Metrics Store: A New Critical Capability for Analytics and Business Intelligence Platforms

In today's data-driven world, businesses constantly collect data from various sources. However, collecting data is just the beginning, as it needs to be analyzed to derive insights and make informed decisions. This is where analytics and business intelligence(ABI) platforms come into the picture. These platforms help businesses to make sense of their data and gain valuable insights that can help them to improve their decision-making process.

Why are Analytics and Business Intelligence (ABI) Platforms Important?

An ABI platform is the foundation of an organization's business strategy. It makes your organization's data usable. Effectively utilizing data is the core purpose of business intelligence, and ABI platforms make this process more efficient, creating a more intimate relationship between your data and your business strategy.

One of the main benefits of an ABI platform is that it allows businesses to gain a deeper understanding of their data. A BI platform can provide users with a comprehensive view of their business operations by bringing together data from various sources. This can help businesses identify trends, patterns, and anomalies they may have missed. With this knowledge, businesses can make more informed decisions to improve their operations.

Another benefit of an ABI platform is that it can help businesses save time and resources. Rather than spending hours manually analyzing data, an ABI platform can automate the process, allowing users to focus on more important tasks. This can help businesses become more efficient and productive, ultimately leading to increased revenue and profitability.

Finally, an ABI platform can help businesses stay ahead of the competition. By providing users with real-time insights, businesses can quickly identify opportunities and take action before their competitors do. This can give businesses a significant advantage in the marketplace and help them stay ahead of the curve.

Critical Capabilities of Analytics and Business Intelligence (ABI) Platforms

What capabilities are analytics and business intelligence platforms expected to deliver? Here are the 12 critical capabilities defined in Gartner's April 2023 Magic Quadrant for Analytics and Business Intelligence Platforms:

• Automated insights - A core attribute of augmented analytics. This is the ability to apply machine learning (ML) techniques to automatically generate insights for end users (for example, by identifying the most important attributes in a dataset).

• Analytics catalog - This refers to the product’s ability to display analytic content to make it easy to find and consume. The catalog is searchable and makes recommendations to users.

• Data preparation - Data preparation includes support for drag-and-drop, user-driven combination of data from different sources, and the creation of analytic models (such as user-defined measures, sets, groups and hierarchies).

• Data source connectivity - Data source connectivity capabilities enable users to connect to and ingest structured data contained in various types of storage platforms, both on-premises and in the cloud.

• Data storytelling - Data storytelling is the ability to combine interactive data visualization with narrative techniques to package and deliver insights in a compelling, easily understood form for presentation to decision makers.

• Data visualization - Data visualization involves support for highly interactive dashboards and exploration of data through the manipulation of chart images. Included is an array of visualization options that go beyond those of pie, bar and line charts, such as heat and tree maps, geographic maps, scatter plots and other special-purpose visuals.

• Governance - Governance capabilities track usage and manage how information is shared and promoted.

• Natural language query - The natural language query (NLQ) capability enables users to ask questions of the data using terms that are either typed into a search box or spoken.

• Reporting - The reporting capability provides pixel-perfect, paginated reports that can be scheduled and burst into a large user community.

• Data science integration - Capabilities that enable augmented development and prototyping of composable data science and machine learning (DSML) models by citizen data scientists and data scientists with integration into the broader data science and machine learning ecosystem.

• Metrics store - The ability to provide a virtualized layer that allows users to create and define metrics as code, govern those metrics from data warehouses, and service all downstream analytics, data science and business applications. This also includes capabilities such as goal management.

• Collaboration - Analytics collaboration is the application of collaboration capabilities to analytics workstreams for organizations that want to provide an environment where a broad spectrum of users can simultaneously co-produce an analytics project.

Among them are three new critical capabilities: data science integration, metrics store, and collaboration. These capabilities enable organizations to test hypotheses, articulate measures, and reach consensus in an efficient fashion, resulting in profitable business decisions.

Metrics Store

This year, Gartner introduced "Metrics store" as a new critical capability for analytics and business intelligence platforms. A metrics store is, in the simplest words, a middle layer between upstream data warehouses/data sources and downstream business applications.

In the past, metrics were usually defined in data warehouses or ABI applications. This is causing increasing pains for organizations as data volume and complexity grow. The rise of metrics store is an attempt to find solutions for the following challenges organizations are facing:

• Inconsistency of key metrics definition across business units causing discrepancy for decision-making: Different teams will get entirely different reporting numbers for very simple business questions. To make matters worse, no one knows exactly which number is correct.

• Inability to reuse defined metrics in more business applications that go beyond just dashboards: for example, to reduce user churn, the product growth team hopes to timely obtain information about inactive users in the past 30 days adopting activation strategies, such as giving users a free renewal. Defining and analyzing metrics in ABI alone is unable to meet such requirements, which will involve feeding metrics to business applications such as CRM systems.

• Challenges for business users to define metrics with SQL: as Ankur Goyal and Alana Anderson in their article, “Headless Business Intelligence”, puts it -

"Simple tasks like user sessionization, funnel analysis, and data deduplication often require 1,000+ line SQL queries which must be written by expert data engineers or generated programmatically."

• High complexity of data architecture and pipelines result in low efficiency of data analytics: materializing metrics in the data warehouse layer is a commonly used solution. The data warehouse supports defining metrics in views and then letting other tools query the views.

Kyligence Zen: More Than a Metrics Store

Kyligence Zen is more than a metrics store. It is a comprehensive platform that offers a wide range of features to empower organizations in their data analytics journey.

Sitting between data sources and business applications, Kyligence Zen creates the perfect opportunity for metrics to be centrally defined and consumed. It enables users, technical and non-technical, to define metrics using low code or no code in a streamlined fashion.

In addition to metrics management, it also provides data exploration, data modeling, and data governance capabilities. With its high-performance OLAP platform and AI-augmented engine, Kyligence Zen enables users to easily identify datasets and metrics, and generate insights in a more efficient manner.

Kyligence Zen is designed to be user-friendly and intuitive, providing a simple interface that is easy to use for users of all skill levels. It offers a variety of customization options, enabling users to tailor the platform to their specific requirements. With its robust capabilities and user-friendly interface, Kyligence Zen is the ideal solution for organizations looking to optimize their data analytics processes.

To find out more about Kyligence Zen and how it can benefit your organization, visit kyligence.io/zen or try it for free at zen.kyligence.io/en/user/register.

References:

1. Gartner, Magic Quadrant for Analytics and Business Intelligence Platforms (2023)
2. Joanna He, Understanding the Metrics Store(2023)
3. Ankur Goyal, Alana Anderson, Headless Business Intelligence(2021)