Überblick
Data analytics tools encompass all of the technologies, systems, and methods that contribute to the planning, structure, enablement, and optimization of data analytics. It’s a multifaceted practice that requires support from multiple types of tools to be fully effective.
With data analytics, organizations can derive insights from millions—sometimes billions—of data points to drive strategy, improve operations, and support better business outcomes in the short and long term.
It's virtually impossible for today's enterprises to function as effectively as they need to without leveraging the vast amount of data they generate on a consistent basis. Data analytics is integral to these efforts, and given the massive volume of information involved, organizations must bring cutting-edge technologies and resources—i.e., everything that we categorize under the umbrella of data analytics tools—into the equation.
Here, we take a look at the wide range of specific tools and solutions that are critical for granular data analysis and modern business analytics. We'll also examine the most important areas that enterprise data analysis tools can cover, what considerations should factor into choosing data analytics solutions, and how Teradata VantageCloud can be at the forefront of your analytics operations.
The term "data analytics tools" often finds itself invoked in reference to software. This is understandable, considering how many of the best-known analytics tools are cloud-based or on-premises software solutions. But a more comprehensive definition would have to include all of the technologies, systems, and methods that contribute to the planning, structure, enablement, and optimization of data analytics.
For example, Teradata VantageCloud would have to figure prominently in any discussion of data analytics tools: It not only drives advanced analytics operations by ingesting, processing, integrating, and granularly analyzing big data, but also includes cloud-native support for multiple types of data architecture.
With that said, programming languages like R and Python, which are frequently go-to codes for many data scientists and analysts, also logically belong in any discussion of data analytics tools. Data teams could not create the applications and solutions that enable them to execute the processes for turning data sets into actionable insights without those codes.
Along similar lines, it's hard to imagine data analytics living up to its fullest potential without the segmentation and structure that design patterns provide. The data warehouse, data lake, and hybridized data lakehouse—as well as emerging architectures like data mesh—are what allow data analytics operations to support the creation of a single source of truth. A data analytics framework, which unites processes such as the Cross-Industry Standard Process Data Mining (CRISP-DM) standard with vital data management technologies, should also be counted as part of the broader ecosystem of data analytics tools.
The bottom line is that data analytics is a multifaceted practice. It's only logical that it requires support from multiple types of tools to be fully effective.
The simplest way to look at the broad spectrum of tools that drive analytics is to go through them by category.
You'll often see Python described as one of the most important codes for data science. This stems partly from its remarkable general popularity. But its open-source code libraries for mathematical and statistical functions, database management, and machine learning (ML) models—NumPy, Pandas, and Scikit-learn, respectively—are considered invaluable by countless data scientists and analysts.
Python certainly doesn't stand alone. R was configured specifically for data science and is about as popular as Python for that purpose. Scala is almost as important, specifically for analytics driven by machine learning, while structured query language (SQL) is mandatory for anyone working with databases. Programming languages that are essentially ubiquitous, like Java, JavaScript, C, and C++, are common in analytics operations, but lack the application-specific usefulness of R and Scala.
This is the most broadly discussed category of data analytics tools, by a significant margin. Many business users will work with an analytics software solution at one point or another, while only some will be writing code or overseeing data architecture.
A significant number of software programs that contribute to analytics operations are dedicated to a specific aspect of data management or analysis. For instance:
Other data analytics and management solutions address a wide range of functions. Teradata VantageCloud, for example, is a complete platform that covers database management, data integration, analytics functions driven by artificial intelligence and machine learning (AI/ML), reporting, visualization, and more. It also has the capabilities of certain application-specific data solutions built in, including Celebrus.
For structured data—which accounts for a significant amount of enterprise data—data warehouses are essential management structures. They unite numerous databases, object stores, file systems, and other data sources to streamline querying and reporting processes.
Yet as unstructured data and semi-structured data became more prevalent in enterprise operations—especially with fast-paced advancements of machine learning—organizations needed different architectures to accommodate this. This resulted in the rise of the data lake and data lakehouse. Both are compatible with structured and unstructured data, which makes them ideal as repositories for data in preparation for analytics operations.
Proponents of the lakehouse architecture prefer it for its combination of structured processing and object storage, but the complexity of its architecture will challenge some organizations. A similar dynamic exists with the domain- and data product-based structure of data mesh: It entrusts data management to the teams closest to it, which increases agility but makes governance challenging and increases siloing risks.
The aforementioned CRISP-DM is perhaps the most commonly followed data analytics framework. Its primary tenets—business understanding, data understanding, data preparation, modeling, evaluation, and deployment—are broad. They can easily be tweaked to meet individual business needs.
The Sample, Explore, Modify, Model, and Assess (SEMMA) and Knowledge Discovery in Databases (KDD) frameworks can also provide valuable guidance for business analytics initiatives. SEMMA is considered something of a legacy framework, however; KDD is structurally similar and more suitable for modern data science. That said, there is no reason why an enterprise can't use all of these frameworks and vary them based on the application, or combine elements of each to create a unique framework.
Although generative AI is perhaps best known to the general public through natural language processing (NLP) models like ChatGPT, this branch of AI can also bring great value to analytics.
There's a clear application for these large language models (LLMs) in the data and analytics industry. Specifically, integrating natural language interfaces into the analytics ecosystem will allow business users to ask complex questions in their usual manner of speaking. This eliminates the need for technical expertise and intricate code-based commands. Ultimately, using enterprise-scale NLP models will make insights more accessible across the enterprise.
