Reduce Time-to-Question and Improve BI

Woman pondering answers and questions.Businesses have increasing amounts of data to cleanse, as research suggests that data is growing at a rate of 40% of each year into the next decade, driven by increased online activity and usage of smart devices. (ABI, 2013). (The New York Times, 2014 ).

The consumerization of data means that business users should be able to access and analyze IoT data from devices and other data sources to get insights.

Time-to-Question a Better Metric

Business Intelligence is based on a time-to-answer metric: How long does it take until I get the data I need? For businesses to analyze their data, organizations are focusing on reducing the initial time-to-answer process, and then reducing further iterations of this cycle.

Instead of time-to-answer, businesses should move towards time-to-question, namely, the time to get to the next question once the answer has been revealed.

Research by Gartner has shown that user adoption for business intelligence is less than 30%. It also shows that less than 30% of them use the technology. Clearly a disconnect is happening between IT and the work done by the business users. Barriers to user adoption should be removed.

BI Evolves Under User Demand

To combat these issues, the Business Intelligence industry evolved. This change was wrought by the business users, who demanded clean data sources on a strong technical apparatus that they could mash and merge together, and they were empowered to connect to these sources. This is typified by the phrase ‘self-service business intelligence’.

The intention is that the business users understand the structured data sources, and the technical teams have a robust technical structure in place. Excel remained the default for working with data, but the business users had more power to mash data together.

Centralized Approach Introduces Delay

Self-service business intelligence was not faultless. Business users remained dissatisfied with the highly-centralized IT approach as they still relied on other people to give them the data they needed. This issue introduced a delay, which increased the ‘time to answer’ metric whilst not recognizing that this feeds into the ‘time to question’ metric.

The change in data will mean that everyone in the organisation needs the ability to find data for analytics, quickly and with quality. The role of the domain expert will develop based on their data expertise. They become familiar with the questions that the business needs to ask to be successful, thereby mirroring a new emphasis on data-driven decision making and reducing the time-to-question cycle.

Reduce Bottlenecks of Data with Simplicity

To be effective, simply give users what they want; the data where it should be. There should be an acceptance that business users will want access to IoT sources in the same way as any other source. These can be exasperating and non-intuitive. The bottlenecks often mean that more silos are created, and there is a risk that this will become more prevalent as more unstructured sources are added. Simplicity is vital in the race for the business analyst and user, and their goal is to reduce time and effort in getting the insights that they want, whilst increasing efficiency and value.

Embedding BI & Infusing Analytics

Increasing the velocity of decision making, improving time to question and making the user experience more intuitive and effortless requires a different and decentralized analytics approach. Embedding BI capabilities into the application users engage on a daily basis, and ultimately infusing analytics into the workflow of that application, is a way to provide users with a more intuitive and impactful experience. As data sets grow with IoT, the use of the application’s existing workflows, user roles and access rights provides a needed complement to an embedded BI platform’s analytical capabilities.

Jen Stirrup

Jen Stirrup, (@JenStirrup), author, SQL Server MVP and PASS Director-At-Large (Elect), is a well-known Business Intelligence and Data Visualisation expert, author, data strategist, and technical community advocate who has been peer-recognised as one of the top 100 most global influential tweeters on Big Data topics. Jen has presented at TechEd North America and Europe, Summit, SQLBits, as well as at SQLSaturday events in Europe and the US. She is a former Awardee of PASS’s prestigious PASSion Award in 2012.

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