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The Embedded Analytics market is growing at a rapid rate and will be worth $51.78 billion by 2022, and over the next few years, most organizations will begin to transform their traditional analytical techniques for analyzing business data to more advanced techniques using Embedded Analytics. If they don’t, they may risk getting left behind their competition.
In 2018, with the aid of innovations in Embedded Analytics and Business Intelligence (BI), Data Analytics will begin to be much more accessible to professionals with various backgrounds and specialties across an organization. Analytics and Business Intelligence will no longer be accessible to analysts alone, and professionals across organizations will be empowered to make data-driven decisions with Embedded Analytics tools that are easier to use than current tools. In fact, Gartner predicts that 80 percent of organizations will work to increase data literacy across their workforces over the next few years as innovations in Embedded Analytics and embedded BI continue to surface with innovations that make for a more user-friendly experience.
Below are the innovations and trends for Embedded Analytics you’ll want to watch for, organized by structure and capabilities, and also by features.
Structure and Capabilities
Embedded Analytics will own a more decentralized structure with robust integration capabilities, more options in the cloud, and adaptive security. It will also be easier for users of various backgrounds across an organization to use.
Various Hybrid Cloud Options
As the amount of data that organizations use continues to grow at alarming rates, businesses will consider moving some archivable data out of the Cloud and into on-premises databases for backup and security purposes. Platform capabilities for accessing, integrating, transforming, and loading data into a self-contained performance engine with the ability to index, manage, and refresh data loads (self-contained ETL and data storage) will be extremely important. In addition, many organizations will opt for Multi-Cloud strategies where they have data in multiple Cloud locations to help with data security, costs, and performance. Overall, in 2018, there will be a variety of either Multi-Cloud or on-premises Hybrid options (or a mixture of both).
Decentralized Analytics’ Evolution to Governed Data Discovery
As Embedded Analytics begin to be easier to understand for users across an organization, analytics will become decentralized and Data Discovery will increase but need to be better governed in 2018. Self-Service Analytics platforms allow for decentralized workflows and for users to prepare their own data analysis with much less reliance on the IT department. As decentralized Analytics becomes more prevalent across an organization, the risk of multiple sources of the truth (of the data) and the integrity of data insight will become a serious concern. So, successful organizations will have to evolve their decentralized Analytics to an approach that embraces governed Data Discovery over time. Governed Data Discovery will permit individuals across an organization to analyze data for their own use cases, but will require that data analyses and insights still fit within the governed parameters of the organization’s business procedures, policies, objectives, etc.
Adaptive Security Architecture
With Hybrid Cloud options for Embedded Analytics gaining popularity in 2018, as well as decentralized data workflows across an organization, security will be a more prevalent concern. Security for Embedded Analytics and BI will need to be highly adaptive. Organizations will need to begin implementing secure keys and authentications for APIs (Application Programming Interfaces), as well as user authentications and permissions. They’ll also need to adopt an adaptive security architecture that supports mobile devices.
Integrated Platforms and Workflows
Embedded Analytics will be integrated into a variety of business platforms in 2018 as organizations continue to build architectures that involve diverse technology stacks and software that work together through more robust API Integrations. Better integrated platforms will offer organizations the ability to have multiple data sources that sync up and work together, allowing for rapid analyses while decreasing redundant and corrupt data.
Enhanced Mobile Capabilities
As organizations continue to adopt BYOD (Bring Your Own Device) alongside decentralized analytics strategies, they’ll also incorporate Embedded Analytics vendors that make it easier for users to develop and deliver content to mobile devices in a publishing and/or interactive mode. Users will be able to use their touch screens, cameras, and GPS locations to access and organize data from anywhere at any time.
Above all else, in 2018 and beyond, Embedded Analytics tools will need to own self-service capabilities and will start to embrace more user experience (UX)-enhanced designs. More users across an organization will need to easily be able to access, view, understand, and manipulate data. So, data catalogs, dashboards, visualizations, and preparations will need to be built for tech-savvy and non-tech-savvy professionals alike. Self-service tools will own capabilities like drag-and-drop cleansing and modeling, and will offer the creation of analytic models. Machine-Learning-enabled semantic auto discovery, intelligent joins and profiling, hierarchy generation, and data lineage and blending on varied and multi-structured data sources will become more mainstream and will empower self-service capabilities.
