How To Get Started on a Data Science Journey for Your Business?

In a study by New Vantage, only 39% of executives confirmed that their organizations have been managing data as an asset. Even fewer (24%) attested that their companies are data driven.

It is a missed opportunity. But businesses can harness the power of data through data science ecosystem if they apply themselves within the data science context correctly. Knowing how to start with data science and drive value out of data initiatives can empower businesses to grow a competitive edge, develop their bottom line and become better at making evidence-based decisions related to the future. For this article we have summarized the key steps to kick off your data science journey and to help you realize success.

1. Data Science with Analytics Strategy

   Establishing an analytics strategy is the first important step to your data science journey. An analytics strategy must consider your business`s unique objectives, challenges, and opportunities while establishing KPIs and measurable objectives as the groundwork for data-driven success.

   An effective analytics strategy outlines how data will be collected, analyzed, and transformed into pertinent insights. As such, it helps create a plan for how data will be integrated into existing processes and systems.

   After all, the analytics strategy should be linked to your organization’s overall business strategy, goals, and objectives. This enables you to make data-driven decisions that help you stay ahead of the competition, increase productivity, and remain focused on achieving results.

2. Develop Use Cases

   Emerging geopolitical pressures have caused many organizations to shift their focus from CX. But that’s about to change in 2023. Amid rising customer expectations and a worsening economy, in 2023, more and more businesses will realize that data is critical to understanding customers, developing better products and services, and streamlining internal operations.

   Developing use cases is pivotal to deriving value from data science initiatives. It involves identifying particular business problems or opportunities that can be solved through data analysis. Use cases describe how data will be used to create value through the questions, opportunities, or problems that can be analyzed.

   Most use cases emerge as descriptive, predictive/prescriptive, and responsive.

   • Descriptive use cases describe what happened and why it happened (for example, sales trends).
   • Predictive/prescriptive use cases look ahead to predict what is likely to happen (for example, customer churn).
   • Responsive use cases suggest actions to respond to an event (for example, customer service).

   The development and focus on use cases ensures that data initiatives align with tangible business outcomes.

3. Create a Tailored Data Science/AI Roadmap

   A customized roadmap will help make sense of the complexities of deploying data science and software applications (AI). This road map illustrates a clear and simplified trail from inception to deployment, specifying milestones, resources, and time frames. The roadmap also reflects an understanding of the distinct challenges of your industry while also providing a strategy for realizing your data-driven vision.

4. Establish a Data Science Framework

   A data science framework is a description of (a) practices and procedures that ensure quality and consistent data science projects. The framework should encompass the entire data science process, from data collection to model deployment.

   Here are some of the critical elements of a data science framework:

   • Data Collection: This includes identifying the data you need to collect and how you will collect it.
   • Data Preparation: This entails cleaning, formatting, and transforming the data into a usable format for analysis.
   • Data Analysis: The aims of data analysis efforts likewise include using statistical and machine learning techniques to analyze the data and generate insight from it.
   • Model Building: This consists of generating models to increase accuracy and inform decision making.
   • Model Deployment: Lastly, model deployment is when you are placing the models into production to be used for decision making.

   The specific features of your data science framework will depend on the specific requirements of your organization. Nevertheless, all data science frameworks should be designed to maintain the quality and consistency of your data science efforts.

5. Explore Data, Select Relevant Sources, and Build Datasets

   Data exploration refers to comprehending what data exists and what insights might be possible. This step can entail developing an understanding of any relevant data sources, both internal and/or external, that lend valuable context.

   After you’ve identified data sources, you will need to build datasets through data cleaning, data pre-processing, and data transformation. Accurate and descriptive datasets are the backbone of meaningful analysis.

   Here are some key steps involved in exploring data, selecting relevant sources, and building datasets:

   • Decide the business problem you would like to use data to solve; you should have a clear idea of what you are hoping to achieve. Once the problem you would like to solve has been identified, you can then identify what data you need to collect.
   • Identify the sources of data that you have access to. This may be internal data (e.g., customer file data, sales data, operations data) or external data (e.g., social media data, weather data, financial market data).
   • Investigate the data to understand its qualities and limitations. This involves examining the types of data, distributions of the data, and data missing or corrupted.
   • Choose the appropriate data and compile datasets. This will involve cleaning and processing the data, removing outliers, and formatting the data in a manner that is suitable for analysis.
   • Verify the datasets to ensure that they are correct and complete. This would involve reviewing the data for errors and inconsistencies.

6. Build Scalable Data Science and AI Processes

   Scalability is an important element of your data science projects. When you’re building scalable processes, you want to establish data pipelines, machine learning models, and analytics workflows that can accept additional volume and complexity of data. Scalability allows data-driven insight to remain useful and effective through the growth and evolution of your business.

How Can We Help?

At Emergys, we work so that enterprises have help find a way to make data science easier. Our data science advisory services are the start of a strong data science journey. Our expert data advisory services give businesses the strategic consulting and planning to create the best alignment of the data initiatives with the business objectives. The goal is to leverage the data revolution to better use data to make better decisions, improve operations, and cultivate innovation.

We have a team of experienced data scientists who can help you:

• Identify your business goals and develop a data science strategy.
• Gather and clean your data.
• Choose the right tools and techniques.
• Build and deploy data science models.

We also offer a variety of other services, such as data analytics, AI/ML development, data visualization, etc.