Today there’s an unfamiliar focus on handling big data. This includes structured and unstructured data distinguished by huge volume, variety, and velocity. Even, developing data lakes to store these vast amounts of data. Why so? Big data offer valuable insights about what your users are likely to want now and in the future. This could be as simple as buying a pair of jeans or an advanced high-tech phone.
Undoubtedly, big data is an amazing concept but not having a solid enterprise data strategy in place increases risks linked to data management and data governance. Building an enterprise data strategy help manage any issue your company may have related to data. Furthermore, it effectively manages big data once you bring it under your analytics domain.
Understanding the Concept of “Enterprise Data Strategy”
An enterprise data strategy refers to a comprehensive roadmap and vision for a company’s potential to exploit data-dependent capabilities. It indicates the support for all domain-specific techniques like business intelligence, master data management, big data, and so on.
Elements of an intelligent enterprise data strategy include:
- Evolutionary: It is expected to change regularly
- Practical: Easy for the company to follow when performing daily activities
- Integrated or connected: Includes everything that comes after it or from it
- Relevant: Contextual to the organization, not generic
Best Practices Why Companies Using Big Data Need an Enterprise Data Strategy
- Help determine priorities with current data sources: This requires you to gather an inventory of all data sources, data owners, and applications. This practice explains the complexity and scope of the data universe and offers the grounds for decision-making.
It also explains- to responsible authorities how to manage the data life cycle. Where competing priorities and gaps exists for resources.
- Rationalize physical and logical data architecture: The inventory must allow technical conversations and business about the relationships between potential conflicts and data domains in terms/of definition. The outcome must be a logical architecture structure that both sides of the company maintain and understand.
- Design a roadmap to phase out outdated software: Your data inventory must explain the platforms and applications where data is gathered and maintained. It must help you comprehend the abilities of your systems, and the effort required in maintaining daily opportunities and operations to modernize access platforms.
Using the inventory to design a strategy and roadmap for modernizing to foresee new big data sources and desired analytical capabilities.
- Enhance the effectiveness of data quality processes: A comprehensive data strategy for enterprises will outline the various data touchpoints where monitoring and correcting data quality processes occur. This can encompass integration points for data and areas that require proactive data stewardship intervention. Use this tool to minimize discrepancies, redundancies, or deficiencies in data quality efforts.
- Need you to reimagine the data you gathered, the risks, and the value: Data introduces both risk and value to any company. There are legal development problems to be known, and reporting, sharing, archiving, or storing data may bring vulnerability to regulatory initiatives. This tool is used to assess the risk your data bring you to before you began to ramp up for new big data sources.
- Evade the burden of unnecessary data: Engaging in the development of an enterprise data strategy should enhance your organization’s awareness regarding the overall volume of collected and stored data. Documenting key data life cycles will contribute to this awareness, encompassing an understanding of the data’s persistence across various applications and its viability duration.
It is important to establish a plan specifically tailored for big data and assess how it aligns with existing data retirement practices. Additionally, consider the associated costs involved in managing big data within this context.
- Build decision-making authority for data management and data governance: When conducting a comprehensive examination of your current data landscape, it is essential to evaluate the accountability and ownership associated with each data source and application. This forms a crucial component of an enterprise data strategy.
Determine who will take responsibility for big data and how decisions regarding data quality will be addressed. Identify existing accountability structures and identify any gaps that may exist. Establish effective mechanisms for accountability through data stewardship and data governance initiatives and reinforce areas that require enhancement. Subsequently, consider the stewardship requirements specific to big data.
- Understand the true advantages of big data to enrich existing data:
- In an all-encompassing data strategy designed for enterprises, the diverse touchpoints of data will be delineated, indicating where monitoring and rectifying data quality processes take place. This encompasses integration points for data and specific areas that necessitate proactive intervention from data stewards.
Employ this tool effectively to mitigate inconsistencies, redundancies, and deficiencies in data quality endeavors.
Using an enterprise data strategy is crucial as it allows companies to provide direction and insights. Before incorporating big data into an established and advanced IT department, it is crucial to consider the inherent dissimilarities of big data sources. These disparities require careful planning and staffing to adequately address the consequences and potential hazards associated with effectively utilizing big data.