There’s no question that the last decade has seen an exponential increase in the rate of data creation. In fact, it has been said that 90 percent of the world’s data was created in the last two years, and that 80-90 percent of this data is unstructured. As we’ll discuss in this guide, along with these changes in data creation comes the need for new ways to analyze data.

Although analytics is not a new field, the analytics tool stack has been undergoing restructuring due to technological advances in areas such as AI and machine learning. In particular, we’ll discuss what an analytics tool is, why they are important, and the modern analytics tool stack.

What is an Analytics Tool & Why is it Important?

Regardless of the technology or tools used, the purpose of any business analytics tool is to analyze both structured and unstructured data and extract actionable insights. For example, these insights can be in the form of business monitoring, anomaly detection, or forecasting.

Ultimately, the goal of analytics is to find patterns in data that can be used to improve decision making. Many businesses have realized that analytics is a core differentiator that allows them to stay ahead of the competition by making data more accessible, secure, and governed.

The Modern Analytics Tool Stack

As mentioned, the massive increases in data creation have resulted in the need to change the modern analytics tool stack. One major technology that has been applied to the field of analytics to address this need is AI and machine learning.

This fundamental restructuring of this tool stack has seen an increased implementation of autonomous processes, which have effectively reduced the number of components required and resulted in end-to-end analytics platforms. That said, while there are platforms that cover the entire analytics workflow, many businesses choose to go with platforms that focus a core functionality rather than broader offerings that offer less specialization.

With fewer moving parts and fewer manual processes, this has both increased the ability to handle big data with fewer errors and fewer resources needed to manage the entire analytics workflow. In particular, as discussed in our Guide to the Modern Analytics Stack can be split into five key building blocks:

  • Data sources
  • Data pipelines
  • Data warehousing
  • Analytics & visualization
  • Business monitoring & anomaly detection

Let’s take a look at each of these building blocks in a bit more detail.

Data sources

The first step in building an effective analytics process is making sure you have the right data to drive insights and business decisions. While your data source is not part of the analytics stack, it is the fuel that drives decision making.

For many companies, data is often siloed between different departments, applications, and products. These data silos present a challenge in the analytics stack as fragmented data can never provide a complete picture of what’s going on. To solve this challenge, that’s where data pipelines and warehousing come into the picture.

Data Pipelines

Each business system typically has various data types, formats, and schemas, all of which must be reconciled before reaching the warehouse. To do this, data must be cleaned, prepared, formatted, and sometimes enriched in order to be analytically useful. This process is referred to as ETL and involves extracting, transforming, and loading data into the warehouse.

Traditionally, many ETL transformations were manual or semi-manual, although this is quite a labor-intensive and inefficient process. To solve this, modern Data Pipelines are now able to perform ETL processes entirely autonomously.

While the optimal data pipeline will change based on the use case, the most essential features are that the solution is able to continuously push data from any source without losing any data quality, governance, or quality.

Data Warehousing

The next step in the analytics process is storing data in a central repository that can be queried and analyzed. With the rate of data creation and ingestion ever-increasing, modern data warehouses require adequate scale and performance to accommodate any size of data.

Traditionally, many companies used on-premise data warehouses (DMBS), although these have struggled to keep up with data demand. To address this, cloud-based storage systems have not only enabled businesses to manage data at scale, but have also reduced operational costs and IT infrastructure requirements. In terms of evaluating cloud-based data warehouses, the primary criteria should focus on speed, security, and availability.

Analytics & Visualization

Now that data has been sourced, prepared, and stored it’s now ready to be analyzed. This part of the process makes use of tools for data visualization, analysis, and collaboration between both technical and non-technical users.

As mentioned, advances in AI and machine learning have made this part of the analytics stack much more intuitive and autonomous. Similarly, many traditional BI tools rely on techniques that simply analyze what has already happened, without being able to act or adjust to this information in real-time. In other words, AI and machine learning have transformed the analytics stack from a reactive process to one that is entirely proactive.

Business Monitoring & Anomaly Detection

AI-based business monitoring and anomaly detection take this proactive analytics process a step further to empower data-driven business decisions when it matters most. In particular, AI-based business monitoring has the ability to monitor 100% of an organization’s data in realtime and correlate KPIs and metrics with one another.

As many companies generate millions of data events each day, this feature of machine learning allows for anomaly detection with a root cause analysis that can often significantly reduce the time-to-resolution. Not only that, but as the normal behavior of business metrics change, AI-based monitoring can autonomously adjust to the new normal.

Business monitoring and anomaly detection have many practical use cases, all of which are designed to improve the customer experience and protect the bottom line.

Summary: Modern Analytics Tools

As discussed, the modern analytics stack has been undergoing a fundamental restructuring due to the demands of big data. The five core building blocks of the modern analytics stack include:

  • Data sourcing
  • Data pipelines
  • Data warehousing
  • Analytics & visualization
  • Business monitoring & anomaly detection

Thanks in large part due to advances in AI and machine learning, the entire analytics stack is a much more autonomous and adaptive process. As data growth is showing no signs of slowing down, many companies have realized that their analytics stack is a key differentiator that allows them to stay ahead of the competition.

Read our white paper The Modern Analytics Stack for more on this topic.

Written by Anodot

Anodot leads in Autonomous Business Monitoring, offering real-time incident detection and innovative cloud cost management solutions with a primary focus on partnerships and MSP collaboration. Our machine learning platform not only identifies business incidents promptly but also optimizes cloud resources, reducing waste. By reducing alert noise by up to 95 percent and slashing time to detection by as much as 80 percent, Anodot has helped customers recover millions in time and revenue.

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