The digital workplace is intended to provide intelligent, needs-based support for the user. The right information at the right time in the right form requires good analytics – targeted evaluations, presentations, and options for action. At the same time, the digital workplace is a valuable source for analysis to optimize business processes and strategic decisions.
When it comes to the interaction between Analytics and the Digital Workplace, a distinction can be made between different use cases and tool classes:
- Knowledge generation in the background: acquisition, processing, and evaluation of information to generate relevant content and relationships
- Provision of information for internal business processes and external communication: visualization of data, e.g., via dashboards, so that employees can make decisions internally
- Use of information to carry out automated actions: Application of the recognized rules and patterns to derive direct actions from the incoming data and also to have steps carried out automatically
Forms Of Knowledge Generation In Preparation For Use
Analytics is often equated with knowledge generation. Data analytics can prepare, transform and aggregate information to gain valuable information to support decision-making. The tools enable the connection of different data sources and thus create comprehensive consolidations and evaluations.
The recognition of patterns and contexts is also an application scenario, for example, to understand decision-making processes or to identify classes of use cases. Knowledge about processes, e.g Customer interaction, purchasing behavior, or internal areas of responsibility, can be recognized, and the knowledge can be made available through analysis and operationalization through special tools. Examples are the generation of recommendations (“other customers also bought”) or actions (next best action: how should one react in customer communication?).
Such analyzes also offer the possibility of identifying errors or bottlenecks. The search for time and cost wasters in projects, deviations from standard values, special influencing factors, or special risks are just some of the use cases. Here, too, the analyzes mostly offer further context and expanded evaluation options to investigate further and thus understand the identified relationships. In this way, targeted improvements or countermeasures can be carried out.
More Than Just Structured Data – Holistic Analysis
In addition to analyzing classic relational data, many other tools support other forms of investigation.
Machine data / IoT are moving more into focus as a further source of information in many areas. There are various special systems for social media (social listening, etc.) to identify trends early and play out your content in a targeted manner.
Text Analytics enables the examination of documents and unstructured information. Depending on the tool, the content itself, and other context information (e.g., filing hierarchies, the structure of the documents) can be examined. Semantic analyses allow language and tonality to be included, e.g., whether an email from a customer contains clear indications of their annoyance. Other special systems analyze the image and video data.
Another application class is process mining. Here, processes can also be traced across different system boundaries and evaluated according to different criteria, e.g., throughput time, costs, waiting times, or the number of repetitions of certain steps. In this way, deviations from the norm can be identified, and targeted research into the causes can be carried out. Various systems also allow the simulation of changed processes. In addition to pure optimization, risk management is also an essential factor here. Not only the lead time and the costs of a process are decisive, but also whether all legal, contractual, and organizational guidelines are adhered to during its processing (compliance).
Visualization And Work Equipment – Act Informed.
The relationships created in this way or identified data and key figures can be displayed via reports and dashboards. The respective employee: receives the essential connections to a customer: in / project/sales area/production section etc., and can derive actions from the content shown.
In addition to clearly defined reports, dashboards with interaction options are an essential tool, as the user can work directly with the data here to display details or, for example, to segment and condense them differently.
Automated Processing And Use
Analysis tools offer many options for collecting, checking, and summarizing incoming data. In this way, the relevant information for the respective application can be filtered from a large number of available information. The data is often transformed or prepared in the form that the user needs: directly in his specialist application or dashboard.
Using appropriate models and rules, the data can also be used automatically, in that incoming content either trigger actions directly or by processing and collecting it to trigger a follow-up action when certain threshold values or scores are reached. Depending on the coupled specialist systems, such actions can range from a simple notification to the automatic display of content and the execution of workflows.
Digital Workplace As a Knowledge Target
The mentioned aspects clarify the importance of analytics for the digital workplace: Employees should make decisions quickly and flexibly based on correct and relevant data. The preparation must be based on the application – the analyzes, therefore, provide direct added value for the respective task of the user.
Individual values and key figures and corresponding reports and dashboards can therefore be displayed in the Digital Workplace. The corresponding special systems are often used for more extensive analyzes.
The Digital Workplace Is a Source of Knowledge.
As the processing of internal tasks and customer-related activities is brought together by the Digital Workplace, the Digital Workplace is also a relevant source of knowledge. The information used here is (implicitly) evaluated by the user and placed in an application context, providing valuable knowledge for future analyses.
Organizational Potential – Linking The Analysis And Operationalization Perspectives
Therefore, the digital workplace is a good means of connecting different business processes and user groups. The analytics specialists get a better picture of the users’ requirements: inside for analyzes and process support. The users can work better with the information and tools because a better context is provided for their use: the dashboards, reports, and self-service applications relate to specific use cases such as the design of sales campaigns or the measurement of business processes.
This supports various current requirements and trends. Analytics specialists in the individual departments support more applications, e.g., analyses for campaign planning in customer communication. The trend towards self-service analytics is also promoted by the context of the digital workplace, while the corresponding functions offer more options for processing and using the data.
At the same time, the possibilities and fields of activity for analyzes are increasing. Data science is an example here. The combination of technical skills, tools, information, and technical expertise is also important to use the knowledge gained. The organizational side of the Digital Workplace helps bring together and use the right information.
Strengthen Your Analytics Projects
Good analytics projects have clear goals and added value. The Digital Workplace is a good means of bringing analytics into a clear application context and including many user groups as possible users of corresponding data and analysis. Analytics will thus be anchored even more broadly and the added value of the corresponding initiatives highlighted.
More and more analytics projects are characterized by the interaction of data specialists and subject matter experts from the departments. The Digital Workplace offers a common basis and the context of which data is to be collected, processed, and used for which purpose and in which form. Questions about data quality and responsibility can also be clarified directly based on the specific tasks.
Good, targeted data is a key success factor for digital workplace experts. Therefore, it is important to connect the relevant initiatives and experts to combine the best of the different worlds.
Also Read: How To Organize The Digital Workplace