7 embedded analytics trends for building your 2021 data strategy
Feb 16, 2021
The importance of data and analytics as a key to overcoming the kinds of shocks that companies experienced in the past year is undeniable. As we lean into the digital future and look to build more resilient businesses and economies, embedded analytics will play a crucial role by empowering people to find analytics insights in the contexts where they are most useful, and in a fashion natural to their decision making processes.
It’s clear that embedded analytics will only become more prevalent in the future. With that in mind, we’ve identified seven embedded analytics trends for you to consider while building your data strategy in 2021.
1. Companies will move beyond BI to data experiences for better decision making
Many organizations rely on reports and dashboards to better understand what is happening within their business. In 2020, we saw more companies move beyond this traditional way of consuming data insights, and on to creating data experiences that position analytics directly within their context — and closer to decision points. We expect to see this trend continue in 2021.
An example of such a data experience is a company using an embedded advertisement “bid bot” to optimize ad auction bids in real time across a dozen different media platforms in order to increase digital ad efficiency. Employees receive automated alerts so they can adjust their bidding to increase ad investments that bring in high-value customers, and decrease ad investments that bring in low-value customers.
With data proliferating — and growing — in all organizations across the globe, companies enabling their teams to interact with data directly (and within the context of their daily tasks) will create a winning data strategy. This way of working moves organizations beyond BI toward a culture of data experiences that includes recommended, automatic actions as a part of daily workflows. And, inevitably, faster and more informed decisions will result... which leads us to our second trend.
2. This year will see the rise of data application developer communities
To create these data experiences, companies will deploy a data application platform as part of their 2021 data strategy. We predict this will lead to the rise of data application developer communities that create point solutions best tailored to their company’s needs.
Today, independent software vendors and developers use the Looker data application platform to create applications and post them in the Looker Marketplace. We envision a future in which companies internally develop such data experiences and apps, or simply download them from an app store or marketplace. Data experiences and data application developers will give rise to a culture of community-generated analytics insights that moves companies beyond self-service to crowd-sourced analytics, leveraging the collective knowledge of the entire organization.
The data application platform that provides data to developer communities will be crucial to the success of this trend. With data delivery streamlined into the hands of the developers, the next logical step is that developer communities will form. And with those communities busy building applications, it seems natural that the next trend we’ll see is...
3. The rise of data application marketplaces
These marketplaces will have a network effect and serve as a central store where community knowledge is searched, shared, and deployed.
The Looker Marketplace has seen a steady increase in developers and end users coming together to ideate, search, and deploy Looker Blocks, actions and analytic applications.
The end result is a directory of pre-built solutions searchable by use case, industry, and author that Looker customers can download and easily use. These marketplaces will also lead developers to monetize their work (if they so choose).
4. End users will expect to embed analytics through self-service components
Employees are typically willing and eager to learn more about their company’s business. Offering them as much information as possible heightens their ability to discover useful information on their own — and embedding analytics based on the needs of specific organizational roles is a great way to deliver that information. At Looker, we have seen a steady increase in the number of customer users who are investigating data on their own as their data literacy improves. That increasing level of literacy leads to more questions and more demand for information. It’s a positive feedback loop where data self-service becomes crucial: those curious users can then go to a data application marketplace, find the component that will help them answer their questions, and install it in order to get to the information they need.
Looker customer GoSpotCheck (GSC), a mobile task management platform for enterprise teams that connects frontline workers with corporate goals and directives, offers an example of how powerful it is when employees have the data they need embedded into their workflows. One GSC customer, a top 5 casual restaurant franchise owner, built custom dashboards and set them to auto-refresh every two minutes. Large monitors in the staff service area give team members full visibility to their location’s performance while they work. This helps prompt team members to complete tasks, helps monitor logistics (including cooler and freezer temperatures to protect food safety), and motivates workers with hourly sales and customer counts throughout the day. As a result, the performance of the location where this was trialed rose well above others in the franchise. The owner later expanded the practice to multiple locations.
5. AI/ML will be infused into embedded workflows
We’ve been talking for years about the potential use of AI and ML to help make better decisions. These technologies give us access to predictive insights, helping us to better consider the future as we weigh options. We predict that this year, AI and ML will become a part of embedded analytics workflows.
AI and ML services are now available as pluggable components, many of which are purpose built for industry and department use cases. For example, the Looker for Google Marketing Platform (GMP) Block uses such pluggable AI services, and delivers AI/ML-infused insights for web and campaign analytics.
This makes it easier to predict desirable or undesirable outcomes that help answer context-specific questions, such as which segments of users to spend more on by understanding who has the highest propensity to purchase.
6. The no-code / low-code citizen developer
The trend of using graphical user interfaces (think a visual drag and drop method) for building applications has been with us for a while. So far we’ve mostly seen simple applications developed this way, because the interfaces can only handle so much complexity.
We see this trend changing due to better platform capabilities that allow “citizen developers” to access packaged components and build applications more quickly. These packages will make it possible for the interfaces to manage more complex data sources.
We predict that more organizations will start to embrace a citizen developer approach, similar to what GoSpotCheck (GSC) has done. GSC’s customer success managers are able to walk their customers through how to make changes to their BI models, or can confidently make the edits themselves. Before using Looker, this type of request would have required technical labor. Now, customer success and support teams can self-serve to meet these requests more quickly, deepening customer relationships in the process.
7. Composable analytics will become prevalent
All of the aforementioned trends combined create what Gartner calls composable analytics: analytics that are embedded in context, can be delivered by no / low-code developers, and which originate from the consumer space1 rather than the enterprise IT sector.
Many of the 2,000+ organizations using the Looker data application platform leverage Looker Blocks to build such composable analytics applications.
An API-first development philosophy will start to take hold. We predict improved APIs from multiple analytics vendors, integrated into a data platform that enables companies to create composable analytics.
Here at Google Cloud, we are very excited about embedded analytics in 2021. We want to help companies use data to build more resilient, dynamic, and automated organizations that learn from data and act in real time to make better decisions and, ultimately, succeed in the market.
To hear more about embedding and how leading organizations are using embedded analytics to be more competitive, check out our upcoming webinar, Why Next-Gen Embedded Analytics Matter for Digital Transformation (2/17/21 at 11am PT / 2pm ET).
1 Gartner Glossary - Consumerization