Not long ago, effective data analytics were seen as a form of differentiation. Today they are table stakes, as companies become increasingly data-driven to help grow their businesses and remain competitive. As business intelligence in the enterprise shifts from the purview of data scientists and IT-led reporting into the hands of non-technical business users, agile analytics are essential.

Embedding analytics deep within an application that employees are already using every day not only provides a seamless user experience, but also empowers employees to share dashboards, reports, and other analytics they create across their organization, with little to no support from IT.

Growth in the embedded analytics market continues to be stoked, in large part, by the rising demand for self-service analytics. Business users of all stripes need to help them connect complex data, run queries, and create insightful reports wherever they are, on whatever device they’re using. They also increasingly demand access to analytics within the business applications they use most often.

Adding self-service capabilities within embedded analytics gives users even greater latitude to customize their applications without external help. For example, a user working with the same analytics every day may discover a unique pattern and want to explore it further through visualization. Built-in self-service capabilities could allow the users to do this on their own.

Self-service analytics adoption can come with its own challenges, however. Two oft-cited obstacles to adoption are concern over the lack of business user skills and a process for ensuring data quality. While these concerns are real for users of stand-alone self-service tools, they’re non-starters for users of embedded self-service.

Closing the User Skills Gap

Self-service is sold on the idea that business users can access statistical data, run queries, and draw conclusions themselves without increasing the burden on IT.

Being highly empowered is a positive thing; however, being able to use a tool is very different from knowing how to manipulate and interpret data inside the tool. Without experience or , most business users don’t  intuitively know which data sets to use and how to connect them to obtain the desired information. They don’t know how best to handle missing data or determine if additional or better data is needed. Their stand-alone self-service tools also can’t help them.

This is one of the biggest advantages embedded analytics have over stand-alone self-service systems. In embedded self-service, the data source, authorization, data quality, and other input are each handled or set up by the host application. This shifts the knowledge burden away from users.

Businesses can’t afford to allow employees to fly blind when it comes to data analytics. Even with a solid embedded self-service solution, it behooves companies to figure out the specific skills needed to produce different levels of analytics and provide employees tailored training to help them excel within their level.

Additionally, democratized access to data doesn’t equate to lack of control. User authorization procedures and access rights shouldn’t go away. Throughout an organization, key stakeholders need to decide who’s going to do what, and only the appropriate users should have access to the reports and data. A good embedded analytics solution will provide this needed control.

Ensuring Data Quality

The credibility of embedded and self-service analytics pivots on the quality of the data. Users need to trust the data enough to confidently use it for important business decisions. Inaccurate and unreliable data will undermine the analytics very quickly.

An embedded self-service solution has data quality covered. The host application identifies and resolves all data quality issues, checking for:

  • completeness of the data
  • consistency among data values throughout the data set
  • data duplication
  • conformity of the data to specified standards
  • accuracy
  • integrity of the data as it moves from system to system
  • and more

Data cleansing should be part and parcel of this process. Periodic checks using data cleansing software will help prevent inaccuracies and errors that can distort the data.

A Culture of Analytics

As more organizations require their employees to report on metrics, it’s more important than ever to provide users with analytics within the applications they regularly use.

Many people- and process-centric considerations are involved in building a data-driven company. Effective organizations are fueled by a shared mentality that recognizes data and analytics as launchpads toward more objective, efficient decision-making. They support a culture of analytics.

In such a culture, it’s second nature for employees to use data and insights to inform their decisions. Conversations are transformed into ones that include facts and data as well as the demand for them if they don’t exist. Companies rely on quantifiable criteria, so they need to be exceptionally good at inspiring habits that promote solid analytics.

Building a culture of analytics takes time. Embedded self-service analytics place the ability to analyze data directly within a user’s workflow, unencumbered by concerns over user skills and data quality that come with stand-alone self-service tools.

A culture of analytics is lived and must emanate from the top of a company down. Executives and senior leaders need to champion the merits of data-driven decision-making. They need to mine their own company for real-life examples with quantifiable business results and then evangelize those examples. Nothing motivates employees better than a leader who’s walking the walk.

The most successful data-driven companies know that true business value doesn’t derive from data or embedded analytics alone, but rather from a well-nurtured combination of the right technology, talent, and culture.

To learn more about the business benefits and mechanics of embedded self-service analytics, visit www.jinfonet.com.

References:

Gartner, “Gartner Says Worldwide Business Intelligence and Analytics Market to Reach $18.3 Billion in 2017”

TDWI, “5 Rules for Successful Self-Service Analytics”

ReportLinker.com, “Embedded Analytics Market: Global Forecast until 2022,” MarketsandMarkets

Zoomdata.com, “What’s Driving the Need for Embedded Analytics with Big Data?” Mike Lock, Aberdeen Group

Measuring the Digital “Practical Steps to Building an Analytics Culture,” Gary Angel

International Institute for Analytics, “Are Analytics Truly Self-Service?”



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