More than ever, the ability to use data for decision-making is critical to company success. Despite this knowledge, companies are still not fully empowering their employees with easy access to the data they need. According to Zhamak Dehghani, the founder of Data Mesh, we must start thinking outside of the box because the traditional approach to managing and collecting data is not sufficient any longer.
For decades, there has been a divide between operational and analytical data with ETL as the intermediary process to get data from operational systems into the analytical data warehouse. ETL, which has always been primarily in the hands of IT developers, is perceived as a bottleneck to delivering timely analytical data. Furthermore, dimensional data models are not well suited for machine learning models that have become essential.
To overcome this, the data lake emerged around 2010. The idea of the data lake is to store vast amounts of semi-structured data in object stores to allow various consumers...
I recently completed a project where we modeled business processes for the Surveying and Mapping Authority in Slovenia and I co-authored and presented a paper about it at the Slovenian Society Informatika annual conference in April 2019. Some of the main benefits of this project that we outlined in the paper were:
Business process modeling was performed for future business processes which coerced us to think in detail about how we want to perform activities in a more streamlined, consistent and fully automated way in the future.
The completed business process models were used as input to derive specifications for a new information system that will be built to support the new processes.
Business process modeling included identifying activities and assigning actors to these activities as well as estimations of expected time spent performing each activity. This in turn was used as input for defining a new leaner and more efficient organizational structure of the Surveying and Mapping Authority.
Key...
The new general EU Data Protection Regulation (GDPR) will become a binding law at end of May this year. The aim of the regulation is to establish greater control over the handling of personal data.
As project managers, we must be aware that personal data may also be handled within projects, especially with respect to project communication and documentation as well as other areas that may be impacted. To comply with GDPR, we must be more conscious of personal data handling in project management via a more strategic and systematic approach to collecting and storing data. Informing and collecting consent from individuals about the collection and processing of their personal data may also be required where applicable by the regulation.
I co-authored the paper Project management and GDPR that was presented at the Slovenian Society Informatika annual conference in April 2018. In the paper, we state that in addition to process and awareness, project managers must also ensure that the project management software...
Recently, I was approached by a colleague who is preparing to take the PMI Agile Certified Practitioner (PMI-ACP) to share my experience. It has been almost 5 years since I passed the exam and I would probably have forgotten many of the details, were it not for this article that I wrote for the PMI Slovenia Chapter newsletter soon after the exam. I dusted off the article and am publishing it here for anyone else who is interested in the process.
When PMI first announced the agile certification, I knew immediately that I wanted to take it. I had become familiar with agile a few years earlier and I was already practicing it in my consulting work. Becoming certified in agile seemed a logical next step.
Eligibility requirements
To apply for the PMI-ACP certification, I had to meet the following eligibility requirements:
2,000 hours of general project experience working on teams.
I automatically fulfilled this requirement because I am PMP certified.
1,500 hours working on agile...
Agile software development is most often associated with small companies and small development teams. The very fact that agile teams should have no more than about a dozen members sets limitations that lead us to question the possibility of doing large enterprise data warehousing projects in an agile manner. Despite the limitations, agile can nevertheless be done in large projects. It may require more organizational change than in small companies that work on smaller projects and must coordinate more than one agile development team. -- Article published in MonitorPro magazine, 03/2016, p. 22-23...
Ever since Ralph Kimball, the guru of dimensional modeling, announced his retirement, it has felt like the end of an era. He was among the first data warehousing/business intelligence pioneers some 20 years ago and although there has been much advancement in the field since then, his dimensional modeling principles are still strongly rooted and widely used even today.
Dimensional modeling is easy to understand because it clearly represents measures that are used in business and dimensions by which we analyze them. The dimensional data model can be shared with business users which allows better alignment between the technical implementation and the intended use. However, dimensional modeling is best suited for relational or OLAP databases. In order keep up with big data trends we should examine how to extend dimensional modeling to make it fit with the latest trends. This article was first published in MonitorPro magazine, VI. 2015, p. 34-35
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Relational databases introduced data modeling concepts where we represent a data model with tables, fields and relationships. A new generation of NoSQL databases which are more suitable for big data environments no longer rely on relational data models. We have to think about data modeling in a different way, primarily to ensure that data is written quickly while sometimes sacrificing consistency. -- Article published in MonitorPro magazine, V. 2015, p. 24-25...
In data warehousing and business intelligence implementations we usually start by choosing between two most popular approaches. One of them is the enterprise normalized data warehouse approach as defined by Bill Inmon, the father of data warehousing. The second approach is a collection of dimensional data marts based on a common bus architecture as popularized by Ralph Kimball. In addition to these two we can always choose other approaches, such as a combination of the above or something completely different. An example of a different approach is the Data Vault. -- Article published in MonitorPro magazine, IV. 2015, p. 28-29...
Loading data into data warehouses, also known as the ETL process, is an established way of taking data from the source systems and bringing it into the data warehouse. The process consists of three steps: Extract data from the source systems, Transform the data so that it conforms to the data warehousing environment and finally Load it. With the proliferation of big data and Hadoop as the underlying technological platform we may have to rethink traditional approaches to loading data. The ETL process may not be the best or most efficient way of loading big data. -- Article published in MonitorPro magazine, 01/15, p. 28-29...
When we talk about Big Data, we nearly always associate Hadoop as the platform for storing huge amounts of data in a distributed environment. We often include MapReduce as the programming model for processing large data sets. Although it has been known for some time that MapReduce has limitations and is thus not universally applicable to all types of data processing, it has been the only available programming model until recently. Alternatives to MapReduce are emerging that challenge its existence in the future. -- Article published in MonitorPro magazine, 02/2015, p. 24-25...
