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Practicing Data: An Overview of the Digital Transformation Process

August 22, 2022

The way businesses operate is continuously evolving. A 2020 global survey by Mckinsey & Co found that around 66% of business leaders from various industries planned to fully automate at least one of their businesses, an increase from 57% two years ago.  

It is more crucial than ever to have clearly defined procedures and practices that organizations can quickly implement to obtain a competitive advantage over their rivals as more businesses move towards automation.  

When looking at digital automation, three significant outcomes to focus on are:

  • Efficient data flows through integrated business systems  
  • Effective use of data to gain business insight through analytics  
  • Continuous development and evolution of technological patterns to address business problems

Businesses typically need to incorporate modern technologies, adopt best practices, and hire skilled professionals to achieve these results. Moreover, companies can streamline their data and get the desired outcomes through three sets of technologies: DataOps, MLOps, and Data Insights. So, let's take a deeper look at these three digital transformation stages.

An overview of the digital transformation process

DataOps

DataOps is the first stage of digital transformation. It involves setting up data systems for the effective flow of data, which goes through various digital transformation processes from the collection stage. This methodology helps define the data flow architecture from multiple businesses and merge all the sources so users can efficiently utilize them. Some ways it does that is through:

  1. Developing and improving data lakes to gather and analyze data from various business channels for sophisticated analytics, visualization, reporting, and machine learning
  1. Establishing data ingestion pipelines for collecting data and storing it in the system in a structured or semi-structured form
  1. Setting up data correction checks according to business rules to ensure data validity
  1. Prepare data according to consumer (data analysts, business owners, unit heads) requirements so it is ready to be used to solve specific business problems

DataOps enables businesses to bring their data, tools, procedures, and teams together. It helps improve the quality of data available, reducing toil, increasing efficiency, and enhancing organizational productivity.  

Data Ops Cycle

MLOps

Building a proper framework for business problems with data science-based solutions is essential. That's where MLOps comes in. It develops a constantly evolving machine learning-based space that helps data scientists efficiently achieve business outcomes.

Data Scientists go through various workflow steps to arrive at the end result when dealing with a data science-based problem. Let's take forecasting as an example. Data scientists first take the relevant insider information from business stakeholders. Then, they perform an extensive initial Exploratory Data Analysis of the appropriate sources. After that, they give the first approval to develop essential model features and multiple candidate machine learning models to set up benchmarks. Moreover, they then coordinate with the stakeholders on the initial feedback and proceed accordingly.  

Additionally, Data Scientists conduct multiple experiments involving different feature sets and ML algorithms to meet the business requirements and get the best possible results. To effectively track these experiments, frameworks like MLFlow are utilized. Finally, advanced model evaluation and selection techniques are used on candidate models to customize them further and optimize the benchmarks. Some of the methods used are:

  • Hyperparameter techniques, for example, hyper opt-based hyper tuning  
  • Cross Validation Techniques to acquire optimal levels of hyperparameters for each candidate model  
  • Model Evaluation metrics (RMSE, MAPE, MAE) to evaluate models during hyperparameter tuning and the final selection process

After the best model/ experiment has been selected; it is forwarded to the productionalization stage. Then, machine learning engineers convert the finalized model into a customized model serving pipeline with CICD structure-based integration and deployment. Other processes like model registry and model versioning are also incorporated to smoothen the integration and deployment process.  

Finally, the last stage in the MLOps process involves monitoring performance in production, checking model metrics, looking for model decay, and re-training schemes to properly utilize the model for the business problem.  

MLOps is one of the most useful practices a company can adopt as it helps create replicable workflows. Moreover, it can quickly deploy high-precision models anywhere. It helps releases be of better quality and, consequently, have a more valuable impact on the user.

MLOps Cycle

Data Insights

Lastly, one of the most critical stages is developing a technological toolkit consisting of custom-built software and apps ready to be utilized by business users to gain insight into relevant processes. This effective software platform is created for business owners and analysts so they can visualize information delivered by both DataOps and MLOps.

To effectively deliver the software product, Software Development Life Cycle (SDLC) Framework processes are incorporated, which include:

  • Planning  
  • Laying Down Requirements  
  • Designing  
  • Building  
  • Documenting  
  • Testing  
  • Deployment

DevOps practices are also used here to optimize delivery. For Data Based Systems, these products can include visualization dashboards (R- Shiny), Power BI-based dashboards, or customized multi-purpose analytics software to display all business data on a single platform. Moreover, these products help end customers utilize systems developed from DataOps and MLOps for business decision-making.

DevOps Cycle

Conclusion

Data-powered operations can accelerate automation while freeing human resources for more significant accomplishments. Therefore, Data is the foundation of any IT transformation. Furthermore, Data is becoming currency to authenticate and regulate IT operations for any firm in the 21st century due to the great demand for digital transformation technologies.

The three practices mentioned above: DataOps, MLOps, and delivering data insights through a software product, can help business owners make their business more agile and seamlessly digitize their manual processes with the help of good data.

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