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Key Learnings of the Expert Talk: Data Science for Managers

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On April 7th, 2022, our experts Sanja Jovanovic and Gabriele Bolek-Fügl, both part of the Austrian AI Community, discussed the use and challenges of data in the business environment. Using examples, they demonstrated why data is so important today and what needs to be considered to achieve good results.

We have summarized the key learnings from the expert talk for you:

Most enterprises struggle with data: The evolution of data – 1) Data Warehousing, 2) Data Engineering, 3) Streaming, 4) Data Science and ML – adds an additional layer of complexity to everything a company does.

Where does this complexity come from? There are multiple steps of complexity: Different kinds of hardware (e.g., servers, clouds, smartphones, desktop PCs, laptops) work with different layers of software (e.g., operating systems, middleware, databases, interfaces, applications) connected by interfaces produced by different developers with different data (e.g., structured, unstructured, pictures, formats, bias). Moreover, to make results useful for companies, different users (e.g., computer scientists, IT-savvy and not IT-savvy) must follow all the requirements (e.g., laws, standards, company policies, and guidelines).

Data Governance and audit: If you do not have good data governance processes in your company, you will never have good data. As business management, you need to make sure that you know: Where do we have the data? Who has access to it? How to define and manage fine-grained access controls?

Kinds of data: Not all data is the same. The main data types, which come with different responsibilities and maintenance methods, found in every company are: metadata, reference data, structural data, transaction structure data, inventory data, transaction data, and audit data.

Characteristics of Big Data and differences to traditional IT systems: The objectives of traditional IT systems are past-oriented (Business Intelligence) and can include the automation of processes and optimization of supply chains for cost reduction, increase in efficiency, and increase in productivity. Big Data Projects are future-oriented (Business Analytics) and are often about developing a uniform understanding of customer behavior, identifying and understanding customer needs, identifying future markets to serve customers better with new insights, and building new lines of business.

How to use Big Data in your company: Be mindful of these key points:

1. Humans make the difference. AI will not replace people. Instead, companies will continue to need people who can create new value from data.

2. Using data creates value, but only if you have the right kind and high quality.

If you already have data in your company, try to use it to enhance your current business model. If the data is insufficient because of quality or a need for other kinds of data, see if you can connect and share with other companies.

3. It is essential to ask (the right) questions. You need to have a vision and ask the right questions, or you will never get the right answer. These answers will not be in old systems, but you can create value by connecting old data with public data, thereby creating new value for future business models.

4. Big Data projects are not IT projects but are similar to research projects. You have a question and then try to determine if your data connected with other data will deliver useful results. Like research projects, you have hypotheses that you need to test.

5. Set a high focus on learning. People in big data projects will learn many things and bring those insights into the departments, which can enhance your business model if you cannot use the data. Big Data projects focus on learning, and you must invest in your people to get the best results and be competitive with other companies.

About the Experts

Sanja Jovanovic,

Sanja works as a Solution Architect at Databricks where she supports customers in their big data and machine learning journey. Before Databricks, Sanja worked as a Cloud Solution Architect at Microsoft. Alongside her work, she supports young women in aspiring a career in tech and is part of the board of the NGO Women in AI Austria.

Gabriele Bolek-Fügl,

Gabi worked for 22 years in international auditing and consulting companies and founded her own start-up in 2020. In this start-up Compliance 2b, she is developing a solution that uses AI to support the processing of internal reports within the company. Furthermore she is a lecturer at FH St. Pölten and IMC Krems.

Women in AI, is a Global community of women experts and influencers of women in AI. Our goal is to close the gender gap in the field of Artificial Intelligence by empowering women and raising awareness about gender diversity in AI. The Austrian WAI Association focuses on the transfer of knowledge and the design of policies.