Can Failed Data Lakes be repurposed As Data Marketplaces
Data lakes bring you excellent opportunities to confer new insights from a vast array of data procured from different new and old sources. But, different business enterprises struggle with the efficient use, maintenance, and construction of different data lake environments.
Due to this, they fail to capitalize on different data-driven insights, which they might miss out on getting excellent opportunities. Data lakes solutions involve certain risks, which turn into massive spaces to store the data.
Hence, a new set of organizational practices and technological capabilities will turn the data lakes into data marketplaces. Here are some of the principles which are associated with turning failed Data lakes into the Data Marketplaces:
Obstacles in the path of effective data marketplace implementation
Data marketplace environments ensure to offer faster discovery of the latest insights. However, business organizations which embark on such initiatives fail to extract enhanced value from them owing to a plethora of reasons:
Complex needs of gathering processes as well as long development cycles result in delays for the business lines to procure insights, which are essential for creating the required momentum and proving the value.
Too much IT controls can slow down the projects as IT gets involved in different operations unnecessarily.
If the business enterprise does not have good collaboration tools, the teams will fail to reap the advantages of the work, which are created by the teams.
Once the enterprise gets clear of such obstacles, different cross-functional teams are composed of IT data architects and data engineers. Team members of the business are known to be empowered to represent different requirements of business function. The group executes the project scope.
The ultimate objective of creating a cross-functional team is the integration of the knowledge of a variety of sources. To successfully accomplish Data Lake projects, you need to implement the knowledge from data engineering, along with business data from different data stewards, analytical expertise from different data analysts, and data scientists.
Possessing several perspectives motivates the development of constant and accurate business insights, thereby assuring that every member of the business organization is on the same page when it comes to data understanding.
Empowering the data scientists to access the data, involved in data preparation
Self-service data visualization tools are gaining more popularity in the latest years to provide direct data access to different business analysts. Self-service initiatives are recognized as the core tenets of creating a data marketplace that offers a helping hand in bringing the data from the data warehouse's shadows into the consumer facing shelves of the business enterprise.
Sophisticated tools allow the potential audience to publish the prepared datasets into the collaborative workspaces back so that different business stakeholders will be capable of accessing the data at once.
In addition to this, the machine-learning and artificial intelligence techniques offer a guided and automated experience for different business analysts as they try to explore the data present in the data lake.
Use of tagging and crowd sourcing to govern different data assets
Business enterprises opt for data lakes to process sensitive data. But, centralized and slow forms of governance are responsible for negating the agility benefits promised by the data lake.
Online retail marketplaces leverage crowds' wisdom to allow the potential audience to share different reviews and feedback, for empowering different future consumers to reap different benefits from the past experience. Such a kind of crowd sourcing of wisdom and collaborative filtering contributes to being a vital part of the data marketplace.
Data Governance happens to be a value-added function that enhances the data quality, ensuring compliance with the standards, and sensitive data protection. Owing to this, business analysts and data consumers have a similar interest in steward peers' data governance. In this time, the data governance concept comes into existence.
Crowd sourcing contributes to being the capabilities of tapping into the user citizenry of a business analyst to gain expertise and knowledge, which increases data quality collectively. Business analysts should contribute the knowledge through classifications and tags so that the data assets are enhancing in quality without any interruption. Collaboration contributes to being a mechanism that assures self resiliency.
Automation of transformation and ingestion of data
A crucial aspect of the data lake environment contributes to being the automation of data transformation and ingestion. Manual data and ingestion transformation contribute to the complicated multi-step process resulting in inconsistent and unrepeatable results. Different business enterprises reap the benefits of built-in connections along with high-speed ingestion platforms for loading and transforming different datasets into a data lake. It allows the data lakes for scaling, thereby enhancing incoming data volume.
Using rule-based data scoring and data validation to recognize data quality problems
If data quality errors are not recognized at an early phase, it might impact the business insights owing to inconsistencies and inaccuracies between various data assets. With the increase in the data volume, it becomes challenging for business organizations to spot different data quality problems manually.
Data lakes, along with different rule-based data validation, will automatically detect different inconsistent and incomplete data signals. With the detection of such anomalies early, you will enjoy a dramatic effect on the business insight's trustworthiness.
Exploiting machine learning for the data stewardship and data discovery
As the data volume increases rapidly, business enterprise encounters an enormous challenge to get the existing data assets' visibility. Though the act of generating a data lake is useful in centralizing the crucial data assets into the same environment, there might be a question to decide, which the assets should be incorporated into the data lake.
Automated data scanners need to be used to search as well as index the latest data assets across the enterprise. You should make sure to identify machine-learning techniques for recognizing the correlations and similarities between various data assets and creating a holistic assets view for data stewardship.
Data lakes come up with a fantastic opportunity to confer new insights into the business efficiently and faster. Exploiting different best practices in different processes and solutions boost the delivery of such new insights.