Analytics builds the competitive advantage, part 2 – Smart Organization, BI and Data Mining

Print | Download as PDF

Information which surrounds us on every step and in every aspect of our life is the most powerful potential of the contemporary world. That is the reason why companies, which have focused on the implementation of the system of business analysis and invested in it as the most promising initiative, are increasing their operational efficiency and they are actually far ahead of their competitors on the market.

Nowadays analytics is a combination of traditional statistics, econometrics and modelling with a more modern elements like data mining, machine learning and, recently, deep learning. It is a sum total of tools, software and expert knowledge which visualise companies their objective business situation on many levels: from the realistic assessment of the existing situation, to discovering threats and opportunities, to budgeting and planning.  This allows companies to make more accurate business decisions and build the competitive advantage.

Analysis and modelling of available data, reshaping them into information and extracting knowledge from them helps to discover behavioural patterns of the company and its environment. The comparison of characteristics of mature and immature organizations in terms of data, information and knowledge management clearly shows that smart organisations build models and use their hidden potential of practical application in business.




We all care for one source

Many sources


Organized people will solve problems

Tools will solve problems


Sharing knowledge, updates

“I know everything”


Organized flow of information and knowledge

“Chinese whispers”


Information and knowledge is stored to develop wisdom in the organization

Information depends on the moment (ad hoc), knowledge is fragmentary


Easy and clear access to information and knowledge

One must wait for information and ask or knowledge


Easy and accessible knowledge – CHEAP KNOWLEDGE



Smart organizations build models

Others don’t build them at all

Most of those characteristics reflect the best practices of Business Intelligence. Basic functions of BI systems offer historical data (tables, charts, maps). However, they don't always allow for taking advantage of and presenting results of advanced analytical techniques hence data modelling (or statistical or else econometric modelling) should be treated separately.


Data Mining

The existence of BI systems allows for data mining, i.e. exploring data and extracting knowledge and information from them.  This is an analytical processes used to study available data. The ultimate goal of data mining is to make use of this knowledge in all possible ways.

Data mining means looking for and finding hidden patterns and systematic dependencies in data between different variables using the computational power of computers and statistical algorithms. It is like walking through the data and not knowing exactly where you want to go, or sometimes even without the destination. Thanks to this assumption it is possible to discover invisible and non-obvious relations. Hence a good source for such data mining is a data warehouse which stores data from different, often functionally distant systems.

So it is not just an analysis or a report made at a request. An analysis made at someone's request immediately limits the scope of data we use and the way we conduct the analysis.  It is made in a specific moment and is used for a certain, often pre-defined purpose.

So why is data mining so unique? If you ask the same questions all the time, you always get the same answers. Data mining teaches us to always ask different questions - look at reality from different perspectives. We can and we should use various systems which are not connected with one another operationally. You could try to combine data sets, for instance, on shipments from the transport systems and data about employees from the personnel and payroll system, etc. Follow your knowledge of business, analytical techniques and business cases when selecting the directions of researching data. In this manner you can obtain new knowledge, look at your work from a different perspective. Presenting the new knowledge which you had not tried to find before, you can change your approach to work and the perception of your business. And this is just the beginning of a change.

Time is essential for such data mining (it's a full-time job) so it is not always acknowledged and appreciated. However, it is the only way to learn the potential hidden in the data gathered by our systems. It is the only way to find out if there is still some uncharted territory, “undiscovered America”, and to draw up a map of data useful for the business. For today and for the future. Discovering and learning your data will also allow you to spot other issues, such as problems in processes (data are the derivatives of processes), lack of data required for efficient management, effectiveness of systems and databases. 

A ton of ore from gold-bearing rocks will bring a yield of just 2-8 grams of gold. It is not much yet gold mines are still being established. Data mining is a very accurate analogy to the work of a miner. You have to dig through mountains of data gathered and stored by your systems to extract, refine and melt them in order to find “data gold” - knowledge.

Adam Karolewski

Adam Karolewski

Genius Lab Analist

An analyst with logistics educational background. Involved in data analysis for many years. Has been working for Raben Group since January 2016. Currently the head analyst in Genius Lab, responsible for the research and development of the Group.


Learn about our services

Zbigniew Kepiński
Anthony Ranson
Nikolett Szuha
Maja Kierzek-Piotrowska
Péter Erdei
Antoni Zbytniewski
Marek Pluciak
Aleksander Kroll
Łukasz Michałowski
Věnceslav Dobrynský
Adam Karolewski
Daniel Rösch
Aleksandra Kocemba
Edyta Staszczyk
Łukasz Dubina
Anna Szymanowicz
Paweł Rymarowicz
Rafał Kukotko
Katarzyna Jaeger
Marek Zychla
Grażyna Łukasik
Marcin Turski
Monika Appolt - Bubacz
Bartłomiej Łapiński
Sławomir Rajch
Paweł Trębicki
Other articles