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I am not writing this blog post in a quiet minute in our b.telligent offices, but live from the Spark Summit in Brussels. For data scientists, it offers an enormous scope of machine learning procedures, both traditional for static data sets, and for streaming data in real-time. All those with practical experiences in the Python library sklearn will immediately feel at home, as this served as an example.

A particularly productive current discussion revolves around the question what a data science team should actually sensibly produce. The two possibilities are quickly named: on the one hand, there is the "analysis", thus, a one-off, rather static final result; in this context, most people immediately think of a PowerPoint presentation.  On the other hand, there is the "app", i.e. an interactive end product continuously supplied with fresh data, frequently in the form of a website or a mobile app.

Due to increasing challenges in digitalisation, e-commerce has been increasingly surpassing stationary trade. According to the IfH Institut in Cologne, this trend will be increasing in the coming years. Parallel to a reduction in sales in stationary trade, by 2020, sales from online trade will increase to approximately 77 billion euros

The local connection arrow enables the restriction of structures without limiting the data at the same time. This function has been existing for many years but is easily forgotten and completely unknown by many application architects and developers. Thus, this is a blog refresh or introduce this function.

Note:arcplan Information Services GmbH has been renamed after the merger with Longview and is now Longview Europe GmbH.

With SAP BW on HANA comes ADSO with new table structures and functions. Compared to the InfoProviders which are used on SAP BW systems not based on HANA, ADSOs have the ability to modify their functions without losing filed data. This also includes a modification of the contents of the tables if the type is changed.

In this process, an ADSO always consists of three tables which are filled and processed depending on the ADSO type. Unused tables are created by the system regardless. Thus, the use in routines, HANA expert scripts etc. is possible but in general not always appropriate.

The role of the customer

The central role of the customer for the strategic alignment of businesses has been discussed in science for decades.

"It costs much more to acquire a customer than it does to keep a customer. That is why a sale to a customer is good, but a relationship with a customer is great." [1]

"Personal data are the fuel of the modern economy" [2]

"In a global information-based economy, data about customers are one of the most important sources for competitive advantage." [3]

After the termination of support for OWB has been officially announced, the Oracle Data Integrator (ODI) is the ETL tool of choice in the Oracle world. The development has progressed to version 12 which brought a few modifications and improvements. The GUI has continued to become more similar to OWB, although there are a few possibilities available which OWB did not offer in this way. In this blog entry, we will deal with the implementation of slowly changing dimensions in ODI.

Thanks to a compulsory interface for database connectors, the "Python Database API Specification v2.0, PEP249", all current connectors have been developed so that database connections and the SQLs for data retrieval and data transactions can be started using the same commands. Results are received in more or less the same format everywhere. It is regarding this issue that there seem to be the most severe deviations from the required standardisation.
But this should not scare anyone off from using Python scripts as a flexible method for automating database operations.

Data science has undergone an increasing professionalization and standardization during recent years. The frequently intrinsically motivated data tinkerer and diddler, who fills the niche "analysis" in his business with very high company-internal data and process know-how, is reaching his limits.

Increasing demands, especially in the course of a stronger customer focus across all industries, force businesses to professionalize the structures in the area "data science": This includes knowledge, available data sources and their preparation and data science products already used in the business.

SAS and R are topics which are very closely related: Both are popular tools for people like us who want to solve problems from the environment of statistic and machine learning on (more or less) large data volumes. Despite this apparent proximity, there are few touchpoints between the communities and only few persons work with both tools. As passionated `outside the box´ thinkers, we regret that and want to start a mini-series by means of this blog article in which we deal with topics which connect the both worlds, in loose order. For this first blog article, we will deal with the possibilities to exchange data between the systems. As there is a high number of ways, this article is limited to the transfer of SAS to R; the opposite direction will follow in a later article.

In order to successfully implement a campaign, it is important to be able to rely to a closed campaign planning process within the business. In case there is no such defined and uniform process, valuable potential is wasted and there is a risk that there will not be successes in the long term.

There are many areas in which neuroscience and machine learning overlap. One of these is the combining of learning during several learning episodes with small success in order to eventually use a merged, stronger, learned model for a particular task. In machine learning, this process is referred to as "boosting". The development of solutions of this kind is a very interesting topic, in particular in the IT industry; thus, a short introduction to machine learning is provided below which presents the basic ideas and the application of the naïve Bayes classifier in R.