärcher, the maker of cleaning technologies, manages its entire fleet through the cloud solution “Kärcher Fleet”. This transmits data from the company’s cleaning devices e.g. about the status of maintenance and loading, when the machines are used, and where the machines are located. The benefit for customers: Authorized users can view this data and therefore manage their inventories across different sites, making the maintenance processes much more efficient.
Kärcher benefits as well: By developing this service, the company gets exact insight into how the machines are actually used by its customers. By knowing this, Kärcher can generate new top-line revenue in the form of subscription models for its analysis portal.
More than mere support
These examples underline that the purpose of software today is not solely to support business processes, but that software solutions have broadly become an essential element in multiple business areas. This starts with integrated platforms that can manage all activities, from market research to production to logistics. Today, IT is the foundation of digital business models, and therefore has a value-added role in and of itself.
That can be seen when sales people, for example, interact with their customers in online shops or via mobile apps. Marketers use big data and artificial intelligence to find out more about the future needs of their customers.
Breuninger, a fashion department store chain steeped in tradition, has recognized this and relies on a self-developed e-commerce platform in the AWS Cloud.
Breuninger uses modern templates for software development, such as Self-Contained Systems (SCS), so that it can increase the speed of software development with agile and autonomous teams and quickly test new features. Each team acts according to the principle: “You build it, you run it”. Hence, the teams are themselves responsible for the productive operation of the software. The advantage of this approach is that when designing new applications, there is already a focus on the operating aspects.
Value creation through data
In a digital economy, data are at the core of value creation, whereas physical assets are losing their significance in business models. Until 1992, the most highly valued companies in the S&P 500 Index were those that made or distributed things (for example the pharmaceutical industry, trade).
Today, developers of technology (for example medical technology, software) and platform operators (social media enablers, credit card companies) are at the top. Also, trade with data contributes more to global growth than trade with goods. Therefore, IT has never been more important for strategy than it is now — not only for us, but for every company in the digital age. Anyone who wants to further develop his business digitally can’t do that today without at the same time thinking about which IT infrastructure, which software and which algorithms he needs in order to achieve his plans.
If data take center stage then companies must learn how to create added value out of it — namely by combining the data they own with external data sources and by using modern, automated analytics processes. This is done through software and IT services that are delivered through software APIs.
Companies that want to become successful and innovative digital players need to get better at building software solutions.We should ponder how we can organize the ‘production’ of data in such a way so that we ultimately come out with a competitive advantage. We need mechanisms that enable the mass production of data using software and hardware capabilities.
These mechanisms need to be lean, seamless and effective. At the same time, we need to ensure that quality requirements can be met. Those are exactly the challenges that were solved for physical goods through the industrialization of manufacturing processes.
A company that wants to industrialize ‘software production’ needs to find ideas on how to achieve the same kind of lean and qualitatively first-class mass production that has already occurred for industrial goods. And inevitably, the first place to look will be lean production approaches such as Kanban and Kaizen, or total quality management.
In the 1980s, companies like Toyota revolutionized the production process by reengineering the entire organization and focusing the company on similar principles. Creating those conditions, both from an organizational and IT- standpoint, is one of the biggest challenges that companies face in the digital age.
Learn from lean
Can we transfer this success model to IT as well? The answer is yes. In the digital world, it is decisive to activate data-centric processes and continuously improve them. Thus, any obstacles that stand in the way of experimentation and the further development of new ideas should be removed as fast as possible.
Every new IT project should be regarded as an idea that must go through a data factory — a fully equipped production site with common processes that can be easily maintained. The end-product is high-quality services or algorithms that support digital business models.
Digital companies differentiate themselves through their ideas, data and customer relationships. Those that find a functioning digital business model the fastest will have a competitive edge. Above all, the barrier between software development and the operating business has to be overcome. The reason is that the success and speed and frequency of these experiments depend on the performance of IT development, and at the same time on the relevance of the solutions for business operations.
