You’re Leaving Data on the Table….Maximizing the Value of Your Analytics
We’ve all seen the beautiful data visualizations the various analytics applications are creating. Today’s variety of charts and graphs are quite astounding. Even the U.S. government is making beautiful graphs these days with its census data. Take a quick peek.
It’s easy to get caught up in the hype. They’re sexy, glamorous and get all of the attention. However, the reality is that when you get to the dashboard / visualization phase, the majority of the work is already done. Anyone who’s ever worked in the business intelligence world knows that to be able to create reports and graphs it takes a team of data technicians and a ton of expensive software.
Data has to be extracted from different sources, transformed to work in the target database, have business rules applied, data quality issues fixed, and loaded into a data warehouse that supports the dashboard tool your IT department chose for you. It’s expensive and only gets your users to static charts leaving them to figure out what to do next.
Today, there is so much more that can be done with data to help your business unlock maximum value from your analytics. In order to understand how, we need to look at the full data lifecycle. This includes both pre and post analytics.
As you probably know, data is growing at an astonishing rate. In the past 10 years, data has grown from 2 zettabytes in 2010 to 59 in 2020 and is forecasted to reach 149 zettabytes in 2024 according to Statista. That’s an incredible growth rate!
Data is not just coming from other systems within the company, but from devices, sensors, and the Internet of Things (IoT). It’s coming from social media, images, video, audio, geo-spatial, and many more sources. It’s also no longer about internal data as there are many services that sell third-party data as well to augment your internal data.
Meaningful analysis of your business landscape becomes possible when you move beyond internal, relational data to incorporate the variety and size of data available. For example, leveraging social media data can provide insights on what is trending and how customers are talking about your business.
Naturally, simply incorporating additional data will not be enough. We must look at the post-analysis side of the data lifecycle to further our analysis. Many companies still have a team of business analysts to create and analyze reports. However, with today’s computing power, we know that machines can comb through data and find anomalies much faster and more efficiently than humans.
Artificial intelligence and machine learning models are becoming more widely available. They can describe and predict patterns and trends in your data beyond what any human could ever uncover. Although humans will always be involved in analysis, these tools augment human intelligence to provide much more insights and value from the data in tiny fractions of the time.
Lastly, automation completes the modern data lifecycle, but is the step most traditional business intelligence are unable to address. This phase is where we move from insights to action. When we automate the process between insight and action, the result is a real-time workflow that handles cases immediately with no human intervention. This is the primary reason Qrvey was built with a no-code automation workflow builder from the very beginning to address the gaps left by legacy business intelligence tools.
To support today’s modern data lifecycle and get the maximum value from analytics, it requires thinking about the entire lifecycle as a single process. We need to move from thinking about business intelligence in three independent steps: 1) pre-analytics (e.g. data preparation), 2) analysis and 3) post-analytics (e.g. taking action), to removing the artificial barriers that often lie between these steps to create one seamless workflow.
Existing analytics platforms don’t work well to support the modern data lifecycle. They were not built to be an all-in-one platform, and, even if vendors try, it’s quite difficult to be successful making a revolutionary change such as this. Instead, the architecture to support a modern data lifecycle on a single platform needs to be planned from the start and built from the ground up.
The future of analytics is moving to a single platform. Just as one restaurant can offer you appetizers, dinner, and dessert, one analytic platform can offer you data prep, reports and charts, and real-time automation. Although data visualizations are fun, beautiful, and helpful to spot patterns and trends in data, they can provide infinitely more value when you leverage a modern data lifecycle on an all-in-one platform that offers tangible results.
