The analytics-driven business has become the rule, not the exception, and with good reason. Research has shown that analytics-driven businesses are more likely to have superior financial performance, make timely decisions, and execute their plans. No wonder growth in business investment in analytics continues apace, slated to reach over $70 billion (US) by 2022, according to Stratistics MRC.
But paradoxically, fewer businesses are driving appreciable differentiation from that investment, according to MIT Sloan Management Review. “But the percentage of organizations gaining competitive advantage from analytics has declined significantly over the last two years.”
Why? There are three primary roadblocks to squeezing competitive advantage from analytics: a lack of staff with the right training for the new analytics world (The Canadian Big Data Consortium estimates Canada’s deep analytics skills gap at as much as 19,000, with a gap of 150,000 in interpretive analyst roles); disjointed and inefficient workflow processes; and siloed technology that makes communication and collaboration more difficult.
Data Visualization and Data Analytics: A powerful duo
Is visualization the same as performing analytics?
Voices at the table
There are people from different business functions that all have a stake in the analytics process, and all need something different from the system. Not to give short shrift to the IT department, without whom operationalizing any kind of analytics regimen would be impossible, the three key voices at the table are:
The decisionmaker (DM). This is where the process of creating insight begins and ends. He or she asks the questions and marries analytical insight with intuition to support the best decisions.
The business analyst (BA). This person is the go-between, the translator who speaks both the scientific and business languages—a citizen data scientist, a storyteller.
The data scientist (DS). The magician who turns questions into queries, the data scientist doesn’t just crunch numbers. He or she knows which numbers to crunch, what data is needed, and where to find it.
Each of these persona have different skillsets and backgrounds, live in different operational worlds, and speak different business languages. They also each need something different from analytics: the DM needs actionable insight and interactivity; the BA needs the flexibility to ask questions and tell stories, an easy-to-use interface, and some coding capability; the DS must have advanced coding capabilities, a variety of tools, and the ability to scale without redefining codes.
Data visualization offers each of these persona a window into the data, the ability to manipulate data to glean insight, and a common visual language that crosses operational silos and drives real collaboration.
Data visualization is not a substitute or replacement for the advanced analytics techniques used by the DS, or the technology that empowers them. But it does add significant capabilities to the analytics toolbox: data exploration; location analytics to add geographic context; interactive reporting and governance; self-service data preparation to support the discovery process and make analytics as agile as possible.
Organizations ready to move beyond those capabilities can avail themselves of artificial intelligence (AI) technologies like natural language processing, machine learning and interactive predictive modeling.
When we look at those three pain points that are squeezing enterprise analytics performance—people, processes and technology—we can see how data visualization can help ameliorate their impact. Data visualization won’t close the analytics skills gap, but it will make analytics accessible to users who don’t have the sophisticated knowledge of a data scientist. It helps bridge the operational silos that sometimes detract from the agility of enterprise processes. And it provides a framework of tools across technologies to drive insight and value from data, bridging the analytical cornerstones of data, discovery and deployment.
Most importantly, data visualization provides a platform for collaboration across business functions, a shared vision of roads to desired outcomes, a common graphical language that everyone in the decision-making process can speak.
Simplify. Experiment. Collaborate. These are the three business imperatives that data visualization makes possible. Watch our free on-demand Webcast to learn how.
About the Authors
Tina Schweihofer is Pre-Sales Manager, Data Sciences, SAS Canada. She is passionate about helping people understand how high performance analytics, coupled with the right data strategy can deliver real business benefits. Tina leads a talented data sciences team that helps organizations across industries apply analytics to solve unique business problems using SAS. Tina can be reach at email@example.com
Kieran DeFilippis is a Solution Specialist, Data Sciences with SAS Canada. By leveraging best-in-class software and modern analytic techniques, Kieran helps businesses uncover new insights. He enjoys the challenge of making complex tasks just a little bit simpler through the advocacy of data driven decision making. Kieran can be reached at firstname.lastname@example.org
- Data lineage: Making artificial intelligence smarterFor AI to reach its full potential, the data feeding its algorithms and models needs to be well-understood. Data lineage plays a vital role in understanding data, making it a foundational principle of AI.
- Key questions to kick off your data analytics projectsThere’s no single blueprint for starting a data analytics project. Technology expert Phil Simon suggests considering these ten questions as a preliminary guide.
- The connected consumer: IoT's impact on the future of retailThe IoT and the connected consumer are set to revolutionize retail. Here are a few ways companies can take advantage of technology advancements.