Autoscout24 has gained an enormous amount of agility through its cloud solution. The company meanwhile has 15 autonomous interdisciplinary teams working constantly to test and explore new services. The main goal in all this is to have the possibility to quickly iterate experiments through the widest range of architectures, combine services with each other, and compare approaches.
In order to become as agile as Autoscout24, companies need a “machine” that produces ideas. Why not transfer the success formulas from industrial manufacturing and the principles of quality management to the creation of software?
German industrial companies in particular possess a manufacturing excellence that has been built up over many decades. Where applicable, they should do their best to transfer this knowledge to their IT, and in particular to their software development.
In many companies, internal IT knowhow has not developed fast enough in the last few years — quite contrary to the technological possibilities. Customers provide feedback online immediately after their purchase. Real-time analyses are possible through big data and software updates are generated daily through the cloud.
Often, the IT organization and its associated processes couldn’t keep up. As a consequence, specialist departments with the structures of yesterday are supposed to fulfill customer requirements of tomorrow. Bringing innovative products and services quickly to market is not possible with long-term IT sourcing cycles. It’s no wonder that many of specialist departments try to circumvent their own IT department, for example by shifting activities to the cloud, which offers many powerful IT building blocks through easy-to-use APIs for which companies previously had to operate complicated software and infrastructure. Such a decentralized ‘shadow IT’ delivers no improvements.
The end effect is that the complexity of the system increases, which is not efficient. This pattern should be broken. Development and Operations need to work hand in hand instead of working sequentially after each other, as in the old world. And ideally, this should be done in many projects running parallel. Under the heading of DevOps — the combination of “Development and Operations” — IT guru Gene Kim has described the core characteristics of this machinery.
Ensuring the flow
Kim argues that the organization must be built around the customer benefit and that the flow of projects must be as smooth as possible. Hurdles that block the creation of client benefits should be identified and removed. At Amazon this starts by staffing projects with cross-functional and interdisciplinary teams as a rule.
Furthermore, for the sake of agility the teams should not exceed a certain size. We have a rule that teams should be exactly the size that allows everyone to feel full after eating two (large!) pizzas. This approach reduces the number of necessary handovers, increases responsibility, and allows the team to provide customers with software faster.
The earlier client feedback flows back into the “production process”, the better. In addition, companies must ensure that every piece of feedback is applied to future projects. To avoid getting lost in endless feedback loops, this should be organized in a lean way: Obtaining the feedback of internal and external stakeholders must by no means hamper the development process.
Learning to take risks
“Good intentions never work, you need good mechanisms to make anything happen,” says Jeff Bezos. For that, you need a corporate culture that teaches employees to experiment constantly and deliver. With every new experiment, one should risk yet another small step forward behind the previous step. At the same time, from every team we need data based on predefined KPIs about the impact of the experiments. And we need to establish mechanisms that take effect immediately if we go too far or if something goes wrong, for example if the solution never reached the customer.
Anyone who has tried this knows it’s not easy to start your own digital revolution in the company and keep the momentum going. P3 advises cellular operators and offers its customers access to data that provide information about the quality of cellular networks (for example signal strength, broken connection and the data throughput) — worldwide and independent of the network operator and cellular provider. This allows the customers to come up with measures in order to expand their networks or new offerings for a more efficient utilization of their capacity.
By introducing DevOps tools, P3 can define an automated process that implements the required compute infrastructure in the AWS Cloud and deploys project-specific software packages with the push of a button. Moreover, the process definition can be revised by developers, the business or data scientists at any time, for example in order to develop new regions, add analytics software or implement new AWS services. Now P3 can focus fully on its core competence, namely developing its proprietary software.
Data scientists can use their freed-up resources to analyze in real time data that are collected from around the world and put insights from the analysis at the disposal of their clients
The cloud offers IT limitless possibilities on the technical side, from which new opportunities have been born. But it’s becoming ever clearer what is required in order to make use of these opportunities. Technologies change faster than people. And individuals faster than entire organizations. Tackling these challenges is a strategic necessity. Changing the organization is the next bottleneck on the way to becoming a digital champion.