• Christian Schitton

Digital Transformation challenges in the real estate industry - the Communication Level

Data collection level, data analytics level and data result/ communication level - that's all about digital transformation. Of course, there are several sub-layers in each level and together with the interdependencies between them it makes the digital transformation process a very complex one.


In previous articles we talked about the data collection and the analytical/ operational level. In this article we'll discuss the data result/ communication level.


Whatever the reason data are pulled through a process, there is a recipient of the results of that process who has to be in the position to work effectively with them.



To Whom Are You Talking To?


Different addressees have different motivations and therefore different demands regarding their information system.


Just to get the picture, let's compare the needs among several industries. There are distinctions in the information requirements of an autonomous-driving car, the maintenance department of a production facility or e.g. the management board of a real estate investment company. There are big variations in how the data are processed, analysed, presented and in which time frame this has to be achieved.


Take a self-driving car. Incoming data has to be made available instantly for the car to be able to react in due time. Time is of such essence that even the connection between data producing sensors and the data processing software in a cloud environment may be too slow or not stable enough. As a solution for this, a part of the software is "brought to the sensors" in order to speed up the process. We are entering the area of Edge Computing.


In this case, it is a machine-to-machine "talk". So, there are distinct requirements how data positions are built up. Above all, data shapes and formats are always within the constraints of speed.


Quant trading is another area where speed is king. In fact, all sorts of data are collected and analysed with the aim to understand early market signals. Nevertheless, the output of the whole procedure is, simply said, just a buying or selling order. Though, orders can be given in an extremely high frequency.


Talking in terms of management information systems, the addressee of a data driven risk management process could be the investment committee of an investment fund staffed by external shareholders. Here, the focus must be to present the results in a way that is conceivable, transparent and in the necessary detail for the committee members in order to support the respective case and to give a fair overview of the risks involved.


The emphasis is clearly on how results are presented, so that any committee member has a sufficient basis for decision making in a reasonable period of time. Nevertheless, the information system should be flexible enough to react on feedback questions by members on the spot and to allow smooth flow of relevant information in the digital environment.


Any digital transformation process has to reflect those circumstances.


Behind the scenes


As it was on the data collection level respectively on the data analytics level, also on the level of preparing, communicating and presenting results, there are enormous possibilities.


As well, open source environments like R or Python can be of extended help. Here, we focus on solutions within the R framework and within the range of a management information system.


First of all, the output of a data driven process can be placed in data objects like data frames, tibbles, lists, matrices or time-series frames in order to be directly accessible for the respective communication/ presentation. Of course, it can also be stored in simple files (like csv- or tsv-files), external data bases or blackboard-like pins-objects. There is a way via local (company) servers and there are cloud-based solutions as well.


For a machine-to-machine communication, json-files within RestAPI - solutions are a fast fix. In this context, the R-package plumber in combination with the SwaggerAPI is a very productive solution.



Though, with respect to management information systems it is much more interesting what kind of dashboard and report solutions are available in order to make the life of our addressees more convenient.



Dashboard Solutions


Dashboards are a great way to include a number of participants and let them interact with each other respectively to work on tasks within a platform. The scope of user cases goes from a pure informational approach:




to more interactive applications:



and in case of need, ending up with portfolio simulations as a predictive analytics model:



However, sometimes it is just a simple table breakdown to serve business analytics tasks:


This system is automated, with possibility of an interactive operation in order to:

  • allow the decision makers as well as teams to get the quality information and analytics in a fast and efficient way,

  • to allow digital data driven decision making in everyday business.

One of the major advantages is that the system is also scalable. Once the data gathering and data analysing levels are connected and functioning, unlimited options spread out. It is possible to expand the use to other entities, like daughter companies resp. international branches, to keep the system on the C-suite level or to integrate extended teams and departments.


Applications are updated on an automated basis and are accessible 24/7. This is fast and practical and brings a whole range of advantages such as saving time and human resources, having the chance to keep active control over scattered portfolios (and avoiding the need for excessive traveling) as well as to cover transparency requirements, especially in terms of ESG standards.



Report Solutions


From there it is a small step to pull written reports for different requirements.

Although the goal is to switch to a whole new level of communication - i.e. a digital one above everything - there are many occasions where written reports have to be created and documented.


So, as a part of the results/ communication level, paper reports can be drawn on a stand-alone basis or in combination with a dashboard application. Reports can be prepared in an automated or semi-automated way. This solely depends on the requirements of the addressee of the report.


An important point is that any result coming from the analytical level can be incorporated in a report automatically without any manual manipulation.


This maintains the connection between the reporting level and the analytical level. So, any change in the analytics is mapped instantly in the respective report.


And all this, can be achieved just by the push of a button:


In order to create exactly those reports an addressee needs to work effectively.

 



 

It is a very convenient way to build up a reporting system within a company. And again, this goes automated, it is highly scalable and the reports are updated and accessible 24/7.



Final Thoughts


One of the main reasons many digital solutions, especially standardised ones, are not used by companies can be found in very complicated systems and in bad user experiences. Inconsistencies in the data gathering and analytics level resulting in often unreliable and insufficient outcomes as well as the inability to create a lean and minimalistic management information system for a practical use in day-to-day operations just fuel the overall problem.


The main challenge in the result/ communication stage should be to allow decision makers and other relevant team members to do their business better and easier compared to existing frameworks. This will help to drive an efficient digital transformation process, while keeping the communication transparent, efficient and fast.