Data monetization is a process that uses data to create monetary value in the organization. Data products are deliverables built from data (datasets, reports, analysis results, APIs, applications, etc.). Data sources can originate from internal sources from company operations or external sources  (customers, suppliers, partners, solution providers, etc.).

Data monetization can create disruptive products and new business models

How to Monetize Data

Many companies have been focused on collecting data but still don’t know how to monetize it. At the same time, others are already taking monetization initiatives by analyzing data models and creating new revenue streams. Monetizing data definitely helps organizations in becoming more competitive.

Organizations with data strategy plans begin by developing use cases, data model concepts, and prototypes. Managers start by providing their analysis and results from internal operational data and bring data to life with data analytics tools such as spreadsheets, databases, SQL query languages, and visualization software.

Transformations require business leadership, adaptation of business operating models and functions, organizational design, data culture, talent, and good management. Data monetization initiatives also require alignment between business operations and information technology (IT). Experienced talent with knowledge of the business and industry is critical for success as well when implementing data monetization and analytics.

Data strategy, design, and architecture of internal data help set up a technical platform for data monetization. As a good practice, get started by fixing your internal data sharing practices and integrations before looking to external data sources

Data Monetization Playbook

Data analytics transforms business functions and models across industries. Data monetization creates differentiation and gives a competitive edge to industry players.

Data Monetization Technologies

Successful data monetization projects depend on workflow design and development with the proper management of a data pipeline. Building  customized data pipelines and ETL workflows for a specific business is a good choice, considering the specifics of the business operations, workflows, maturity level, skill set, and technologies being used.

Our teams help clients modernize and optimize data architectures, organizing data that is spread across environments and comes in many formats. We can also manage the process of aggregating metadata from different sources into the data pipeline, making sense of its correlations, and extracting insights that feed business intelligence (BI) and advanced analytics software

The Krasamo team can build customized data applications for data monetization and integrate multiple databases and multi-cloud environments with a combination of tools.

Data Visualization—Visual Communication of Data

Nowadays, we have almost unlimited amounts of data, and we have to convert that data into a visual story with context. Showing the data and telling a story is key for data-driven decision-making. Data visualization is an important step in data analytics and monetization projects, as it is the best way to show and communicate results to stakeholders. Our data analysts have developed expertise in communicating data via dashboards and reports  (data storytelling).

Krasamo is vendor-neutral and provides unbiased assessments.

Business Intelligence (BI) Software—Data Visualization Tools

Business intelligence software empowers users in reporting, querying, data visualization, predictive analytics and machine learning capabilities, and descriptive and diagnostic analysis. In addition, the Krasamo team provides support to clients to enhance the BI platforms’ capabilities, Devops practices, version control systems, and Agile development processes. Various tools are available to provide augmented analytics, each with functionalities that cater to specific situations:

• Power BI
• Tableau (from Salesforce)
• Looker  (from Google Cloud)
• Oracle Analytics

Open-source Tools

• Apache Hadoop
• Apache Spark
• Apache Kafka

Data Integration Tools

• Informatica
• SAS Data Management
• SAP Data Services
• Oracle Data Integrator (ODI)
• Trifacta
• IBM Data Stage
• Fivetran
• Confluent
• Google Cloud Data Fusion

Analytical Databases Software (Back-end Technologies)

• Amazon RedShift
• Google BigQuery

Benefits of Data Monetization

Thanks to data analysis, data monetization can take place, adding value to the business. Some of the most relevant benefits and opportunities of data monetization include:

• Developing new products and services that bring new revenue sources
• Generating new insights from data to improve products
• Improving customer experience and loyalty through product personalization
• Developing new business models
• Creating partnerships that share data (e.g., create a data utility)
• Increasing business competitiveness

Challenges of Data Monetization

Businesses facing challenges to monetize data usually confirm the following issues:

• Poor data quality due to the lack of efficient process for  collecting, transforming , and  organizing data
• Complexity in integrating data with existing systems (data integration)
• Lack of know-how and skills
• Lack of stakeholder buy-in and/or management support
• Data security concerns

Krasamo helps clients keep up with constant changes in data, model retraining, continuous monitoring, and maintenance of systems.

Big Data Analytics

Big data refers to large and complex datasets that require advanced methods (e.g., distributed computing, automation) for processing and analysis to extract insights from complex and large datasets for use in decision-making.

Query Languages—Databases

The Structured Query Language (SQL) is the standard language for interacting with relational databases. SQL is used to select, insert, update, and delete data, relying on rules and relationships that organize information into tables. Many popular database engines implement SQL with their own extensions, including MySQL, PostgreSQL, Microsoft SQL Server, and Google BigQuery (which supports ANSI SQL). Data managed through these engines can be exported to spreadsheets for further analysis or imported from spreadsheets into databases to enrich datasets.

Programming Languages

Statistical analysis, data visualization, and other advanced analytics require skills in programming languages such as:

• Python
• R (for statistical computing and visualization)

Data Monetization Opportunities with Big Data and Analytics Solutions

Big data and analytics solutions—leveraging massive datasets—drive data monetization growth and new revenue streams for enterprises.

Internal data monetization focuses on optimizing operations, improving products and services, and enhancing customer experience. On the other hand,  external monetization involves sharing or selling data with customers and partners to create mutual benefits to create new revenue streams. Data can be anonymized, aggregated, and integrated with public data sources in order to pursue high-value opportunities, such as in IoT use cases.
 

Krasamo is a software developer and integrator with expertise in IoT, mobile apps, and machine learning. Based in Dallas, Texas, we have been serving medium to large corporations in the US since 2009.

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Krasamo’s Data Engineering Services

• Cloud Computing Services
• Machine Learning
• Data Management
• Data Analytics
• ETL/ELT Pipelines
• IoT Data Analytics