The pandemic-pushed shift to a brand new virtual global of commercial enterprise has multiplied the tempo of virtual transformation.
Personnel, commercial enterprise procedures, and merchandise want to be supported and shift to digital surroundings for corporations to stay aggressive and survive. It is similarly crucial that data and analytics come to be as agile as different enterprise elements as the range, volume, and distribution of facts continue to grow, unencumbered by external global activities.
Data analysts and business analysts rely heavily on a fit-for-purpose data environment that enables them to do their jobs well. These environments allow them to answer questions from management and different parts of the business. These same professionals have expertise in working and communicating with data but often do not have deep technical knowledge of databases and the underlying infrastructure.
For instance, they may be familiar with SQL and bringing together data sources in a simple data model that allows them to dig deeper in their analysis, but when the database performance degrades during more complex analysis, the depth of infrastructure reliance becomes clear. The dreaded spinner wheel or delays in analysis make it difficult to meet business needs and demands. This can impact critical decision making and reveal underlying weaknesses that get in the way of other data applications, such as artificial intelligence (AI). These indicators of poor performance also show the need for scaling the data environment to accommodate the growth of data and data sources.
To relieve frustration and deliver a better analytics solution and experience for the organization, data and business analysts must focus on strengthening the three pillars of data analytics: agility, performance, and speed. The overall data architecture, supported by a sound data strategy, is the foundation for enabling these pillars. A reliable and fit-for-purpose infrastructure is essential for handling increasing data volumes and can also accommodate more advanced and more performant analytics models.
Data architects and data engineers provide the expertise to proactively identify weaknesses in the underlying infrastructure and existing data models that might impact performance and subsequently the end-user experience. Reviewing and addressing these weaknesses makes the overall environment more flexible and provides room for a more agile approach to data analytics. With the right infrastructure choices and architecture, an organization can achieve better performance, which is reflected in the user experience of analysts as well as that of stakeholders across the business as they consume and interact with information.
Strengthen Agility by Solving the Model Problem
When we think back to how we used search engines 20 years ago, it seems almost prehistoric. We had to use specific syntax and formulas to get relevant results. Today, these engines account for poor spelling and grammar, often offering the answer before the query is fully typed. Using natural language to find information is as much a consumer demand as it is business demand. The underlying data drives the suggestions for autocompleting a search query, but this process relies heavily on the data model.
