Business intelligence helps start-ups succeed
Henri Remes
Master of Engineering graduated in 2016
Head of Operations Support, Eniram Oy
henri.remes [at] eniram.fi (henri[dot]remes[at]eniram[dot]fi)
LinkedIn
My thesis (1) was conducted for Eniram, a marine business company I work for and which was established in 2005. During the past 13 years, the company has expanded from a start-up to a successful international middle-sized enterprise. As a start-up, the company gathered many different tools and systems for its different functions and roles.
From Excel files to well-structured BI
In the beginning, data and analyses (measurements, forecasts, reports) gathered from the internal functions and roles in the company played the role of ‘business intelligence’ (BI) - but they were reported separately to the CEO* who then had to consolidate them to make sense of the big picture. All these data streams were not automated or integrated from the existing systems, so the vital reports were created manually and compiled in Excel sheets and Word files.
For effective reporting, a good reporting tool was needed so that it would bring all internal reporting together systematically. In the thesis, I strived to merge all these different functions and data streams together for effective reporting. When the outline of the solution was clear, it evolved into the discussion of how to get this tool – develop it internally ourselves, or buy it from somewhere. The thesis managed to resolve the problem regarding the choice between optimizing an internal existing tool or relying on an external provider. It made a clear proposal to the company based on the comparison of the internal tool and one selected external solution. The company adopted the proposal and implemented it.
Good BI is hot, but also challenging for start-ups
What are internal customers in a company looking for in its own data? They are looking for business-critical information by analyzing both internal and external data. (2). Business Intelligence (BI) gives a good view to both sides. From the internal data, the company knows what it is doing and how effectively, and how the projects are doing. From the external data, the company can reflect its situation against the market and plan effectively. It can also learn the cost structure, KPIs*, and so forth. This is common usage for BI tools (3).
Reflecting on how your KPIs look against the global environment is something new and hot for a ‘start-up-oriented’ growing business. This is a field where small players are typically helpless and left out, as it is too expensive for smaller companies, and minor improvements in systems and software can hardly help. Yet, in my thesis, a BI tool was developed by a small company for a small company to help adapt to the needs of internal and partially external customers, and it was successful. This makes the outcome significant and, in a way, unique.
Appropriate BI makes changes smoother
The thesis showed that it was possible to grow as a business that takes its internal data seriously. Moreover, as a business, it was critical to have an ecosystem that integrates all data into a single point of output.
The importance of the outcome was noticed especially after Eniram was bought by Wärtsilä during the summer of 2016, right after my graduation. Wärtsilä has its own SAP system which is very hierarchical and stiff in terms of adapting into new businesses like Eniram. All the preliminary work done for the thesis - harmonizing data, unifying terminologies, figuring out the reporting functionality – all these efforts paved the way to a successful integration plan with Wärtsilä’s SAP system. As a result, we were able to put the reporting tool into truly effective use while planning to integrate with Wärtsilä‘s systems.
Leadership is a keyword for future
When I learnt that my thesis was downloaded from Theseus more than 4000 times, I thought that maybe it was due to the topic and keywords that were applied. As it turns out, BI is a key driver of change in businesses and it is highly searched on the Internet. The growing importance of business intelligence is evident in any business (4). Many companies have started digitalization, but many have also failed on that road. In the era of digitalization, business intelligence, artificial intelligence (AI), machine learning, and similar topics are becoming ever more important.
The future will be closely related to these innovations. Nowadays, development is going more towards technological solutions – cloud, digital twins, solutions for more transparency and more integration. But the practicalities for them, the actual ‘hard stuff’, will enter the arena with a delay. In big traditional companies especially, there may be different business lines that cannot immediately adapt to the new ways. Therefore, creating ecosystems due to these hindrances will be quite problematic. In marine business, which is a very traditional industry, there are early adopters who are open to new ideas and new solutions can be taken into use. But there are also some old-fashioned departments and units with fax machines still in use. This is how things may ‘lag behind’ and will surely have consequences in the short-to-mid-term.
