Whether you are a mom and pop donut shop or international business, you rely on data to make better decisions. Data is collected and created through daily transactions with your customers. It reflects their buying decisions and preferences, how often they shop and even if they are willing to pay.
Today, there is an unprecedented focus on large data management (including structured and unstructured data characterized by high volume, velocity, and diversity) or development of data pools. Data to store this huge amount of data. Reason? Big data can provide even better insights on what your customers may want now and in the future – be it medical treatments, jeans or the latest high technology smartwatch.
Attractive as big and attractive data concept as it seems there is no solid enterprise data strategy to manage your entire data warehouse will inevitably lead to risks in data management and management. data. Setting up a business data strategy will help control every vulnerability your organization currently has in relation to data. And it will help you better manage big data when you introduce it to your analytics store.
What is an enterprise data strategy?
An enterprise data strategy is a comprehensive vision and roadmap for a potential organization to exploit data-dependent capabilities. It represents all domain-specific strategies, such as primary data management, business intelligence, big data, etc. A good business data strategy is:
- Be practical (easy for an organization to follow when conducting daily activities).
- Relevant (contextual with the organization, not general).
- Evolution (expected to change on a regular basis).
- Connection/integration (with everything appearing after it or from it).
The main reasons why organizations look at big data needs an enterprise data strategy:
- Help prioritize with existing data sources. The first step in designing an enterprise data strategy is to collect an archive of all data sources, applications, and data owners. That step illustrates the scope and complexity of your data universe and provides the basis for decision making. It also demonstrates – for executives and those responsible for managing data lifecycle – where vulnerabilities exist and competing priorities for resources.
- Streamline logical and physical data architecture. The inventory will allow both business and technical conversations about the relationship between data domains and potential conflicts in definition/terminology. The result must be a reasonable enterprise architecture that both parties understand and maintain.
- Provides a road map to remove legacy systems. Your data warehouse should describe applications and platforms where data is collected and maintained. It will help you understand the capabilities of your system, the amount of effort involved in maintaining daily operations and opportunities to modernize on platforms. Use the repository to develop modern maps and strategies to predict new large data sources and desired analytical capabilities.
- Improve the efficiency of data quality processes. Powerful enterprise data strategy will illustrate data contact points for data quality monitoring and calibration processes. This may include data integration points and areas for active data management interventions. Use this tool to reduce inconsistencies, redundancies or gaps in data quality operations.
- Ask you to rethink the data you collect, value and risk. Data shows both value and risk to any organization. There are legal discovery issues to keep in mind and sharing, reporting, storing or storing data may cause vulnerabilities for regulatory initiatives. Use this tool to assess the risks of your data that you encounter before starting to speed up new big data sources.
- Avoid the burden (and hardware/storage costs) of unnecessary data. Working through business data strategy will help your business be more aware of the total amount of data collected and stored. Part of this understanding will come from recording important data life cycles, understanding the extent of data in different applications and determining when data is considered feasible. What plans for big data? How does this fit into existing data retirement practices? What are the costs involved?
- Establish decision-making authority for data management and data management. A thorough analysis of your current data universe should include an assessment of the responsibilities and ownership of each data source and application. This is an important part of an enterprise data strategy. Who will be responsible for big data? How will the data quality be handled? Find out where responsibility exists today, and where there are gaps. Establish mechanisms for accountability through your data management and data governance activities and upgrade areas that need improvement. Then consider the management needs of big data.
- Predict the real benefits of big data to enrich existing data. Now that you have a strong enterprise data strategy for the current situation, you can start planning for where you should introduce big data sources to complement analysis capabilities compared to where they will be Take risks. You need not only data management platforms and resources to handle the volume of data; You will also need processes and human capital to be responsible for questions that will certainly arise with completely new types of data.
Get serious about your enterprise data strategy
Combining an enterprise data strategy (once and continuously) will be the basic responsibility of any organization that is serious about using data to provide in-depth information and direction. Even before introducing big data into a sophisticated, mature IT store, you should anticipate the fact that big data sources are fundamentally different. The difference will require careful planning and personnel to ensure you prepare for the potential impact and risks when you learn to use big data effectively.