A Look Inside the Modern Analytics Stack

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In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times as likely to surpass them in customer loyalty.

There are many different types of tools in the analytics stack – some of which are specialized in a single solution while others offer end-to-end analytics. The modern analytics software stack has been undergoing a fundamental restructuring in recent years due largely to advances in AI and machine learning.

How AI Is Reshaping Analytics

Data analytics is about taking raw data from various sources and extracting insights in order to make better business decisions. These insights can come in the form of analyzing past performance, forecasting future demand, or detecting anomalies in real time.

Having the right AI-driven analytics stack allows companies to stay competitive, agile, and profitable in data-driven industries during times of change.

Al analytics stacks are expected to perform similar basic functions such as making data accessible and enhancing data security and governance. This technology can also be layered on top of existing tools to extend coverage and scale, enabling teams to monitor more with less.

Using AI, companies are able to analyze 100% of the data they collect, something that cannot be achieved by traditional BI dashboards. It is also being used to facilitate operational decision-making in real time, with some solutions providing end-users with a “smart push” – actionable insights attached to each notification.

And with fewer moving parts, the AI analytics stack allows for less manual intervention and transformations, leading to a significant reduction in data handling processes, insight generation, and human error.

Five Essentials for Modern Analytics

There are five key building blocks in the stack:

  • Data pipelines: Before data reaches the data lake, it needs to be cleaned, prepared, formatted, and sometimes enriched. This is done through ETL processes, which involve extracting, transforming, and loading the data into the warehouse. Leading data pipeline vendors market include Skyvia, Rivery, Segment, Fivetran, mParticle, and Tealium.
  • Data lake: While your data sources are not part of the actual analytics stack, they are the fuel that drives insights. The first step to building an effective analytics stack is building one place that unifies data from various sources (IT, application, business, etc.) and consolidating them into a single pipeline.Now that the data is in a central repository it’s ready to be queried and analyzed. To overcome the challenge of rapidly increasing data creation and ingestion, cloud storage has become the choice for many organizations as they offer the speed, security, and availability that’s required of big data. Leading data warehousing vendors include Amazon Redshift, Google BigQuery, Snowflake, and Panoply.
  • Visualization: Once data is properly stored, this part of the stack combines interactive tools for data analytics, visualization, and collaboration amongst stakeholders. Leading data analytics and visualization vendors include MicroStrategy, Sisense, Tableau, and Looker.
  • Business monitoring and AI analytics: While visualization solutions are seeing under 1% of data is being collected, autonomous monitoring is the only proven way to analyze 100% of the data you capture. Across the data silos, many organizations collect millions of data events each day. Effective business monitoring allows companies to detect revenue-impactful issues in real time before they turn into incidents that affect the customer experience, operations, or the bottom line. The ability to monitor data and detect anomalies in real time is the secret weapon of the stack, as it enables data-driven organizations to increase their visibility and transparency into various operations such as their financial health, product usage, and sales and marketing performance. Ultimately, this ensures the fastest possible time to detect customer experience issues and allows teams to fix them before the customer notices.

AI-driven analytics software has become one of the most important technologies in the 21st century to adapt to the demands of big data. The ability to take raw data and turn it into strategic insights has become an essential competitive advantage in the data-driven economy. When it comes to making informed decisions in near real time, AI-driven business monitoring and anomaly detection are the secret weapon in the modern analytics stack.

In terms of the modern analytics stack, there are both tools that offer end-to-end analytics as well as best-in-breed options that specialize in a single solution. In reviewing the available analytics software tools on the market, it’s important for business leaders to consider whether the solution is keeping up with technological  trends such as increased automation using machine learning, advanced features such as proactive insights in real time, and adaptive forecasting that automatically optimizes its forecasts as conditions change.

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