That data is the fuel of the digital economy is a frequently heard statement. But what does that mean for the engines, that are supposed to use this fuel, i.e. the systems that process data to move a company forward? For example, the digital machine of a company cannot fill up the necessary energy at a central gas station. Moreover, the same engine must be able to process different types of fuel, namely different types of structured and unstructured data.
The challenges this poses for companies and what the corresponding solution approaches look like can be illustrated using practical examples from e-commerce and the world of banking.
In e-commerce, more traditional data such as master data and data on ordering or accounting processes are supplemented, for example, by data that provide information about what arouses the attention of which customer at what time via which sales channel. Customers generate this additional data, which an online retailer should consider if they want to be competitive. For example, it may be relevant whether a new customer came to the company’s homepage via a comparison platform and is perhaps more price-conscious than other customers. If a customer also looks at the ratings of other customers on the homepage before making a purchase, this can be a sign of more quality awareness. Is there a point where customers often abandon the buying process?
A look at the world of finance shows that a traditional bank now has competitors on two fronts. First, big tech companies like Apple and Google are revolutionizing certain services, such as payments. It is estimated that Apple Pay is activated on more than 500 million iPhones, and according to Google Pay, it now has 150 million users in 30 countries. Secondly, neo-banks and fintech focus specifically on service modules and do not have to cover the entire spectrum of a universal bank. From the start, most competitors rely on cloud-based and modular IT landscapes. The flexible integration of third-party applications is planned directly. The traditional banks must also upgrade their data to stay competitive. They need to install new IT systems that produce additional data and merge that data with that from the existing legacy systems. In addition, the entire system must be protected against an increasing number of fraud cases, which is particularly difficult in such a heterogeneous IT landscape.
Modern analytical platforms play a central role in overcoming the challenges described. Classic relational database systems and statistics and visualization programs are usually unable to perform the required type of big data processing. New data storage and analysis approaches must be used that work in parallel on up to hundreds or thousands of processors or servers. Modern analytical platforms enable companies to implement customer-oriented, digital processes combined with intelligent data analysis. Available data pots, often scattered throughout the organization, can be brought together and the analytical possibilities expanded.
Efficient analytical platforms should be operated in the cloud to be used flexibly and be technically open so that, for example, proven and cost-effective open source solutions can also be used. For example, license fees can be saved by transferring statistical analysis software to free, open-source computer languages such as Python and R. The platform must be designed to map the entire analytical value chain. It must combine data storage, analysis and the presentation of results so that no different resources are required for these steps, but a single user can manage them with the support of the system.
In addition, an analytical platform can and should also support the use of machine learning ( ML) enable. The use of ML is not required by the sheer volume of data to be processed but by the need also to include unstructured data in the analysis. For example, ML plays an essential role in identity control in online commerce and banking, especially in advanced text and face recognition software. Appropriately trained ML algorithms can, for example, check the authenticity of the identity documents photographed by the applicant. ML algorithms for face recognition can also be used to compare the photo on the ID document with a recent selfie of the applicant and, at the same time, verify that it is a live selfie transmission of a natural face.
A robust analytical platform can be the real engine of a digital enterprise. It can tap into all relevant fuel sources and process the most diverse forms of fuel to give companies the best possible insights and the necessary overview to offer their customers the best possible service and to be able to assert themselves against their competitors in their environment.
Time To Act
It turns out that almost every company has big data, but many lack an infrastructure that is suitable for drawing comprehensive benefits from it. The realization that silos in infrastructure should be avoided whenever possible is not new. Still, it is nowhere more relevant than in data analysis, where it is precisely the relationships between data from different sources that provide the most valuable insights. Pioneer companies have already migrated to central analytical platforms in the cloud and have thus gained competitive advantages. Companies that continue to cling to traditional infrastructures fall behind analytically and miss opportunities to save costs through central processes and open source solutions – not a good combination. You should act.
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