Getting Started with Data Science in Your Organization

Converge Technology Solutions
February 21, 2023
Advanced Analytics | Blogs

Data science is gaining a lot of attention as enterprises realize that simply getting their hands on data isn’t enough — they need to turn that data into actionable insights. 

Organizations are implementing data science strategies that combine statistics, data analytics, and programming knowledge to analyze and derive insights from large data sets. These findings can help develop data-driven predictions, make accurate decisions, and solve business problems. 

But getting started with data science can be daunting. What software is right for you? Should you choose an on-premise or cloud-based solution? What should you look for in such as solution? 

Enterprise vs. Open Source Data Science Solutions

One of the first questions is whether to use an open source or an enterprise solution. While open source applications may be cheaper and more widely adopted, commercial software can provide specialized support, maintenance, and version control, as well as standardized functionality. 

Some companies start with open source data science software but quickly run into capability and performance issues. However, most open source solutions can’t accommodate scalability and deployment requirements. Many of these companies eventually switch to enterprise solutions, such as IBM Cloud Pak for Data

Unlike open source software, which you’d have to install and configure to find out if it’s right for you, many enterprise solutions offer free trials so you can see if they meet your needs before committing to a service package.  

On Premise/In House vs. Cloud Based Solutions 

The next question is, should you use an on-premise, cloud-based or hybrid solution? 

Some of our clients are in industries that require them to process highly sensitive data. Many folks prefer to have complete control over their information by implementing an on-premise solution. Keep in mind that with on-premise solutions, you are responsible for all the software upgrades and maintenance to ensure performance and data security. 

Meanwhile, other clients choose a cloud-based platform to take advantage of the cost savings benefits and improve their speed of deployment or time to market so they can recognize value quickly from their investment. A cloud-based solution also enables them to leverage various microservices to test different ideas quickly and easily. 

The good news is that IBM Cloud Pak for Data offers hybrid, multi-cloud flexibility in its deployment. You can choose from an on-premise, cloud-based or hybrid solution to meet your business requirements and objectives. 

What to Look for in a Data Science Solution 

When you implement an AI powered data management solution, make sure it supports the needs of your data science practice and your business objectives. Here are some key capabilities to consider: 

  • Cross-team collaboration: A multi- or hybrid cloud solution facilitates collaboration and model deployment among teams and partners in different locations. 
  • Scalability: Your platform should be able to grow with your organization as your requirements change and the demand for data-driven insights increases. 
  • Shareability: Built-in governance allows teams to share master data across the organization without compromising its integrity. 
  • Data cleansing: The accuracy of your predictions is only as good as the quality of the input. Built-in data cleansing capabilities can streamline the process. 
  • User-friendliness: Consolidation, visualization, and representation of data, supported by a responsive and intuitive interface, help increase adoption. 
  • Deep learning capabilities: Artificial neural networks allow you to automate the processing of massive, complex, and differently structured data sets. 
  • Mass processing: The ability to make system-wide changes accurately to datasets ensures that users can always access the most updated and relevant data.  
  • Security and compliance: Built-in data governance ensures compliance with changing regulations and enforces policies across the organization. 
  • Model monitoring: Post-deployment monitoring ensures that your machine learning model performs as intended in the operational environment. 
  • Explainable AI: Transparency in how your AI model operates allows you to calculate your ROI and determine if you should scale the project. 

IBM Cloud Pak for Data: Make Data Science Work for Your Business

IBM Cloud Pak for Data enables you to reap the cost savings benefits of AI powered data analytics. It operates on portable containers that can run on-premise,  public clouds, or in hybrid systems.  

Cloud Pak for Data helps you modernize and streamline the process of collecting, organizing, and analyzing data with AI technologies to derive actionable insights. You can also manage end-to-end data processing and machine learning pipelines using IBM Watson Studio to deploy and monitor models efficiently. 

Cloud Pak for Data can help you improve business agility, scalability, efficiencies, and cost savings. 

Wonder How to Get Started with Cloud Pak for Data?

Defining your use case is a critical first step to optimizing the value of the software. Download our tip sheet to see how. 

Ready to Get Started with Cloud Pak for Data?

You can take advantage of IBM Cloud Pak’s free trial to see if the software is right for you. Then, get in touch with us to schedule a  consultation, during which we’ll walk you through the platform, assist you with defining your use cases, and recommend the best path forward for your environment and budget. Whether you are just beginning down the path or well down the path of your analytics journey, LPA can help with analytics, data science and AI.

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