Big data—including storing and analyzing it—has the reputation for being complex. There is so much data being collected that data science is needed to analyze the information and gain insights. Machines can help to decipher what data we should look at and we have the opportunity to train models based on the information collected. But to take advantage of all that can be gained from it, business leaders must prioritize data science to make its outcomes something that the organization can understand and utilize. Here are four suggestions to make these insights more accessible.
If your big data plan is for everyone to learn a programming or scripting language and boil their data inquiries down to code and formulas, you’re in for a rude awakening. Math has a tendency to scare people away, so that’s not going to bode well for a successful uptake. Companies have also realized that it’s just not possible to staff enough data scientists to make that work.
Instead, look for platforms that allow users to query data sets using standard questions, like “How many more sales did I make this year than last year?” or “What are the top five things my customers purchase along with Widget Model A?” or “How many times does a customer look at this item before buying or reserving it?”
This type of natural language processing makes accessing large quantities of data easier and more approachable, and it has the side benefit of encouraging interest and curiosity in further analyzing data. As users ask natural questions and receive straightforward answers, they begin to think of more questions, which leads to more answers and more insight. Save the math and SQL-like queries for your developers and put a friendly front end on your data.
You’ve surely heard the old excuse, “Well, I’m more of a visual person.” Some people simply absorb and digest information better if it’s presented visually. As it happens, there are many platforms and frameworks on the market that can take a raw data-query result and turn it into a rich graph that answers a user’s question.
And the more sophisticated platforms allow you to interact with certain subsections of a visualization, such as having a pie chart explode out in smaller sub-pieces, or layering statistics onto a map of a certain geographic location. Even heat maps and gradients can make statistics pop into life, enhancing your users’ understanding of what the data is telling them.
Visualizations can also encourage more questions and further interrogation of the data by creating a rich, inviting atmosphere to discover new ways of thinking about data. Having an accessible platform and the resources and tools to extract insights from real-time data sources can create a data-driven, data-positive environment—not to mention the fact that companies often discover new and innovative ways to use data in the ordinary course of business that are far beyond the initial scope of the project.
Having a platform and the resources that can extract the insights using tools across a wide set of Big Data real-time sources when making decisions can create a data-driven, data-positive environment
Consider finding a group of individuals who are excited about the potential for big data in your business, and invite them to preview your plans, test systems, and provide feedback about what they like or don’t like and what they need to augment their roles—all of which can be very valuable.
Often, the best members for this group are in sales and marketing departments. After all, the best campfires start with proper kindling, and a data portal project is no exception to this rule. Beginning with a small but dedicated group of users is a tried-and-true method for getting a successful project off the ground.
Mobility has changed the world around us. Computers that are more powerful than the space shuttle are available in pocket-size form for less than $100. Many professionals—probably most at this point—have smartphones or tablets, many either issued by or paid for by their employer. Users expect to be able to get answers on their mobile devices at any time, wherever they are.
Your big data projects and deployments must be available in a mobile-friendly format, either with responsive web design, customized native apps for the popular mobile device platforms, or perhaps some combination of both. Such a mobile app ought also to satisfy the points already established, including supporting natural language and providing rich visualization support with the capability to touch, drag, pinch, zoom, scroll, and more. The bottom line: In this day and age, you simply must make data accessible on mobile devices.
By implementing these strategies, you’ll ensure that data science seems less like a looming specter and more like an understandable and powerful tool that anyone can use. Data accessibility is at your fingertips: it’s time to get everyone on board.
If you’re ready for what big data can offer your business, find out how to get started with data science and discover its potential with cloud technology.