Data can make the difference between services that only shine on paper and customer-oriented services that really add value. Companies today have access to a vast amount of data that is constantly growing. Mankind generates an estimated 2.5 quintillion bytes of data every day, 90% of which have only been added in the last two years. This poses massive challenges for organizations. The mere hoarding of data does not help. When used correctly, however, this data has incredible optimization potential and can have an extremely positive influence on company decisions.
Source: Inside Big Data
Take agriculture, for example. It is actually a sector that is very far removed from technology. But a few years ago the Economy of Things also moved into the fields. Digital tracking of environmental influences, crop yields and resource input suddenly allowed conclusions to be drawn about agricultural decisions that optimized harvests. Manufacturing companies like John Deere became service providers licensing farming software and IT systems.
Data can therefore create services and fill them with relevant content. In addition, they are able to support price definition and define customer-specific offer and price packages. Furthermore, they can optimize the marketing and sales process and enable personalized communication.
Data is a powerful accomplice to improve business performance. But the hard part is collecting the right data and performing targeted data Analysis.
Typical pitfalls in data analysis
Too Technology-focused. Unfortunately, data analyses are often considered purely technical. According to the motto: the data can be collected, so we collect them. A lot is invested in appropriate IT systems that are highly potent. Without a business perspective, however, the potential of the data will not unfold. They must clearly be used for certain entrepreneurial goals and questions in order to be of value.
Too Fast. In order not to lose the technological edge, many companies act too quickly and too headlessly when it comes to new IT. Strategic considerations are not taken seriously enough and there is no holistic consideration of where the technology must be used in order to provide real support.
Too Fragmented. Even if a large number of different cloud-based IT systems are necessary to bring the economy of things into the company, they must all work towards the same objectives and be based on the same database (customer data, product data, etc.). Often, however, the systems run in parallel and are not synchronized enough.
Core questions of data analysis in the Economy of Things
Data analysis is the foundation of the Economy of Things
Source: iq! Management Consulting
What is done with the data analysis? Although this question describes the result of the data analysis, it should be answered first. The goals and desired applications of the data are the be-all and end-all to define the process of data collection.
Where does the data come from? You can then define which of the available data pots must be tapped.
How is the data transferred? Data is transmitted in many different ways. Technical interfaces, partner data records, digital forms or websites are just some of the possibilities. The different interfaces must nevertheless provide uniform data sets.
How is the data stored? The next step is to transfer the data to the correct data pots, e.g. to the customer database, the product usage database, etc.
How is the data analyzed? In addition to the question of what the data should be used for, this is the biggest challenge: Finding the right analysis questions and performing the appropriate analyses is extremely challenging.
Customer Facing IT for the Economy of Things
An important learning out of many iq! projects is that the IT to be restructured for the Economy of Things is best set up parallel to corporate IT. We call it “Customer IT”. Integration into the broad branches of corporate IT is almost hopeless. As a lean, separate IT organization, it can quickly develop its full potential. For all necessary “Cloud” modules, e.g. Marketing, Commerce, IoT, etc., there is now a selection of specialized providers who also have good interfaces and integration options to each other.
Ideal model for Customer facing IT
Source: iq! Management Consulting
Data and the right analyses are the breeding ground on which meaningful digital products and service offerings can really grow. Separate customer IT from the often cumbersome corporate IT is much easier to use. There are enough specialized providers. However, IT should not be set up in a hurry. It must be precisely tailored to the goals and use cases.