This sponsored post is produced by Sisense.
Visualizing data has led to world-changing triumphs — from tracking epidemics to pinpointing weather impacts. Today, every company has adopted some form of data visualization to better understand their customers, service, and market. Yet, while both companies and users tend to emphasize data visualizations, they fail to recognize that visualizing data is an end result. The means to this end is to properly prepare the data for analysis — or more specifically, to prepare data that’s increasingly complex due to its size, disparity, or structure.
[aditude-amp id="flyingcarpet" targeting='{"env":"staging","page_type":"article","post_id":1834278,"post_type":"sponsored","post_chan":"sponsored","tags":null,"ai":false,"category":"none","all_categories":"big-data,business,cloud,enterprise,entrepreneur,marketing,","session":"A"}']Only after a BI tool is implemented do users understand that data visualizations tools are meaningful only if the data behind them can be gathered, prepared, and analyzed easily and quickly. This has created a new set of challenges for analysts who are now struggling to visualize data that is incomplete, too slow to process, or not ready for analysis — exposing a much deeper problem that is causing many BI projects to fail: the need to easily prepare complex data sets.
Taking the right approach for your data
Analyzing complex data is not only a matter of managing data that has grown in size — it’s about taking the proper approach to handle data sets that have become so large and so varied they demand immensely more efficient methods of preparation and processing. For example, where once data sets were well structured and came from a few sources, now there is a demand to work cohesively and analyze large multi-source data sets.
Get an engine instead of more wheels
To see the extent of this challenge, it’s necessary to realize simple and complex data sets not only differentiated by scale but intrinsically by their mechanics. In other words, the approach entails more than upgrading your data management tool from a spreadsheet to a database — which is akin to moving from a tricycle to a bicycle.
Rather, the approach requires upgrading from a tricycle to a car: you have to make sure the back-end mechanics are powerful enough to quickly connect varied data structures together to make them ready for analysis. In order to successfully make complex data sets ready for actionable analysis, the business analytics tool must be extremely efficient with available systems, skills, and the steps to prepare and model data.
4 rules for a successful BI project with complex data
After years of encountering these frustrations in the data analytics world, where preparing complex data for analysis was a cumbersome and time-consuming process, Sisense was created to answer this need using a unique technology that maximizes a machine’s capacity to store, compress, and access more data faster — allowing its users to tackle any type of data quickly and easily.
I have outlined four lessons about business intelligenc tools and data analytics that guides users on how to ensure they can execute a successful BI project with any type of data — including complex data.
1. Use the least number of systems
Layering various systems on top of each other to move raw data to analysis creates exponential overhead. Each system requires different skills, its own maintenance, and time to continuously integrate — not to mention cost. Mitigating this overhead is possible if we look to put in place tools that offer the broadest capabilities.
The ideal is to use the least number of tools — or even better, a single end-to-end solution. An end-to-end BI single-stack architecture is ideal, because it includes everything you need to prepare, analyze, and visualize complex data, eliminating the need to use a hodgepodge of additional tools.
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2. Focus on efficiency not availability
Being able to add more hardware, more clusters, and more professionals is a blunt approach. Leveraging complex data means becoming as efficient as possible with hardware resources and available skills.
Choosing solutions that cut the number of steps and process more data per unit of hardware resource adds up to saving serious hours of work. That’s the thinking behind the In-Chip engine which lets you run any ad-hoc query and receive answers on the spot, without the need to prepare data in advance for each new question.
3. Recognize scripting is part of the problem
SQL and other scripting languages are great and will continue to be used. Yet, we need to leverage visualization as a way to see and prepare complex data sets. Blending both scripting and visual representations of data dramatically accelerates data preparation. The ability to quickly visualize data sources, relationships, and changes makes it easier to plan data preparation requirements as well as understand previous transformations to the data set.
4. Aim to open the largest data set for analysis
Analysts get stuck when they have missing data or need to rely on someone else to make the necessary changes to data. Most times all business questions aren’t known up front and value is derived through exploration and playing with data. To do this type of self-service BI, the highest level of granularity of data needs to be made available and solutions must make it easy for users to explore, query, and manipulate the largest possible data set with the most flexibility.
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Taking back eighty percent
It’s estimated that 80 percent of data analysts’ time is spent just on data preparation — and that’s no wonder considering the challenges. The fact that data is growing in overall complexity — size, disparity, structure to name a few — means acquiring a technology with a robust data preparation and analysis engine, along with beautiful visualization should be the first and foremost consideration.
Evan Castle is Product Manager and Data Scientist at Sisense.
What’s the next step? Understand how to overcome complex data using our Data Complexity Matrix.
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