Future Trends in Data Analytics

DZone 's Guide to

Future Trends in Data Analytics

The future of business is a four-letter word.

· Big Data Zone ·
Free Resource


The future of business is data

Data is exploding: the IDC says data is growing at 40% annually. By 2025, there will be 175 zettabytes —that’s 175 sextillions bytes-of data floating around the world.

To harness that data and use it to create a competitive advantage can be quite daunting. One way forward-thinking organizations have responded to the challenge is by focusing on streaming data.

Think of streaming data like a never-ending poll that consumers respond to on every device, platform, and time of day. It paints a picture of how your audience interacts with your company at any point in time —whether they’re logging into your website, opening your app, making purchases on your eCommerce site, or even commenting on social media.

But to fully utilize streaming data, it’s critical to shore up your current data strategy. That means mastering and cleansing your data architecture and implementing a cohesive data culture across all departments.

You may also like: Big Data Trends to Look Out for in 2019.

Let’s take a minute to take a look at how the leading companies of 2030 are already taking steps to prepare their organizations to capitalize on this wealth of information.

Streaming Data

Streaming data’s growing emphasis is the result of a new way to think about data. As Wayne Borcher, Chief Operating Officer at tdglobal, remarks, “data that was generated even an hour ago is already old news.” Consider the stock market brokers; they don’t wait for the market to close, run regression analysis over a period of days, then make a buying decision. Instead, they react in real-time to fluctuations in the market and economic news so that they can capitalize on each and every opportunity.

Streaming data empowers stockbrokers to rebalance their portfolios in real-time as the market changes. Real estate firms use streaming data to recommend properties to app users based on their current location. Logistics companies can put sensors in transportation vehicles that detect when a breakdown is imminent; then they place an order for a spare part so that it arrives at the vehicle’s next location and can replace the faulty part without ever stopping the trucker’s journey.

Meanwhile, media publishers are able to measure interactions with their online properties and modify content placement based on users’ demographics, geography, and the time of day they visit the site.

So, how can you leverage streaming data in the next decade?

The first step is to set up cloud-based data warehouses. These can separate compute tasks from storage, reducing data processing time from days to minutes. Then, incorporate artificial intelligence (AI) and machine learning-powered algorithms that can make decisions based on the data your warehouses are processing. These algorithms have the added effect of mitigating human error from your data analytics strategy.

Master Data

To prepare your organization to fully utilize streaming data, it’s important to ensure the data you’re currently using is cleaned, secured, and mastered. Inaccurate or duplicated data leads to poor decisions and confusion between departments.

Imagine if the marketing department doesn’t know that sales just closed a lead and sends that customer an onslaught of redundant messaging! Mastering data creates trusted data, and trusted data is critical when you move to the world of streaming analytics.

Sam Underwood at Futurity said it best: “we see 2019 and 2020 as being the years when organizations that have taken the time to clean and update their underlying data architecture will begin to really leverage AI and machine learning, leaving many of their competitors behind and having to play catch up to match their newfound advantage.”

Data Culture

The other step you need to take to prepare for the streaming data revolution is cultivating a thorough data culture. Already, “citizen data scientists” have become the norm, as new self-service data analytics solutions are becoming amateur-friendly. These new data preparation technologies can show correlations, exceptions, clusters, links, and predictions in data without end-users having to build models or write algorithms.

The result? Everyone in your organization — from your college intern to your CFO — can now act as a data scientist. In fact, more than 40% of data science tasks will be automated in 2020, resulting in increased productivity and broader usage by citizen data scientists (Gartner).

Organizations should also clearly define how their departments collect, process, and interpret data so that everyone is using the same language and visualization protocols. It’s all about creating a single version of the truth across your entire enterprise.

Thinking Ahead

By 2025, nearly 30% of all data created will be real-time, compared to 15% in 2017 (IDC). Turn this into a competitive advantage by shoring up your data culture, mastering your data infrastructure, and, ultimately, leveraging streaming data as a central tenant of your analytics strategy.

Related Articles

cloud, data analytics, devops

Published at DZone with permission of Sri Charan V . See the original article here.

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}