Data Monetization

by Mar 29, 2022#MachineLearning, #HomePage

Printer Icon

Table of Content

  1. Custom Software Development for Data Monetization
  2. How to Monetize Data
  3. Data Monetization Playbook
  4. Data Monetization Technologies
  5. Data Visualization—Visual Communication of Data
  6. Business Intelligence (BI) Software—Data Visualization Tools
  7. Open-source Tools
  8. Data Integration Tools
  9. Analytical Databases Software (Back-end Technologies)
  10. Benefits of Data Monetization
  11. Challenges of Data Monetization
  12. Big Data Analytics
  13. Query Languages—Databases
  14. Programming Languages
  15. Data Monetization Opportunities with Big Data and Analytics Solutions

Custom Software Development for Data Monetization

Data monetization is a process that uses data to create monetary value in the organization. Data products are the source of data that comes in raw or refined forms such as data sets, reports, analysis results, applications, etc. Data sources can originate from internal sources from company operations or external sources (customers, suppliers, partners, solutions 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 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 models 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, query languages, and visualization software.

Transformations require business leadership, an adaptation of operating business 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 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. Coding a customized data pipeline 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 to the data pipeline, making sense of its correlations, and extracting insights that feed business intelligence (BI) and smart 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 in its 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 user interfaces (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, machine learning capabilities, and descriptive and diagnostic analysis. In addition, the Krasamo team provides support to clients to enhance the BI platforms’ capabilities, development operations (DevOps), version control systems, and agile development process. Various tools are available to provide augmented analytics, each with functionalities that cater to specific situations:

Open-source Tools

Data Integration Tools

 Analytical Databases Software (Back-end Technologies)

Benefits of Data Monetization

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

  • Developing new products and services that bring new revenue sources
  • Generating new data to improve products
  • Improving customer experience and loyalty through product personalization
  • Developing new business models
  • Creating partnerships that share data (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 an efficient process for collection, transformation, and organization of data
  • Complexity challenges in integrating data with existing systems (data integration)
  • Lack of know-how and skills
  • Lack of stakeholders 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 is the automation of information processing to extract insights from complex and large data sets for use in decision-making.

Query Languages—Databases

Structure Query Languages (SQL) are used for selecting, adding, or downloading data from a database, which requires accessing data using rules and relationships that organize data into collections. Data can be exported to a spreadsheet for analysis, or it can be imported from spreadsheets to a database.

  • MySQL
  • SQL
  • Microsoft SQL Server
  • BigQuery (ANSI SQL)

Programming Languages

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

  • Python
  • R (Visual Objects with R)

Data Monetization Opportunities with Big Data and Analytics Solutions

Big data and analytics solutions—along with massive amounts of data—drive data monetization growth and new revenue streams for enterprises.

Internal data monetization optimizes operations, products and services, and customer service. On the other hand, external monetization shares data with customers and partners with 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.

Contact Our Data Experts

About Us: Krasamo is a mobile-first Machine Learning and consulting company focused on the Internet-of-Things and Digital Transformation.

Click here to learn more about our machine learning services.


AI Consulting: Accelerating Adoption Across Business Functions

AI Consulting: Accelerating Adoption Across Business Functions

In today’s digital age, adopting AI solutions is crucial for businesses to gain a competitive advantage. However, many organizations lack the necessary data and machine learning (ML) skill set to create valuable AI solutions. This is where AI consultants play a key role, bridging the skill set gap and accelerating the adoption of AI across business functions. AI consultants help assess an organization’s maturity level and design a transformation approach that fits the client’s goals. They also promote the creation of collaborative, cross-functional teams with analytical and ML skills, and work on creating consistency in tools, techniques, and data management practices to enable successful AI adoption.

Building Machine Learning Features on IoT Edge Devices

Building Machine Learning Features on IoT Edge Devices

Enhance IoT edge devices with machine learning using TensorFlow Lite, enabling businesses to create intelligent solutions for appliances, toys, smart sensors, and more. Leverage pretrained models for object detection, image classification, and other applications. TensorFlow Lite supports iOS, Android, Embedded Linux, and Microcontrollers, offering optimized performance for low latency, connectivity, privacy, and power consumption. Equip your IoT products with cutting-edge machine learning capabilities to solve new problems and deliver innovative, cost-effective solutions for a variety of industries.

Feature Engineering for Machine Learning

Feature Engineering for Machine Learning

Feature engineering is a crucial aspect when it comes to designing machine learning models, and it plays a big role in creating top-notch AI systems. Features are attributes that represent the problem of the machine learning use case and contribute to the model’s prediction. The process of feature engineering involves creating relevant and useful features from raw data combined with existing features, adding more variables and signals to improve the model’s accuracy and performance. It starts manually and can be accelerated by adding automated feature engineering tools and techniques. Follow the steps of feature engineering to optimize your machine learning models and create innovative products.

Machine Learning in IoT: Advancements and Applications

Machine Learning in IoT: Advancements and Applications

The Internet of Things (IoT) is rapidly changing various industries by improving processes and products. With the growth of IoT devices and data transmissions, enterprises are facing challenges in managing, monitoring, and securing devices. Machine learning (ML) can help generate intelligence by working with large datasets from IoT devices. ML can create accurate models that analyze and interpret the data generated by IoT devices, identify and secure devices, detect abnormal behavior, and prevent threats. ML can also authenticate devices and improve user experiences. Other IoT applications benefiting from ML include predictive maintenance, smart homes, supply chain, and energy optimization. Building ML features on IoT edge devices is possible with TensorFlow Lite.

DataOps: Cutting-Edge Analytics for AI Solutions

DataOps: Cutting-Edge Analytics for AI Solutions

DataOps is an essential practice for organizations that seek to implement AI solutions and create competitive advantages. It involves communication, integration, and automation of data operations processes to deliver high-quality data analytics for decision-making and market insights. The pipeline process, version control of source code, environment isolation, replicable procedures, and data testing are critical components of DataOps. Using the right tools and methodologies, such as Apache Airflow Orchestration, GIT, Jenkins, and programmable platforms like Google Cloud Big Query and AWS, businesses can streamline data engineering tasks and create value from their data. Krasamo’s DataOps team can help operationalize data for your organization.