How to build up the support of Data Analytics for superior outcome
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.
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.
Build Up swiftness by resolving the Model obstacle
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 a business demand. The underlying data drives the suggestions for autocompleting a search query, but this process relies heavily on the data model. A data model is only as good as the information used to create it and the requirements communicated by the business to the data engineers. Ideally, a data model brings together all the relevant data and relates tables from different data sources to one another so they can be queried by analysts in their entirety and in relation to one another. Otherwise the information and the value of the insights that analysts produce is limited. One challenge for the organization’s data model is the constantly changing business environment, especially right now during a global pandemic. A rigid data model may still be suitable for certain data processes and to achieve specific reporting outputs. However, it can get in the way of running an organization flexibly. At the heart of the data strategy should be the consideration of the business’s current operating environment. One method of creating an agile data model is to use data vault modelling. With a data vault, the organization can grow its data volumes unhindered and can respond to rapid business changes, keeping their data model flexible while maintaining a detailed data catalogue. This proves useful for compliance and auditing requirements because a full history of the data is available.
Build Up Performance by Including Semi structured and Unstructured Data
Although trickier to analyze than structured data, semi structured and unstructured data are far more widespread in the enterprise. IDC projects that 80 percent of worldwide data will be unstructured by 2025, with the vast majority of this influenced by the rise of IoT. Determining what data is structured and what is semi structured or unstructured is not always clear. Sometimes unstructured data is defined and processed in a structured way (i.e., log data in CSV format), and other times, data that is schema-defined isn’t necessarily structured. These discrepancies throw a proverbial wrench into data analytics.
To stay competitive, businesses need to bring a broader array of data together in a 360-degree view to support deeper, more accurate, and more precise analytics insights. Supporting semi structured data formats such as JSON is now a business imperative because they offer potential for business advantage to any company that handles and analyzes them well. AI algorithms can help extract meaning from large volumes of unstructured data, driven by data scientists and data analysts with deep expertise in developing the right models and approaches to work with this data.
Build Up Speed and Performance with GPUs
Speed and performance for your analytics environment go hand in hand. Enabling your data professionals to stay in the flow of analysis requires a performant architecture that delivers data to applications without delay. There is the speed with which your technology handles the analytical queries. Performance goes even deeper. It includes the complexity of your data model and whether it handles massive data volumes effortlessly and ensures that everyone can use the system when they need it, without having an unsatisfactory user experience.
As your data architecture evolves, so does the technology and — more specifically — the hardware that makes it all happen. Leading companies are going beyond the traditional use of CPU power and are embracing graphics processing units (GPUs) for their AI and machine learning (ML) applications. This allows them to develop, train, and run their analytics models faster, which in turn can lead to better products and services for their customers.
With GPUs, organizations can massively parallelize operations to support the training of analytics models and/or inferencing, providing the scale and performance required to efficiently complete multiple epochs in a shorter time frame and to fine-tune the model. Additionally, using GPUs in the cloud offers the ability to run different AI/ML workloads with the flexibility needed for a cost-effective, scalable, and secure AI solution. By evaluating how an organization is truly meeting the three pillars of data analytics, business leaders gain insights into where change may be welcome, where it is needed, and where it is critically necessary if the business is to survive as the pace of transformation and volumes of data continue to rise.