Although technology is important, the leadership of companies is even more important. They cannot just rely on technology that will supposedly save the day. Big traditional companies have always relied on tangible deliverables (goods such as a computer or an engine, for example). This is technology for them. In this sense, the change will be really technology-led – to the age of future digital ecosystems. There is a risk that digitally provided added value through applications and software will have a slightly secondary focus for too traditional ways of thinking, especially from the ecosystem point of view. Because of this, I hope I can be a part of the change and nurture it from the people and leadership perspective, backed up by my studies at Metropolia, which I fully enjoyed.
Master’s studies as a personal and professional path
My Master’s studies in Industrial Management became true as a result of my long and deliberate search. I had a clear picture in mind that I needed tools to understand business in general – understand customers, strategies and business development. I had already worked for some time in a managerial role and felt a need for effective practical tools. I managed to get them from the Master’s Program, and I also expanded my understanding of business to various other perspectives beyond technology and processes. For example, my Master’s thesis gave me a chance to interview my whole company. From that perspective, I also got what I wanted – and now, reflecting on the whole experience, it was very much worth it.
The perception of studies changes from student to student. For me, doing my Master’s studies was just day-to-day work, although it was a very tight one year of studies while working full-time. It was intensive, but I did not consider it as a sweat shop. It was great to come to classes; there was plenty of new substance every time and everyone was speaking the same ‘business language’.
The studies were a game-changer for me. My career also had a boost and my current title is Head of Operations Support. My team’s primary mission is to help operational functions and the people in them to do their job efficiently and systematically while enjoying it.
*CEO = Chief Executive Officer
*KPI = Key Performance Indicators
References:
- Halonen, H. 2016. A Proposal for Business Intelligence Solution Based on Systems Integration and Enhanced Reporting Functionality. Master Thesis. Metropolia University of Applied Sciences. Master´s Degree Programme in Industrial Management. Helsinki.
- Rouse, M. 2014. DIY BI: A Guide to Self-service Business Intelligence Implementation. TechTarget: Search Business Analytics.
- Few, S. 2013. Information Dashboard Design: Displaying Data for At-a-glance Monitoring. Burlingame: Analytics Press.
- McKinsey Analytics 2018. The New Analytics Translator – From Big Data to Big Ideas: Interview with P. P. Grandes. McKinsey.
Business Intelligence needed in evidence-based decision
Thomas Rohweder
D.Sc. (Econ.)
Principal lecturer
Metropolia University of Applied Sciences
thomas.rohweder [at] metropolia.fi (thomas[dot]rohweder[at]metropolia[dot]fi)
Zinaida Grabovskaia
PhL
Senior lecture, Head of Master’s Degree Programme in Industrial Management
Metropolia University of Applied Sciences
zinaida.grabovskaia [at] metropolia.fi (zinaida[dot]grabovskaia[at]metropolia[dot]fi)
Business intelligence (BI) is as important as ever for the field of industrial management and evidence-based decision making in general. There are two main reasons behind it. First, competition is getting tougher, and companies need to understand their own operations, as well as their customers and competitors, much better. All these core business concerns - reliable, evidence-based knowledge of own operations, awareness of competition, and deep, intimate understanding of customers - are driven by data. Secondly, the latest web-based technological advancements allow for greatly improving this understanding.
Massive leap for BI is still coming
As a result, there is huge interest toward the topic of Business intelligence and many papers are published within this big and broad topic of BI (1,2). Also Henri Remes’s (former Halonen) Master’s thesis project (3), when it started in 2015, took up a practical need of the company for creating an internal BI tool for the case company in question and resolved it. Now only three years later, we have advanced a great deal in this highly dynamic field, especially from the technology perspective (4). However, the guiding mind-set of Business intelligence remains pretty much classic: A data-driven evidence based analysis of the dynamics of a business enterprise’s internal and external operating environment in order to improve the quality of decision making.
What is worth stressing here, amidst the buzz and fast development of the field, is that we have not seen but the very beginning of a data-based revolution. Currently multiple tools and methods are appearing, web and internet based ways of tracking business stakeholders (not only customers) and their behaviours and various patterns. It is our strong belief that the massive leap in this area is still coming. When it comes, digitalized intelligence, continuous tracking of data and its continuous analysis and application for decision-making will allow almost endless development, especially for the areas influenced by Business intelligence.
