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Scalefree – Consulting Solutions – Data Science

Data Science for Business Success

Nearly a decade ago, Harvard Business Review declared data scientist the sexiest job of the 21st Century. But what current value does a data science professional bring to your business?

The possible applications of data science are extremely diverse.

As a result of changing consumer patterns and new digital technologies, data science has become a necessity in most companies. Thus, data scientists are more in demand than ever.

Therefore, it is worth taking a closer look at what value data scientists provide and which questions they answer.

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Data science diagram showing the key pillars of a data scientist.

What is Data Science?

On a fundamental level, data science deals with the analysis, interpretation, and utilization of data from various sources.

That considered, its core responsibilities are formed by four general questions:

  • What has happened?
  • Why did it happen?
  • What will happen in the future?
  • What is the best scenario that can happen?

Methodical, data science consists out of four foundational pillars:

  • Domain Knowledge
  • Mathematics
  • Computer Science
  • Verbal and Digital Communication

What is Business Value?

Data scientists can use historical data to make accurate predictions. This both creates transparency and identifies optimization potential as well as possible strategies for it’s realization. In general, the business value of data science depends on organizational needs.

The Data Science Process for creating Business Value

One example of a successful data science application is the personalization of newsletters. By identifying a customer’s interests using their data, a newsletter can be customized so that it bring the highest value to the customer.

This changes a uniformly generated  newsletter to a unique potential piece of value for the customers. Which in turn will lead to better customer relationship while having a direct financial impact. All of this, by just using data science to group customers by their potential interests.

Read the success story of Berenberg, a leading privately-owned bank, to learn more about how data science and managed self-service BI can be applied in the financial sector.

Data science can be applied to answer every question regarding your data. With all of its different use cases, data science can bring a huge value to almost every company, regardless of industry.

Data Science Tools We Use

OpenSource Tools

Data Scientist and Data Science

Your Business Value

Scalefree consulting is here to help you extract the maximum value from your data.

The key of which is the analysis of all of your source systems and data infrastructure. By implementing a suitable enterprise data strategy, personalized for your company’s needs, a solid base for data science is built.

As a certified Data Vault 2.0 Partner, Scalefree has a proven record of building an enterprise data warehouse, which is scalable, real-time enabled and consistent. This enables modern agile development with short lifetime cycles and exactly what is needed to apply real data science.

Furthermore, Scalefree helps setting up an environment where everyone can access suitable data by offering managed self service BI.

We identify possible data science applications and help implement different solutions. Our goal is to enable both casual and power users an easy to access working environment.

Our Approach to Help You

It is a common problem that user-created solutions never reach enterprise-wide adoption. Due to unclear responsibilities and changing data governance policies, solutions are often canceled before users can begin to fully utilize it.

This is where Scalefree comes in. We build an environment where both casual and power users are encouraged to work with data. It is in this environment, in which everybody is able to industrialize their custom solutions by setting up a data-driven infrastructure in your company.

Furthermore, we help casual users gradually become power users by enabling them to dive deeper into the scientific aspects of their data.

1

In a typical data science environment, someone works inside their user space and creates a solution, for example a dashboard.
2

After a while, the dashboard gets shared with some other users to show what is achieved. As time passes, more and more users begin working with the dashboard despite it being originally only a cobbled together solution that hasn’t passed any quality checks.
3

At some point, management wants this dashboard to become industrialized because the whole company is working with it.
But then questions regarding responsibility for the processes required arise.
These questions create obstructions that lead to the creator of the dashboard dropping their work which then will no longer be used. In this case, the last thing a data driven company would want happens, highly valuable information is lost.
4

As soon as a solution gets industrialized, all responsibilities are clearly distributed. As soon as all data governance policies are applied, the solution is deployed to the business vault.
5

The business users may now use a solution that meets all requirements. Through implementing all enterprise policies, working with this solution is easy,and new business value is used.
Process of industrializing custom data science solutions, from user-created dashboards to enterprise-wide, governed solutions.
Kilian Grünhagen Senior Consultant

Kilian Grünhagen
Senior Consultant

Phone: +49 511 87989342
Mobile: +49 175 2589365

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