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As a customer – no matter which product category, be it fashion, convenience or perhaps a piece of furniture – we are used to digital offers being tailored to our needs. Information that dealers provide to us is, in most cases, highly personalised and adapted as soon as something changes with us. In the background, algorithms process all the data and information they receive about us; and if an algorithm has understood that we have decided to buy a new winter coat, ideally offers for the matching scarves and hats are displayed next.
This matches our lives as consumers in the digital age. For a business leader in the midst of digital transformation, however, the world looks different. His control systems, on the basis of which he makes strategic decisions for his company, still follow the paradigms of the transactional age: structured data is aggregated and provided for use in a standardised format.
Digitisation means that not only structured, but also unstructured data is generated, a state for which traditional control systems are not designed. They are neither suitable for the collection of such data quantities, nor are they able to analyse and prepare the data in such a way that management can draw suitable conclusions for their business from them: Instead of the required real-time information, they only receive standard data. Decision-makers therefore lack control mechanisms and monitoring tools to guide their companies through the digital transformation.
In the digital cosmos, control systems require a high degree of automation for company management, which allows the business level to really focus on the important questions of the business and to create transparency about the performance of the company. To achieve this, companies must open up to the application of new technological possibilities such as data lakes, machine learning (ML) and artificial intelligence (AI), and integrate these into their control systems. This results in digital analytical assistants that can actively inform you about changes or irregularities in company data via KPI dashboards that can be personalised. With the help of real-time data, it is possible to react more quickly to problems, and the support provided by ML and AI makes it possible for systems to search the data for answers to specific questions or to compile entire dossiers on a topic and ultimately even identify patterns that can be converted into recommendations for action.
Digital analytical assistants thus provide management with an overview that is based exclusively on the relevant data. Where, in the past, standardised analyses were displayed that were not prepared for structural changes, nowadays algorithms should be used that detect and evaluate changes and irregularities in the data in real time.
One example of how modern dashboards can serve as personal analytical assistants is Pulse - a solution from TD Reply that helps business leaders make better, more informed decisions. A data-driven KPI evaluation with KPI effect modelling allows the creation of a data set tailored to the needs of each user. All defined KPIs are set up in the Pulse dashboard for regular and automatic reporting. If something changes while the user is not on site, an automatic Smart Data Assistant notifies him by e-mail. At the same time, a text-based chatbot ensures that decision-makers are informed as simply and in as user-friendly a way possible about relevant changes in the data.
The recording of data at high frequencies requires automated tracking and measurement of all relevant online and offline data points. The Pulse Dashboard simplifies the integration of standard data sources and custom sources - including Facebook, Twitter, Google Analytics, Google Search, Brandwatch, Sprinklr, internal CRM systems or existing sales data. The user is therefore comprehensively informed across all channels. The aim here is not only to provide descriptive information on current developments, but also to use mature statistical models to predict developments in the data and/or the overall business performance, and thus to derive relevant recommendations for action at an early stage.
Another example is SAP's CoPilot, a chatbot and dashboard solution that already comes very close to digital assistants like Siri or Alexa: Just as we, as customers, order next week's shopping via Alexa, or ask Siri about our upcoming appointments, corporate decision-makers can also communicate with the CoPilot via voice input. The tool also aims to simplify the preparation of KPIs and their communication, which is primarily supported by the intuitive operation via speech, which is possible in addition to the usual text input. This solution enables transactions to be executed via SAP solutions without leaving the current context or conversation. What's more, there is no need to leave a running conversation in order to share notes, screenshots or other elements. Another feature of the SAP CoPilot Skill Builder is the ability to create additional, individual skills for the digital assistant.
Digitisation is really happening and is advancing in every business. A lot of experimentation is going and many initiatives are being started. New digital channels to the customer are opened up. Companies have new data at their disposal, whose value needs to be unlocked - a necessity that cannot be achieved without appropriate tools.