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Using Data Strategies for Growth

By Apoorva Kasam - January 18, 2023 4 Mins Read

Using Data Strategies for Growth

An enterprise data strategy is a comprehensive vision for an organization’s potential to harness data-dependent capabilities. It delineates an umbrella for all domain-specific plans.

Enterprises need to build a robust architecture seamlessly without hampering the current equilibrium of the business. Here are a few transforming data strategies

Management of Analytics Workflow and Success Criteria

The primary goal of the data platform is to enable analytics, helping organizations analyze their current state and make better decisions. Having a streamlined, documented workflow that walks through the entire process explaining the renewable layer for data integration and workflow, is crucial. The workflow to build an analytics platform should include how different data sources can be collected, managed, and incorporated into the analytics platform. At the same time, it should be capable of taking the analytical models from experiments to production rapidly, using agile deployment methodologies. Before starting any analytic project, robust acceptance criteria should include critical points in the existing system and their impact on the growth of an organization. Since analytical projects take weeks, months, or years to complete, defining success criteria in the short and long term is essential.

Also Read: Wasabi Technologies Secures USD 250M to Scale ‘Affordable’ Cloud Storage for Enterprises 

Alignment of Technology Team with Business Perspectives

It is important to have a clear view of the tech implementation, once the analysis process is completed. Understanding business state and requirements are vital for building an effective data strategy. Enterprises need to spend time identifying the business goals for any project. This will allow them to design the analytics platform architecture as per requirements.

Hybrid Data Management and Defining Key Data Sources

Gathering all or as much data as possible is a frequent mistake when building an analytics platform that costs companies hundreds of dollars to manage a huge volume of data. To keep the costs reasonable for data integration activities, it is necessary to find the relevant data sources required before the analytics team implements the data integration process. This strategy helps the enterprises make decisions on those business processes that require immediate action, keeping the data analysts confident about the data. Hybrid data management allows businesses to manage real-time and batch data enabling them to hand over the information to the analytics team to procure near real-time insights for the business team.

Unifies Data Catalog for Data Discovery and Exposes Syndicated Data via APIs

Data stores have numerous capabilities and are utilized as per the use case requirements. Therefore, businesses need to have a unified metadata system that shows all data stored in a single place. This will enable the data consumers, like analytics and the engineering team, to quickly grasp the schemas and fields of data.

Integrating machine learning (ML) into the data catalog allows data analysts to understand the data better. At the same time, organizations need to have data delivery capabilities to third parties, often called “Data as a Service.”

Data Catalog allows visualization of all metadata in a single place enabling enterprises to expose the data utilizing APIs under the influence of data governance and business team.

Secure Data Sharing with Data Governance

Numerous teams are involved in developing a successful analytics platform. A robust enterprise data strategy should include the process of data sharing across various teams securely. It is vital to guide all teams to build a secure data-sharing culture, and educate the team on the relevance and benefits of data governance within the organization.

Also Read: Managing the Spiraling Costs of Securing Your Company’s Data 

Data Quality Management for Enhanced Decision Making

Decisions based on inaccurate data lead to massive losses in business, while companies lose their customers due to irrelevant recommendations. Effective enterprise data strategies provide clear KPIs which will help the organizations determine the data quality and the actions to be taken if the data quality is not at par.

The latest technologies and frameworks necessitate cultural shifts. Hence, the data strategy team needs to be updated with massive data storage capabilities and requirements, ensuring the data and analytical workflow management are always updated.

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Apoorva Kasam

Apoorva Kasam is a Global News Correspondent with OnDot Media. She has done her master’s in Bioinformatics and has 12+ months of experience in clinical and preclinical data management. She is a content-writing enthusiast, and this is her first stint writing articles on business technology. She has covered a wide array of crucial industry insights like Blockchain, strategic planning, data analytics, supply chain management, governance, compliance, and the latest industry trends. Her ideal and digestible writing style displays the current challenges, and relevant mitigation strategies businesses can look forward to. She has a keen interest in the latest enterprise trends like digital transformation, cloud, and enterprise resource planning. She looks for minute details, while her excellent language skills help her deliver a crisp-looking, niche-specific message through her articles. She is looking forward to exploring her writing styles and portraying her thoughts that can help enhance organizational effectiveness, business performance, and sustainability. Apart from writing, she enjoys spending time with her pet and reading oncology publications.

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