All of the resources we've described so far, in various ways, serve the overall purpose of big data analytics in the modern enterprise: deriving insights from millions—sometimes billions—of data points to drive strategy, improve operations, and support better business outcomes in the short and long term.
Data analytics tools facilitate a number of specific processes that help contribute to the larger goal of effective analytics use. Let's take a look at some of the most notable applications for the tools and systems that fall under this umbrella:
Also known as exploratory data analysis (EDA), data exploration refers to the initial examination of data sets shortly after their ingestion. The objective of this process is to identify obvious patterns and trends within data sets and develop an impression of conclusions that the data may reveal once it goes through the more in-depth steps of the analytics life cycle.
Exploration also helps data teams spot problems with data sets at a critical early point. For example, if a data analyst or scientist sees that a dataset clearly has missing values, redundancies, or other blatant issues, they know the set isn't ready for thorough analysis—it will have to be cleansed of these anomalies. This prevents data teams from executing analytics operations on a problematic data set that could've easily produced inaccurate or misleading insights, thus saving their time and resources.
Enabling easier data access across an organization is critical to effective data analytics. Data integration facilitates this by presenting a single, unified view of data from many different sources and formats. This mitigates data siloing and allows data teams to efficiently access the information they need from various business units to conduct thorough data analysis.
Data architectures such as the data warehouse, data lake, and data lakehouse help simplify data integration by serving as organizational hubs for data from disparate sources. For many years, the extract, transform, and load (ETL) process was the main driver of integration. Newer methods including extract, load, and transform (ELT) and streaming data integration can be faster and more efficient.
This is another key process within the larger landscape of data analytics. Data mining involves running data sets through models to discover complex patterns and correlations. These eventually lead data teams to their final conclusions regarding sets of information, which forms the basis for the actionable insights that drive strategy and operational decisions.
Modern data mining models are typically automated via the use of AI/ML technologies. Some employ techniques that originated in more traditional statistical analysis, like k-nearest neighbor and decision trees. Others involve multi-layer neural networks for extremely in-depth analysis.
For today's enterprises, it's simply not feasible to rely on basic descriptive analytics or the moderate granularity of diagnostic analytics. The pace and intricacy of modern business demand more sophisticated big data analysis.
Predictive analytics uses new and historical data to project the progression of data points—e.g., the rise and fall of certain securities on the stock market. The method's prescriptive counterpart analyzes the same data to suggest possible actions in response to patterns or trends. Both provide invaluable support for strategic and operational decision-making.
Presenting data patterns and insights in an organized and understandable format is integral to maximizing the value of analytics operations. That's where reporting comes in. The data analytics tools that facilitate this process typically offer various ways to organize data into reports, including but not limited to CSV and Excel files or simplified PDFs.
Because human beings tend to be visual learners, data visualization is often the most effective reporting method. It is also the only way to present valuable insights from real-time data analytics processes. Visualizations can vary from straightforward charts or graphs to dynamic, interactive visuals that make data truly tangible for business users.
Determining which data analytics tools will best suit the needs of your organization requires the careful consideration of several factors. These are some of the most notable:
For example, perhaps it's your development team or another tech-savvy business unit that hasn't been utilizing analytics tools enough. In all likelihood, they'll quickly take to whatever tools you adopt. But the average business user will have a steep learning curve for a code like Scala. More importantly, they might find it difficult to use a variety of solutions for different aspects of data management.
Therefore, it'll be wisest to choose a complete data and analytics platform, and to standardize Python as your primary data science language: It supports complex data science operations but is easy to learn for any non-technical business users who become interested in upskilling. Also, solutions designed for a wide-ranging user base will often have an intuitive interface and enable a certain level of self-service.
Addressing immediate reporting needs for one team might only require an analytics tool that serves this basic purpose, such as visualization software. But in the long term, that won't cut it for any enterprise-scale organization. Prescriptive and predictive analytics capabilities will be necessary to make the most effective use of data generated by your business.
Along similar lines, short-term data analytics needs might be served by relying solely on one data architecture design pattern. In the long run, the volume and variety of your organization's data consumption and generation will likely expand to such a degree that you'll need multiple options. Additionally, be sure to select data analytics solutions that allow for scalability and flexibility—such as those that are cloud-native.
Running analytics operations can come with certain security risks—in the cloud and even on premises. This means either adopting analytics software that has powerful native security features or implementing tighter security features on a broader scale throughout your IT infrastructure. Additionally, ensure that the data architectures you use allow for appropriate security and governance.
Analytics tools can become costly if you aren't careful. Be mindful of cost from multiple perspectives: Pricing is typically either fixed, consumption-based or some combination of the two. For data analytics solutions that offer provisioned resources, think not only of per-query and storage expenses, but also whether costs remain commensurate with provisioning or if these fees increase for any reason.
Also, keep a close eye on expenses related to architecture-related technologies—object storage, for example, might be low cost, but everything adds up if it isn't carefully planned.
Pairing your portfolio of analytics tools—whatever each of them may be—with the comprehensive strength of VantageCloud is a surefire recipe for success. Use Teradata's cloud-native platform—driven by the powerful AI/ML and financial analytics engine of ClearScape AnalyticsTM—to realize the full potential of the data at your disposal.
VantageCloud is available in both Enterprise and Lake editions and is the ideal solution for turning a "data mess" into a consolidated data ecosystem, driving business decisions that contribute to better bottom-line business performance. Get in touch with Teradata today to learn more.