According to a Forbes survey, 8 in 10 data scientists spend most of their time collecting and preparing data. If your data science team is bogged down with the tedious tasks of preparing data, it’s hard for them to focus on the strategic priorities. They should be able to spend more time on higher-value activities like model development, data interpretation, etc., to drive the business objectives.
To relieve data scientists of this everyday hassle, businesses are looking to incorporate AI/ML and analytics to improve data management practices. These enhancements have been termed augmented data management and are a part of the new data and analytics trends. Through 2022, data management manual tasks will be reduced by 45% through the addition of machine learning and automated service-level management, Gartner predicts.
In this blog, we will look at some of the challenges in the current practices, use cases of augmented data management, and the subsequent benefits.
In this digital age, the sheer volume of data is a challenge in itself. High volumes leave organizations struggling to aggregate, manage, and create value from data. Despite this struggle, many enterprises take a reactive approach to deal with issues in data management, compounding the problem.
Your data science team has to often transform the raw data so that it can be actually used for its intended purpose. In this process, they must profile, cleanse, link, and reconcile data with a master source. The current practice of statistical profiling is often time-consuming.
Data is often pulled from disparate databases that can lead to inconsistencies and inaccuracies. It’s spread across multiple sources, both internal and external. Different stakeholders are involved in every department rather than the business as a whole. There is no single source of truth.
Data integration can be challenging especially when many data elements in different data sources represent the same attributes despite having clearly different names. Currently, data science teams are leveraging statistical methods that match data based on names and abbreviations. They also create statistical data profiles about the attributes to facilitate data integration but these methods are exhausting.
The database administrator holds a critical role in the data science team. These administrators end up spending most of their time configuring and tuning for both hardware and software. This can lead to challenges in scaling instances to meet business requirements.
With the increased volume of data, the way businesses manage their data must evolve to transform into data-driven organizations and keep up with their business’ needs. Technological enhancements like augmented data management can help automate while improving their processes.
Leveraging advanced analytics techniques instead of only statistical profiling can help expedite the process of ensuring data quality. These techniques include:
Creating a single source of truth for disparate data sources isn’t a cakewalk and requires a lot of collaboration. Machine learning models can replace the current hard-coded rules to easily match records and identify authoritative sources.
Machine learning models are easier to maintain and can avoid overfitting the training data through abstraction. Therefore, with both the training and production data, these models perform better.
Instead of the statistical methods, data scientists can leverage tools that automate the process of analyzing the names and domains of the instances. During data mapping, such tools can give more accurate suggestions, empowering the team to simply add new data sources. They can be assured that the dataset will not be compromised in terms of quality in any way.
Database administrators can be relieved of their responsibility for hardware configuration and tuning by leveraging database-as-a-service (DBaaS). Through automatic management of security patching and upgrading, these DBaaS solutions have enabled faster scaling up of instances. This empowers data science teams to be always ready to meet the evolving demands of the business.
With machine learning, some tools are even putting together databases that can self-tune autonomously, including the automatic creation and optimization of indexes and database configuration parameters.
To ensure the overall quality of the data, metadata management is practiced. It involves managing a direct, traceable lineage for the data. By automating all the processes involved in attribute matching, data cleansing, and integrations, you can ensure that this data lineage is fully traceable and accessible by your information customers to see the full path from source to destination.
Augmented data management empowers you to derive actionable insights without wasting time or resources. It drastically improves the productivity of data scientists, freeing them from the mundane tasks of data management. This saves costs and improves revenue generation for the organization.
Leveraging AI/ML, you can also make sense of unorganized data and turn those data swamps into data lakes. You can gain insights on live conditions, act faster, and improve the bottom line for your organization. Augmented data management enables scalability and can keep up with the growing demands of your business.
Acuvate’s analytics and database services can help you with data strategy and architecture, master data management, data warehouse automation, and reporting and analytics. If you have any queries about how you can leverage augmented data management for your organization, please feel free to get in touch with our experts.