Enhancing Your Data Science Team’s Productivity With Augmented Data Management

Byrihangowda

 

Eight out of ten data scientists, according to a Forbes survey, spend the majority of their time gathering and preparing data. It’s challenging for your data science team to concentrate on the strategic priorities if the tiresome responsibilities of data preparation weigh them down. To advance the company objectives, they ought to be able to devote more time to higher-value tasks like model building and data analysis.

 

Businesses are attempting to apply AI/ML and analytics to improve data management procedures to relieve data scientists of this daily bother. The new data and analytics trends include these improvements, referred to as augmented data management. The combination of machine learning and automated processes will result in a 45% reduction in manual data management duties until 2023.

 

In this blog, we’ll examine some of the problems with the way things are done right now, use cases for augmented data management, and the advantages that result.

 

Data Management Challenges

  • Excessive volume

The sheer volume of data in this digital age is a challenge in and of itself. High volumes make it difficult for organizations to aggregate, manage, and create value from data. Despite this struggle, many businesses take a reactive approach to dealing with data management issues, exacerbating the problem.

  • Ensuring that data is fit for use

Your data science team must frequently transform raw data so that it can be used for its intended purpose. They must profile, cleanse, link, and reconcile data with a master source during this process. Statistical profiling is currently a time-consuming practice.

  • Various sources

Data is frequently retrieved from disparate databases, which can result in inconsistencies and inaccuracies. Various sources provide it.

  • Bringing together multiple data sources

Data integration can be difficult, especially when many data elements in different sources represent the same attributes despite having distinct names. Data science teams currently use statistical methods to match data based on names and abbreviations. They also generate statistical data profiles about the attributes to aid data integration, but these methods are time-consuming.

  • Increasing the size

The database administrator is an essential member of the data science team. These administrators spend most of their time configuring and tuning hardware and software. This can cause issues when scaling instances to meet business requirements.

 

Before moving on how augmented data management can help solving the above problems, do check out the Data Science certification course in Delhi. 

 

How augmented data management can assist in addressing these issues

 

With the increased volume of data, businesses’ data management strategies must evolve in order to transform into data-driven organizations and meet the needs of their customers. Technological advancements such as augmented data management can assist in automating and improving processes.

  • Data integrity

Using advanced analytics techniques rather than just statistical profiling can help speed up the process of ensuring data quality. Among these methods are:

  • Detection of outliers

Establish the norm in your organization, isolate outliers, and correct them before they are used.

  • Statistical reasoning

To maintain data quality, automate the cleansing of flagged data.

Time series forecasting and predictive categorization

Fill in the blanks and enrich your data for a complete, consistent picture of your business.

  • Management of master data

Creating a single source of truth for disparate data sources is a difficult task that necessitates extensive collaboration. Machine learning models can easily match records and identify authoritative sources, replacing the current hard-coded rules.

 

Through abstraction, machine learning models are easier to maintain and can avoid overfitting the training data. As a result, these models perform better with training and production data.

  • Integration of data

Instead of statistical methods, data scientists can use tools that automate the process of analyzing instance names and domains. Such tools can provide more accurate suggestions during data mapping, allowing the team to add new data sources simply. They can be confident that the dataset will not be compromised.

  • Database administration systems

By utilizing database-as-a-service, database administrators can be relieved of their responsibility for hardware configuration and tuning (DBaaS). These DBaaS solutions have enabled faster instance scaling by automating security patching and upgrading. This enables data science teams to be always ready to meet the changing business demands.

 

Some machine learning tools are even creating databases that can self-tune autonomously, such as the automatic creation and optimization of indexes and database configuration parameters.

  • Metadata administration

Metadata management is used to ensure the overall quality of the data. It entails keeping track of the data’s direct, traceable lineage. You can ensure that this data lineage is fully traceable and accessible to your information customers by automating all of the processes involved in attribute matching, data cleansing, and integrations.

 

Final Words 

You can derive actionable insights from augmented data management without wasting time or resources. It significantly increases data scientists’ productivity by freeing them from the mundane tasks of data management. This reduces costs while increasing revenue generation for the organization.

 

You can also use AI/ML to make sense of unorganized data and transform data swamps into data lakes. You can gain insights into live conditions, act faster, and improve your organization’s bottom line. Scalability is enabled by augmented data management, which can keep up with your company’s growing demands. Thus, to become a data scientist, head to the AI and Data Science course in Delhi today and become a certified data scientist to help organizations grow.