Coming soon

Some posts that are coming:

Converting Dynamic Technical Data to Business Value also in a Regulatory Context

Diverse and frequently changing data sources can be synthesized and transformed into consistent, multiple data packages targeting different end-user profiles. Data packages can and should include transparency, tracking and data-quality information. The problem is of course the word "can".

But also understanding "why" is a problem. ABC. This is really a high level business concern.

This transformation is critical if technical data will be used by owners, investors, tenants, and users. Regulatory drivers like ESG compliance would be positioned as a core value drivers for making this transformation and for garanteeing transparency of transformed data.

The focus would be on developing flexible transformation frameworks that incorporate both static (physical) and dynamic (behavioral, environmental) data.

Key bonus results would include the possibility of machine learning use for example in predictive analysis of user behavior or climate optimization and ESG-driven metrics to meet compliance needs. Without the need for detailed programming to get the needed information.

Getting more from the BMS that you are already paying for -> Improve functional descriptions in early phases of building construction or transformation

Most buildings have a building management system. It controls the indoor environment, lighting etc. This system has a load of information that we can utilize in compliance reporting and optimize on many levels.

Getting data out of the BMS is difficult. Not because we do not have the technology but we choose to implement solution that do not look broader than the primary basic needs of the building. But understand it is not as an increased cost, but by defining and organizing the BMS at the onset that is the problem.

We can have high data quality and also have low data trust

The problem is that the word “data” means something different if you are gathering it or if you have to use it.

You can have a high technical governance and handling of data but the conversion to business data needs to be documented, transparent and aligned to needs to gain trust.

Technical data ≠ Business data even if it seems like it.

Business-functions based on technical data = Business data

The function needs to be transparent and documented for use at the business end.

Business data users are data illiterates. Business data users expect technical resources to be competent in business needs and therefore do not invest in the information flow which causes wrongly used data and erroneous assumptions. Naming conventions created by technical resources enhance this assumption.

Do not blind yourself and think data sharing is just about connecting to data

This article is not an IT-nerd article. It is for people wishing to use information gathered at all levels of business. If you are an IT-nerd you could learn a lot, but this article is for your business bosses giving you your goals.

Now lets jump in. Besides connecting to data — which is a big win in itself as it is not always just straight forward — data must also be organized, understood and usable.

Handling employees having multiple employers

In this case it is not about HR problems. It is about being a small employer and needing few hours a week on different technical and data related issues. If all the jobs could be bundled with one person all would be alright. But we can´nt.

BYOD, systems, office space integration also is very relevant.

The lack of quality, structure and definitions of data

The lack of standards and not knowing it. And not knowing that you need to up your understanding of data if you are to get the most from your investments, if you even get what you need.

The Art of Data Modelling a Building — Creating Organized Information

The largest part of our lives is spent indoors, in buildings. Surprisingly buildings adapt very little to how we use them. More surprising, however, is that we accept and feel that this is the natural state.

This doesn´t have to be the case and we can significantly enhance our climate and economical investments if we learn to better organize and integrate data within buildings.

The unstructured data stack will emerge (unfortunately).

The Growing Need for Data Quality in an Increasingly Complex Data Landscape

In today’s digital economy, poor data quality has emerged as one of the most prevalent challenges. As data-related complexity increases, maintaining quality has become an even greater struggle for teams.

Pipelines are expanding — but quality coverage isn’t

Already flagged as a concern in previous years, the problem has escalated significantly. In fact, 57% of data practitioners now highlight data quality as one of their chief obstacles when preparing data for analysis — up from 41% in prior reports. (It’s important to note that respondents were asked to select three main challenges, so percentages exceed 100%.)

Structuring data in a built context will often fail but doesn´t have to

Well not only in a build context, but this is one of the domains that has some problems in organizing data and information. Not the physical building because there is many structures in place there, but usage, the people, consumption etc.

Typically data is not coordinated and put into a mutual context whereby data is often siloed or organized en ad-hoc manners. Costs increase, confusion arizes and the whole idea of coordination is droped.

We lose an oportunity for cost reduction and insights that could increase our organization resiliens.

Collecting Data in an Evolving Multi-Source Environment

Using traditionel methods the job is never done and costs will just skyrocket if trying to harmonize information comming from diverse sources. But not harmonizing data but collecting data and placing these diverse structures on a mutual framework can solve many problem. A sematical approach for handling information.

Data and information illiteracy — the hidden organizational disease

I believe significant challenges occure when individuals or organizations attempt to make decisions or draw conclusions based on data. Some of these challenges occure because of data illiteracy often without knowing it.

But organizations and individuals can more effectively harness the potential of data while avoiding the pitfalls of misuse or misinterpretation if understanding this challenge of illiteracy.

Why are digitalization projects always under estimated?

Let me be more precise on the context. Everyone uses estimation. How long does our trip to our aunt take or is it reasonable that painting our garage takes two days. Problems arise when we use our gut-estimates as guidelines for how reasonable a project cost is for projects we really have no experieces with.