Most new or improved Embedded Analytics features in 2018 will be based on UX — they’ll be easier to use and will be built around the fact that Embedded Analytics platforms aren’t just for Data Scientists anymore. Below are the Embedded Analytics and embedded BI features to watch for in 2018.
As Embedded Analytics tools gain popularity, more organizations will want to customize their reports and features with their own brand and logos — a term called white labeling. They’ll want their users to feel as if they are always in their own organization’s platforms, and that users have a consistent experience when accessing and analyzing Analytics from various data sources.
Dashboards with Interactive Reports and Visual Exploration
Embedded Analytics tools will need to offer contextualized and interactive reporting to their users. Dashboards will need to offer users the opportunity to explore data via a wide-range of visualization options that go beyond basic pie, bar, and line charts. Visualizations will need to include trellis, heat, tree maps, scatter plots, and other interactive and specialized visuals. Users will expect opportunities to analyze and manipulate data by interacting directly with a visual representation of it.
In addition, dashboards should be supported offline and on mobile devices and will need to offer highly visual content with advanced and geospatial analytics.
Extended Smart Data Discovery
Smart Data Discovery will automatically visualize the most important findings and insights in data for the user and offer more robust predictive capabilities. It will uncover associations, exceptions, clusters, forecasting, and predictions in data that are relevant to users without necessitating them to build their own data models or write their own algorithms or queries. This feature (brought to the forefront with robust Artificial Intelligence capabilities) will bring organizations much closer to prescriptive analytics in the future, where Embedded Analytics tools will be able to tell users the courses of action they should take to reach an intended business goal based on collected and analyzed data.
Optimized Data Strategies and Data Preparation
More organizations will want their Embedded Analytics tools to bring data to one central location from disparate sources, as well as an optimized repository for data reporting. Embedded Data Analytics features will need to further optimize data and better stream data so that it is centralized, consistently reliable, rapidly accessible in real time, and easily prepared into visualized reports accessible on interactive dashboards.
For Embedded Analytics tools to be optimized, they’ll need to have secure write-back capabilities. Such capabilities enable better Metadata Management across sources and data stores. With secure, and better, integrated write-backs, multiple data sources can be updated in real time as users conduct their various analyses on different platforms and in different interfaces.
Natural Language Generation
This feature will allow for more “Conversational Analytics” and will be implemented through smart data discovery features and capabilities. Users will be able to easily process, query, and generate data reporting and visualizations through natural language voice search. However, each Embedded Analytics integration with this feature will be highly-customized to individual-use cases and businesses processes per organization.
Users will vary in how they need to use Embedded Analytics, as well as the level of access they will require. Session variables allow each user to securely get their own customized view of data and other features without forcing organizations to manage multiple versions of the same applications.
Automated Scheduling for Reports
More users are beginning to request scheduling features with their Embedded Analytics, since many reporting needs across an organization are recurring. By integrating scheduling features with your Embedded Analytics, your organization will be able to automate alerts and reports based on specific events often determined by recurring time schedules.
The innovations and trends for Embedded Analytics listed above focus on making analytics more accessible and easy to use across an organization. In 2018, your organization will want to focus on creating structures and capabilities, as well as adopting various features that make your Embedded Analytics easier and more efficient to use. Don’t let your organization get left behind the competition in 2018.
- Top 10 Analytics and Business Intelligence Trends for 2018 Blog. Published 12/13/17. Last Accessed 12/27/17.
- Critical Capabilities for Business Intelligence and Analytics Platforms Report. Published 3/2/2017. Last Accessed 12/27/2017.
- Build Adaptive Security Architecture into Your Organization. Smarter with Gartner Blog. Published 6/30/17. Last Accessed 12/27/17.
- Information Management. Opinion Predictions 2018: 11 Top Trends Driving Business Intelligence. Published 12/20/17. Last Accessed 12/27/17.
- The Future of Embedded Analytics. Last Accessed 12/17/17.
- Smarter and Faster: BI and Visual Analytics Trends in the New Year. Published 12/14/17. Last Accessed 12/27/17.
- Data to Decisions: New Trends in Leveraging Analytics. Published 8/16/17. Last Accessed 12/27/17.