Making Big Data More Accessible

An edited version of this article was published on the Hortonworks blog. Credit: Hortonworks

Big data, and storing and analyzing it, has the reputation of being a complex and confusing. However, because of the great insight that can be taken from big data, it’s important to allow data science to be more easily accessible.

How can business leaders lower the barrier to entry to make data science more accessible to everyone? I see four ways this is possible.

Invest in natural language capabilities. Math sometimes has the tendency to scare people away, so if your plan is for everyone to learn Python and reduce their questions to a formula, I have news: that is not going to bode well for a successful uptake. Instead, look for platforms that allow users to query data  sets using standard English questions, like “how many more sales did I make this year rather than last year?” or “What are the top five things my customers purchase along with Widget Model A?” This type of natural language processing makes accessing large quantities of data easier and more approachable, but it also has a side benefit of encouraging interest and curiosity in further analyzing data. As your users ask natural questions and receive straightforward answers, they 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.

Make visualization an integral part of your analysis and presentation. You surely have heard the old phrase, “I’m a visual person.” Some people simply absorb and digest information better if it is presented visually. There are currently many platforms and frameworks on the market that can take a raw data query result and turn it into a rich graphical answer to a user’s question. Further, the more sophisticated of these platforms can allow you to interact with certain subsections of a visualization, such as exploding out a pie chart into smaller subpieces or layering statistics onto a map of a certain geographic location. Even heat maps and gradients can make statistics pop into life and really enhance your users’ understandings of exactly what the data is telling them. Visualizations can also help to enhance the encouragement to ask more questions and interrogate the data further by creating a rich, inviting atmosphere to discover new ways of thinking about data.

Smart small and then aim to grow your user base. Chances are, there is a vocal community of employees or contractors within your organization who are already clamoring to access new data science tools. Perhaps you have already harnessed their enthusiasm and enrolled them in a controlled pilot of your data deployment. If you have not, however, consider finding a group of individuals excited about the potential for big data and data science and invite them to preview your plans and test systems and provide very valuable feedback about what they like, do not like, and wish to see or need to have to augment their roles. After all, the best camp fires start with proper kindling; 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 of getting a successful project off the ground before expanding it so quickly that it outgrows its capabilities.

Embrace the power of data on the go. Mobility has changed the world around us, with computers more powerful than the space shuttle available in pocket sized form off the shelf at Target for $80 or less. Many—I dare say most—professionals have smartphones or tablets, some either issued by or paid for by their employer. This means you have an audience that expects to be able to get answers on their mobile devices whenever and wherever they are. Your data science 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 paths. That mobile app ought to also satisfy the points we have 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.