C ROSS - CASE ANALYSIS

RQ1: What is the gap between descriptive and predictive analysis in Finnish companies that uses business intelligence in decision making?

Both cases emphasised organization managerial culture towards data and infor-mation. Case A illustrated, that many corporate managements are lacking sense of data driven mind-set. As case B, explained the hierarchal of social and wellbe-ing sector, which also had misswellbe-ing mind-set towards data and its usage, especially when talking about predictive methods.

Case A introduced quite usual problem, which occurs often when companies are wanting something that they do not fully understand. This can be seen in people that are working in management section. Managers that are holding responsibility of company data and are given task to make the best use of it, opposite can be managers that are very budget strict and therefore, cannot see the data driven po-tential versus the costs. Case B, revealed, that university hospitals are very well-known the potential of data and they are using quite innovative methods. From level down to central hospital and local hospitals are not sharing same method.

But end of the day, issues towards data usability sits on managerial level.

Case A emphasises, that if we are talking about gap between descriptive and pre-dictive methods, we should also make in consideration what is the understanding of business managers towards data management. Case B, rises that managers should have the right questions which to ask from their data, without the right questions, how managers can understand what they need or want?

Both cases mentioned the importance of understanding the data management pro-cesses, to understand what is needed to produce descriptive or predictive analyt-ics. Case B spoke about using the already existing data and turning it to infor-mation, it is crucial to look the data in eyes of business intelligence, which

some-times is not happening. As case B issued, that usually first it is necessary to intro-duce business intelligence concept, which will help the managers to understand their existing corporate data.

Both cases stated, that descriptive methods are more common in their cases than predictive. Case B stated, that in epidemiology descriptive methods for reporting has been done for ages and it is very regulated by the government. Case A stated, that companies tent to say having analytics in their environment and they may even have some kind of data management structure, but it is usually narrow and short sighted.

Both cases agree, that predictive method is huge trend at the moment. But the technical aspect for using those methods, is not the problem. Main issue is to avoid the shortcuts in data management and understand all the needed steps in technical perspective. Another problem is the managerial level and how they figure out correct questions for their data.

In both cases, budget was a common barrier, in their respective way. Establishing proof of concept or funding innovative method for a research are highly tided to money. There can be one or several proof of concepts which main point is to take advantage of predictive methods. No doubt, these proofs of concepts are expen-sive. The problem is that these concepts are seen as investment, not a mind-set or part of a strategy and therefore, usually the small but effective improvements are not taking use. Because at that point or some calculated point, it won't give the

"needed financial benefit versus to spent money".

Table 2 What is the gap between descriptive and predictive analysis overview

Case A Case B

Emphasised organization managerial culture towards data and information.

Emphasised organization managerial culture towards data and information.

Lacking sense of data driven mind-set Hierarchal of social and wellbeing sec-tor

Companies are wanting something that they do not fully understand

Health and wellbeing sector data usa-bility sits only on managerial level.

Business managers competence on data management

Managers should have the right ques-tions to ask from their data

Budget Budget

RQ2: What kind of capabilities is needed for companies to take advantage of predictive methods?

To take advantage of predictive methods or descriptive methods, it is necessary to understand holistic process of data management. According to cases, competence can be divided in two categories: technical and business competence. Technical competence is at the moment more or less divided to two groups: competence to data management, which in another words means competence to create data warehouse. Second technical competence is way to use descriptive and predictive methods. It could say, that that future need is to handle the both technical aspects.

Business competence means that managers have solid understand between their business goals and data. Cases presented in their respective fields, that it is cru-cial to get managers to understand how data can be beneficru-cial towards company strategy. It’s important that managers can see thru all the hype-words which are clustering around data. To put it simple way: if you want to use descriptive or pre-dictive analytics, you need to have understood of architectural data management framework. Case B, on the other hand issued, that in future data management pro-cess can be fully automatic which means, that analysts do not need deeper

knowledge towards technical aspect of data preparation.

By these two main competence categories. Cases A and B believe that in future competence it is highly valued that one person can poses both, technical and managerial competence. Case A pointed out, that in client cases the best result has occurred, when technical and business people sat the same negotiation room

and together solve data related need. Case B emphasises, that future managers need to understand to ask right questions towards their data. If you cannot ask what you want your data to show you, it’s no point at all.

Table 3 What kind of capabilities is needed overview

Case A Case B

Technical and business competence Technical and business competence Understand architectural data

manage-ment framework

Automatic data management pre-pro-cess

RQ3: What kind of resources companies need to have for implementing pre-dictive methods?

In both respective cases, came across to the same resources need, which they have noticed during their career. To obtain descriptive or predictive, and even more in predictive, biggest resource problems are related to financial issues as also having competent people working within the companies.

Case A and B both gave examples in their respective industry where budget plays the biggest role when adapting predictive methods. Case A addressed, that proof of concepts are still very expensive versus the possible gain. Therefore, there has been many cases which never continued after proof of concepts. In case B, it was mentioned, that in health and wellbeing sector innovative methods are very de-pendent on budget and funding.

Third research question was seeking answer to competence. The competence is also a resource which companies need to have. Without proper educated workers who understands data management and methods which are used in analytics, it is almost impossible to get the business benefits using data. It is necessary to under-stand how data management process works, as also having correct questions for the data. Having unstructured data processes or in worst case poor understanding of how to extract correct information from data, that meet business requirements.

Table 4 What kind of resources companies need overview

Case A Case B

Budget / Financial Budget / Financial

Know-how Know-how