Understanding Modern Data Analytics Frameworks | Teradata

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In this day and age, data is foundational to virtually every enterprise across the world. Organizations at this scale produced a significant amount of the 79 zettabytes—i.e., 79 trillion gigabytes—of big data estimated by Statista to have been generated in 2021, and thus are responsible for the lion's share of its usage, processing, and storage.

Data analytics frameworks are integral to all large-scale data management and optimization efforts. They combine efficient processes with cutting-edge data technologies to create insight-rich strategies for enterprise operations. Older models didn't look at the organizations' needs as a whole-—thereby siloing data and creating roadblocks to efficiency. Understanding modern data analytics frameworks and implementing them successfully will be critical for any business looking to get ahead of the curve.

A data analytics framework is a concrete system for managing data analytics efficiently and effectively. But the term itself is used in multiple ways. 

Sometimes, those describing data analytics frameworks will be talking about the processes and best practices for managing data analytics. Major examples include the Sample, Explore, Modify, Model, and Assess (SEMMA) process and the Cross-Industry Standard Process Data Mining (CRISP-DM) guidelines. In other instances, the phrase is used in connection with data analytics solutions, like Teradata Vantage, as well as burgeoning data trends like the data mesh design pattern.

For practical purposes, think of a data analytics framework as a union of processes and technologies. The specific guidelines and solutions used will vary, often widely, between organizations. But the fundamental goal of data analytics frameworks is consistent—to help enterprises use analytics in a way that derives the greatest possible value from the information. Lacking such a framework, and taking a piecemeal, disorganized approach to data analysis, is not an option in today's business world.

Organizations typically base their data analytics frameworks on a clearly defined purpose. This goal can be basic at first—something like, "What business outcomes do we hope to achieve for our enterprise with data analytics?" From there, data teams branch out into more specific operations.

This is one of the most common use cases for analytics and the frameworks supporting them. Enterprises need to be constantly cognizant of everything that affects their bottom line, and gathering key performance indicators (KPIs) and assessing them is how they maintain awareness.

A data analytics framework provides data teams with processes and tools for wide-ranging performance assessments—e.g., of profitability across different business units—as well as narrower operations, like monitoring a customer-facing app's daily active users, engagement, and new user adoption.

Today, it's impossible to develop a new product in a vacuum. Development must be informed by data, including historical sales figures, KPIs regarding competitors' success or failure with a similar product, demand analysis, projections of potential product failures, and much more. 

The ability of many modern devices—ranging from smartphones and medical wearables to modern cars—to collect consumer behavior data adds another dimension of insight developers can draw upon. Data analytics frameworks ensure that product teams can leverage this disparate information, drawing insights to learn from past mistakes and determine better product design strategies for the future.

With predictive maintenance systems in place, manufacturers and other heavy industrial businesses can assess machine health, project the likelihood of failure, and schedule priority repairs when needed. This helps minimize equipment downtime and keep production schedules on track. Data analytics frameworks provide the structure analysts need to gather all the data necessary to make these predictions—equipment age, number of previous repairs, indicators of wear and tear, overall effectiveness, and so on.

To achieve optimal results using a data analytics framework, support it with cutting-edge technologies and solutions:

If you successfully deploy a data analytics framework based on sound principles of data science and supported by agile, reliable technologies, your enterprise has the potential to realize numerous benefits. Here are some of the most notable:

A cloud-centric analytics framework allows for the coexistence of multiple data types and permits multiple analysis methods. Together, this helps significantly speed up the integration and effective use of data, cutting down on time to analyze and minimizing performance bottlenecks. Thus, less time is spent on processing, preparing, and reconfiguring data, meaning more time can be devoted to applying data in innovative ways.

The speed of integration and use also allows for real-time data processing. This can improve customer service, facilitate more effective internal collaboration and innovation, and boost operational efficiency.

Adopting a cutting-edge, cloud-based data analytics framework gives your enterprise the ability to store, access, and use all your data without reformatting, duplicating, or moving it around. Instead of having data spread out and in multiple incompatible formats, you can get straight to analysis, application, and innovation. This, ultimately, will support an end-to-end view of the business and create a single source of truth (SSOT). 

In an unpredictable business environment where organizational needs and customer demands can change instantly, a data analytics framework that allows you to scale up or down on a dime is invaluable. That's exactly what you get with a cloud framework. This scalability can also drive cost savings. The tools used in more traditional analytics frameworks can be expensive or involve rigid pricing models, but cloud analytics solutions allow you to pay only for what you use.

There are many approaches to business analytics and countless tools that support them, and the market will grow wider in the near future. Examples of key trends to watch include:

Data teams should monitor these and other developments and determine whether they need to adapt their data analytics frameworks and architecture to accommodate them. But one trend is too big not to act on. As massive swaths of data from new sources continue to emerge, it's become increasingly clear that effective data analytics frameworks must leverage cloud computing resources. 

A versatile analytics platform like Teradata Vantage that bridges the gap between the cloud and the data center enables enterprises to maximize value across the data ecosystem. Vantage integrates data from all sources, regardless of format, and brings it to bear for critical data analytics projects that provide comprehensive perspective on any organization.

To learn more, read Gartner's 2021 Magic Quadrant report, which names Teradata a leading vendor in the cloud database management system (DBMS) space. Or explore the benefits of QueryGrid, Teradata's high-speed parallel data fabric system.