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At last: The starting shot for digitization strategy has been fired, and marketing is meant to be an important component of this strategy. In the course of implementation, a decision was made to use a customer data platform (CDP) to form a 360-degree customer view and automate marketing journeys in a real-time environment. This decision, in addition to an investment which is not small, has significantly influenced marketing processes and areas (online, e-mail, e-commerce and CRM) at an enterprise. In this third and last part of my CDP-series I would like to focus on the appropriate selection and evaluation of the suitable CDP-tool.

Unlike marketing automation solutions, a distinction is made not according to functional scope such as enterprise or best-of-breed, but according to the customer data platform's functional focus. In other words, the right kind of CDP can be selected already in advance, depending on how requirements are defined.

A total of three categories of customer data platform are distinguishable:

• Data CDP
• Analytics
• Engagement CDP

This end-to-end and step-by-step quick demo shows you how to connect an existing on-premises data source to a modern cloud data warehouse solution such as Snowflake:

• Easily
• Fast
• Securely, and
• Without having to write a single line of code.

Before HANA, exception aggregations in SAP BW more frequently posed a challenge at runtime, this also applying to the distinct count operation. This operation is used, for example, to obtain a customer count from orders.

Earlier, distinct count operations were often implemented as follows:  A calculated key figure with a value of 1 was established, and additions then performed via exception aggregation. However, what worked well in environments without HANA currently causes the pushdown to stop functioning. This can prevent calculations from being optimally performed, depending on settings.

A solution is therefore required for HANA environments: Accordingly, let us concentrate next on optimal implementation of distinct count with business warehouse on HANA or BW/4HANA.

In the following article you will get an overview of the different categories of Customer Data Platforms - based on the classification of David Raab from the Customer Data Platform Institute and further developed by the b.telligent experts on the subject of CDP.

German companies must venture a big step toward digital transformation and improvement of customer experience - this insight is evolving increasingly among decision-makers.  The reasons for this are manifold:

• The number of customer touch points is growing, and background data needs to be used correctly here.
• Customers' expectations are rising immensely.
• New technologies have entered the world of marketers.
• Common campaign management systems are very expensive to implement.
• As the tool landscape continues to grow, it is important to retain a overview.

CDP systems, existent in the USA since 2013, appear to be right on cue here. Whereas campaign management systems tending to be based on workflow often automate CRM-based campaign processes in relational data models, CDP systems allow comprehensive treatment ranging from customer-centric data management in big-data technologies to e-mail dispatch in real time, in order to optimize the customer interaction across all digital channels such as the web, e-mail, mobile communications, online shops and social networks.

In an analysis of the causes of the financial crisis of 2008, the European Central Bank (ECB) and many other regulatory authorities discovered, inter alia, major deficiencies in institutional IT and data architecture. As a result of these deficiencies, risk assessments could not be performed sufficiently quickly and accurately, and not completely either. In short: Risks could neither be reported nor controlled to an appropriate extent. In the wake of this lesson, the Basel Committee on Banking Supervision (BCBS) published standard 239, widely known through press releases and also referred to as the "principles for effective risk data aggregation and risk reporting" and AT 4.3.4 of MaRisk.

New data sources and areas of application emerging constantly continue to drive the steady expansion of data storage systems such as DWH, Data Lake and Analytics Platform. Data management processes must also keep pace with growing requirements. Not seldom do small BI applications grow into major initiatives in which several development teams participate. The situation is exacerbated in many industries by the need to make adjustments faster than ever before. This demands short reaction times and high flexibility from the teams and, not least of all, from the infrastructure.

The Same Problem: Listing Folders and Drives

In the last blog, we used a recursive function for a solution with less than 10 lines to scan folders and allow file evaluation by modification date and size.

Now I’m going to raise the bar somewhat for this example by showing even better alternatives.

The problem: listing folders and drives

Recently while working on a project, a colleague asked whether one could list the content of drives in Python. Of course, you can. Moreover, since this isn’t at all complicated, I’d like to take this case to illustrate key best practices recommended for working with paths on drives.

What Can Snowflake Do as a Cloud DB?

Because (storage) volume and execution time are paid for, the shorter times can reduce costs. A detailed online documentation is available at the following URL: https://docs.snowflake.net/manuals/index.html

Incidentally, one does not have to be an AWS customer to be able to use Snowflake. As a cloud-DB service, Snowflake itself offers no proprietary ETL tools, but leaves this to the manufacturers of ETL, reporting or self-service BI tools. These usually provide native drivers and connections to allow use of their tools with Snowflake. If no separate ETL tool is to be used at the company, there are several possibilities of loading data and realizing the ETL routes. One possibility is implementation of logic in SQL, and orchestration via Python.

What to consider before starting the Development

Recommendation systems are a crucial part of every digital business model. This blog post concisely answers two foundational questions:

1. Who should care about recommendation systems and why?
2. What are the primary flavors of recommendation systems? How much work is it to implement them?

In this article I focus on a solid overview.