How to Create Data Products - From Data Scientist to Business Owner

Dr. Carlo Velten, Senior Analyst & CEO

What is a Data Product?

A data product is an application or tool that uses data to help businesses improve their decisions and processes. Data products that provide a friendly user interface can use data science to provide predictive analytics, descriptive data modeling, data mining, machine learning, risk management, and a variety of analysis methods to non-data scientists.

Reaching business objectives through informed decisions made with insights from data products is the main driver for company adoption. The competitive advantages created by improving services or products according to data gathered from customers, website visitor behavior, surveys, and other data assets through data-driven analysis adds significant economic value.

Examples of Data Products

Common data products examples are Salesforce’s Einstein AI that provides customer predictive analytics, finance terminals such as the Bloomberg Terminal, website analytics tools such as Google Analytics. However, useful data applications don’t need to be enterprise level to be impactful to a company. Often businesses develop their own internal data products for privacy, data integrity, and adaptability.

Businesses look to find data applications that are built to fulfill a specific need. The more flexible and customizable, the more valuable within an organization.

A company with great data products woven into their strategy and culture frees up economic resources. This is commonly seen where employees spend copious amounts of time and effort preparing, cleaning, and analyzing data.

For instance, the work of financial analysis can be manual, time consuming and tedious. Our data product, Tableau Prep, empowers financial analysts to speed up this process and spend their time on finding better insights. The product is flexible enough to be customizable across different industries for many different roles.

Much like other technology products, the ease of use is crucial when building a data application that can be widely adopted as a scalable solution. Similar to beta testing, data products should be improved through feedback processes from customers who are using the application in day-to-day work.

Do you have a strategy for creating data products?

Companies are increasingly measuring and evaluating the economic value of their data assets. In addition to assessing the value of the data itself, revenue generated through new, data-based business models will also play a very important role in the future. As we see more use cases for data products and analytics services, more organizations are exploring data monetization opportunities and embedded analytics integrations.

Key findings covered in this report include:

  • Analytics-based solutions and business models have shown incredible growth through 2018.
  • A wide range of digital use cases and business models make data products and data monetization attractive to many types and sizes of organizations.
  • Revenue generated through data and analytics-based business models is becoming a key indicator of digital success.
  • Embedded business intelligence use cases and analytics integrations with IoT platforms have increased, as modern analytics functionality is one of the most success-relevant components of digital products and platforms today.
  • Data science responsibilities are expanding into new areas, including the economic success of the data business and the technology that supports it.
  • Analytics platform selection and “build or buy” considerations are driven by needs to connect disparate data sources, multiple clouds, and APIs.

“Data is the new oil.” This slogan is popular among senior managers when they are explaining the relevance of a data-driven digital strategy or corporate culture to board members, investors or employees. The phrase appears simple and clear, and rarely provokes questions. But actually, it’s a lot more exciting to answer questions like: How can this oil be extracted? How will it be transported? How and where do you refine it? And what does the business model look like? The same questions apply to data and the opportunities to monetize data assets within a framework of new data-driven business models and analytics solutions.

Read the report to learn more.

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About the author


Dr. Carlo Velten

Senior Analyst & CEO

Dr. Carlo Velten ist CEO des IT-Research- und Beratungsunternehmens Crisp Research AG. Seit über 15 Jahren berät Carlo Velten als IT-Analyst namhafte Technologieunternehmen in Marketing- und Strategiefragen. Seine Schwerpunktthemen sind Cloud Strategy & Economics, Data Center Innovation und Digital Business Transformation. Zuvor leitete er 8 Jahre lang gemeinsam mit Steve Janata bei der Experton Group die „Cloud Computing & Innovation Practice“ und war Initiator des „Cloud Vendor Benchmark“. Davor war Carlo Velten verantwortlicher Senior Analyst bei der TechConsult und dort für die Themen Open Source und Web Computing verantwortlich. Dr. Carlo Velten ist Jurymitglied bei den „Best-in-Cloud-Awards“ und engagiert sich im Branchenverband BITKOM. Als Business Angel unterstützt er junge Startups und ist politisch als Vorstand des Managerkreises der Friedrich Ebert Stiftung aktiv.