Companies invest heavily in data-related topics such as analytics and AI (IDC). The motives for this are as diverse (IDG Research, PWC) as what data can do. Data helps to improve decisions, processes, products, services and customer satisfaction. You save time as well as costs. They promote innovation. And they boost sales because, among other things, they enable new business models and business areas. In short: data make companies winners or even big players. Scientists also speak of “The winner takes it all” syndrome. Amazon is a prime example. 1996 still a small online bookstore, today the leading internet retailer and service provider as well as an important streaming provider and logistics group.
A recent study by Fujitsu and Freeform Dynamics (FFD) shows that data-driven companies that proactively provide real-time insights across all business areas achieve far above-average business performance. Of course everyone would like that, but the way there is not without. The following best practices make implementation easier.
Depending on the desired result, you first need to determine which data is relevant at all. Then companies can check what data they already have, what they still need and how they can get this data. Whether about employees, customers, suppliers etc. or about scanners, sensors, cameras etc. It also needs to be clarified how the data must look so that it can be used at all. Data can have various formats, structured, unstructured, inconsistent or incomplete, contained in larger data sets and come from a wide variety of contexts. Which is why it is often necessary to convert, clean, enrich, extract or relate data. The better the data, the better the outcome.
In order to be able to make something out of distributed data, it must converge at the appropriate place and be accessible. No matter where they come from or where they are stored: whether in the edge, in the core or in the cloud. What is needed is a permeable architecture with integrated data management across the entire hybrid IT environment. This requires extensive integration at the hardware and software level, including seamless connection of any cloud infrastructures. With all of this, issues such as data hubs, data lakes, data warehouses, etc. are often also to be examined. In addition, workloads and performance must be taken into account. Because the data-driven business thrives on speed.
Data-driven scenarios depend on the availability and integrity of the data. The data must therefore be protected against failure, theft, corruption, manipulation and loss – along with the implementation of data type and industry-specific compliance requirements. It is best for companies to clarify the security issues when building the architecture, where they look at the entire infrastructure. Data protection can become complex. Be it because of many different data sources or also because data from clouds, mobile devices, smart products etc. offer cybercriminals numerous points of attack. In any case, the security measures must start where companies collect their data.
If you want to generate real business value from data, you need analysis methods and data science methods. Which one is best suited depends on the desired result. There are four analytical methods: Descriptive, Diagnostic, Predictive and Prescriptive Analytics (Gartner). The list of methods, however, is long and ranges from OLAP to data mining to AI varieties such as deep learning or machine learning – to name just a few. Combinations are often necessary, but not always AI. However, it is a must to put the whole topic in expert hands. It is in good hands with data scientists or people who are familiar with analytics and can also develop appropriate algorithms.
All in all, many aspects are important, lots of skills are required and many people need to be involved: in addition to company-specific stakeholders, external specialists as well as hardware, software and service providers are usually also involved. We therefore recommend a partner who manages everything and accompanies the project end-to-end, from the idea to the technical implementation, such as Fujitsu – which thanks to extensive technology and service expertise, an ecosystem with over 100 renowned providers and one specially developed 4-phase model has the prerequisites for a competent all-round partner.
With the Digital Transformation Center, Fujitsu offers a specially equipped design thinking space for co-creations. Usually stationary, DTCs are also mobile and currently even available as virtual 3D space.
At the beginning there is a discovery phase in which Fujitsu analyzes with the customer where he is, where he wants to go and what is necessary for it: from data to technologies. “Often companies do not yet know exactly what they want to do,” explains Frank Reichart, Senior Director Product Marketing for Data Center and Workplace IT at Fujitsu, and adds: “That is why we are also exploring business challenges and possible use cases. Much more specific only in the discovery phase. ” The result: a data-related transformation strategy and a baseline for the three subsequent co-creation phases related to architecture, security and procedures. These phases are about finding the best solution. To this end, Fujitsu brings its customers together with other know-how carriers. And in highly specialized workshops, special co-creation rooms and using Fujitsu’s own HXD method. This quickly leads to implementable concepts. In the past few months alone, Fujitsu has counted 250 successful co-creations.
A data-driven transformation must contribute to the general corporate strategy. In addition, data, architecture, security and procedures are closely interrelated. So each project must be viewed in the overall business context and holistically calculated and planned. Because every step has to sit so that there are no surprises. “A company told us that after extensive external advice, they were completely alone in the implementation. And the project budget was already consumed by the consulting,” reveals Reichart. That doesn’t happen with an end-to-end partner, and projects run more efficiently than with fragmentary support. For example, a French IT service provider, together with Fujitsu, transformed its entire service and support center in a data-driven manner in just six weeks. This shows that despite all the complexity, success is possible much faster than you might think.
For more information, visit the Data-Driven Transformation website.