Development of Business intelligence
At the end of the day, the bigger picture to Business intelligence is BIG DATA. So far “big data” has been able to develop in quite a liberal way. But lately, especially on the political arena we have seen this area “getting on the radar” and emerging attempts to control the collection and use of individually or collectively confidential data. Our estimate is that a liberal development will not continue forever, and it will be interesting to see the impacts of increased governance on Business intelligence.
In the 1970’s, Business intelligence - if we broadly take this area as a field - was pretty much dreaming about “cracking the code”, to understand the dynamics of the operating environment of businesses through holistic mathematical systems modelling. As a result of a succession of oil crises and other hard to forecast phenomena, the urge to model everything was laid back for a while. Nevertheless, recent major advances in computing power have brought the topic of a holistic understanding of the business operating environment back in a powerful way, in a “Big Data” way (5).
What does it mean for business that we have all this refined big data? It means, first of all, that there is huge potential for improving the quality of decision making. Secondly, there will be a time of transition when the first companies to master this field will gain significant benefits. But over the time, more and more players will understand how to capture and utilize big data and the initial competitive advantage - as would be the case with any competitive asset - will gradually diminish.
A more interesting issue arises: the dream of eliminating risk of business decision-making. For obvious reasons, this dream will still not materialize, whatever big data is available. Why? Because business decisions are concerned with future, and the future is not a pre-destined foreseeable entity, but instead is something that unfolds gradually, step by step through the actions of the business players involved. In other words, longer term future will remain more or less random, and therefore, the risk will always be there. But for the short to medium-term decision making, BI definitely has great potential to improve decision making. In this sense, big data marks a game-changing transition.
BI at the heart of Master’s Program
The topic of access to quality data and use of data in business decision making has a very strong influence on our Master’s studies at Metropolia University of Applied Sciences. In effect, the whole Master’s program in Industrial Management Master is based on trying to engage students in evidence-based decision making and evidence-based business development. In other words, the topic of Business intelligence - by definition - lies at the heart of our Master’s training.
More broadly speaking, recent data-technical developments have a continuous influence on all sides of business and society. Metropolia’s Master’s programs have taken this issue seriously from the start. But for the future, we have to increasingly incorporate the rapidly advancing big-data aspect into our programs.
We need to be clear on one thing, however. The very technical aspects of business intelligence will continue to stay in the hands of highly focused specialists. What our Master’s students will need to fully understand is the immense potential of modern business intelligence and how to apply it in daily decision making and business development. The Master’s Thesis by Henri Remes is a good example of utilising this information.
References:
- Chen, H. & Chiang, R. H. L. & Storey, V. C. 2012. Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly 36 (4). 1165–1188.
- Davenport, T. H. & Harris, J. G. 2017. Competing on Analytics: The New Science of Winning. Harvard Business School Publishing.
- Halonen, H. 2016. A Proposal for Business Intelligence Solution Based on Systems Integration and Enhanced Reporting Functionality. Master Thesis. Metropolia University of Applied Sciences. Master´s Degree Programme in Industrial Management. Helsinki.
- Nelson G. S. 2018. The Analytics Lifecycle Toolkit: A Practical Guide for an Effective Analytics Capability. New Jersey: John Wiley & Sons.
- McAfee, A. & Brynjolfsson, E. 2012. Big Data: The Management Revolution. Harvard Business Review, October.
Master's Programme in Industrial Management
Industrial Management Master's degree program started in 2006 as a forerunner of Metropolia's Master's level education. The program has been developed in collaboration with leading industrial companies and is updated annually. In 2018, Industrial Management Master's has grown to 70 student places. The Program continues for 1 year and can be combined with a full-time job.
Metropolia Master´s - Keskusteluja työelämän kehittämisestä
© Metropolia Ammattikorkeakoulu 2018
Metropolia Master´s - Discussions on the Development of Work Life
© Metropolia University of Applied Sciences 2018
Julkaisija (Publisher): Metropolia Ammattikorkeakoulu, Helsinki
Toimittanut (Ed.): Marjatta Kelo & Elina Ala-Nikkola
ISBN 978-952-328-130-1
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