Latest trends in Data Warehousing
Where next for Data Warehousing?
Across industries, companies are dealing with increasing volumes of data as they chase nuanced analytical insights. The result is a rise in the demand for secure data warehousing and operations. An unwanted side-effect is a rise in the risk of data breaches. Data warehousing providers are trying to minimize data transfers using innovations in analytics and AI to ensure greater security. Companies are also modernizing their enterprise data warehousing (EDW) to achieve greater energy efficiency and save on costs.
All companies struggle to organize the data collected or accessed during business operations. However, companies seem to hire data warehousing services ad hoc, which causes several pain points. For instance, their data is scattered across several warehouses and requires more data management and operations effort. Companies incur a higher cost in this process and face more data security risks. This lack of organization makes business processes inefficient and prevents companies from rethinking data warehousing.
Reducing Data Scattering
While the data warehousing challenge may appear complex, companies have several options. They can move all their data to a single warehouse if they are comfortable tackling data migration and integration issues. Companies can analyze the cost-benefits to compare the cost of migrating data with the potential savings on data storage, management, and operations costs. Also, advances in cloud technologies can help companies avoid data migration headaches. Cloud data warehousing providers include Google BigQuery, Snowflake, and Amazon Redshift.
At a broader level, migrating enterprise data to cloud storage is widely considered the first step toward a full-fledged digital transformation. By switching to a cloud data warehousing solution, companies can achieve the first stage of enterprise data warehouse (EDW) modernization. Further, they can minimize their carbon footprint and boost their eco-friendly credentials by adopting cloud-based solutions. If a firm is unsure about adopting cloud data warehousing, it can consider data virtualization as a middle path. Data virtualization allows companies to view and analyze all enterprise data without worrying about the location of their data.
Ultimately, data warehousing should enable processing data more rapidly for analytics. With this aim, data warehousing providers now offer on-site data processing and on-site analytics. Some of the choices for data processing include ramping up CPU capacity, using AI tools for data operations, and compressing enterprise data. Azure Cosmos DB with Azure Synapse Analytics, the operational data warehouse offering from Microsoft, is one example of bundling analytics services with data warehousing. Such a combination lowers the time taken to arrive at insights, removes the need for API integrations with analytics software, and lowers data security risks.
Firms that want more control over data analytics can adopt an analytics-on-demand architecture. Doing so limits user access to relevant data and speeds up data processing and analysis. They can also outsource either the data management or the analytics, if not both, to an external agency. Companies that have already migrated their enterprise data to the cloud may find this a more secure option, but they may also need to vet the agency for data security risks. A holistic digital transformation can allow companies to re-examine their data warehousing needs.
Defogging the Future
Modernizing enterprise data warehousing can help firms expand their view of the data accumulated to facilitate business operations. As laborious as this sounds, companies cannot put off revisiting their data infrastructure forever. Instead, they should proactively seek to understand the potential benefits of executing a digital transformation. As a first step, they can adopt optimal data and analytics integration solutions. Cloud data warehousing, for instance, can involve integrating legacy data through relevant API integration. Also, data integration will likely become more seamless with further innovations.
However, firms should choose a suitable solution only after carefully investigating the technologies available in the market. They should ensure that the technology chosen continues to offer benefits as advertised well into the future. Companies should share their ambitions upfront with vendors and understand how the vendor’s offerings may evolve. The expense involved in adopting a data warehousing solution makes such scrutiny necessary. At the same time, they cannot think only about optimizing costs when deciding on a solution. They should compare the expense involved with the savings they can realize by, for instance, not needing to hire data management specialists.
Even without considering a full-scale digital transformation, EDW modernization offers efficiencies and savings that no company can realistically ignore. Agile data management and processing can enhance performance and reduce the time required for analysis, shortening decision-making timelines. Considering the various uncertainties that companies face, they should consider making the necessary investment now and not wait until they face a crisis.
Irrespective of their size or business ambitions, companies will need faster, sharper insights to make complex business decisions. They cannot afford to think about and use data in siloes. On the contrary, firms need solutions that keep the bigger picture in sight. Further, any solutions adopted today – whether for enterprise data warehousing or deriving analytics – must remain relevant for a considerable time. Otherwise, the expense involved in exploring and selecting the solution bears no fruit. Ideally, firms need a scalable solution to keep up with their growing data requirements. A solution that does not grow in tandem will drain their budget and hold back further ambitions.
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