Information is the key to success in any industry. Whether you’re trying to run a tech startup, manage a small retail business, or even become a famous musician, it’s important to know as much as possible about your business, your industry, and your audience.
In the digital sphere, information comes in the form of data. Website, smart devices, and millions of other online programs are constantly capturing data points and compiling them into collections you can analyze to understand your audience and better manage your business. But as the internet becomes bigger, reaching into more parts of our lives, the available data also becomes more complex.
How can you analyze the wealth of data your company collects… without suffering from “information overload”? Simple: by using the new trends and technologies designed to collect, analyze, and present data at a 21st-century pace. Here are a few analytics trends you can expect to see in 2020 and beyond.
One major problem facing data scientists today is the sheer volume of data they can gather for any given organization. The average company operates on many different platforms (their website, various social media pages, digital sales platforms, etc.), and each of these sites yields a wide range of useful data.
Of course, once all that data is gathered into one place, it can be overwhelming to organize it all – let alone glean any insights from it. For this reason, many organizations are working towards augmented analytics, which uses machine learning (ML) software to quickly and efficiently analyze their data. Augmented analytics can spot both repetitive patterns and unusual trends in each data set, and these quick, automatic insights can ultimately benefit an organization.
While augmented analytics can be a useful tool that quickly identifies patterns, it’s not a perfect substitute for human insight and creativity. For this reason, some companies are opting to streamline another part of the data scientist’s job: data collection. With data automation software, a computer can speed up the collection and organization process, giving data scientists more time to look for useful patterns and insights in their data sets.
Businesses will likely welcome data automation with open arms, as it helps eliminate the risk of “dirty data” – incomplete or inaccurate data sets caused by human error. It’s believed that dirty data costs the U.S. economy up to $3.1 trillion a year, so it should be no surprise that businesses are itching to solve this problem with new data science trends.
If you’ve ever spoken with a customer service bot, sent a message using a voice-to-text feature, or even asked Alexa what the weather was like, you’re already familiar with natural-language processing (NLP). This form of machine learning is hardly new, but as “smart” devices become more commonplace in our everyday lives, it is become more advanced and more ubiquitous.
In 2020, NLP is expected to move into the realm of data science. According to global research firm Gartner, Inc., about half of all analytical queries will be generated via voice- or NLP-based searches in the coming decade. This development will be beneficial for workers in hands-free environments (for example, warehouses) or for generating quick analytics reports when the guys who “speak computer” aren’t available.
For many years, analytics has been based around spreadsheets – huge swaths of numbers that have been meticulously organized so they can be examined. However, these spreadsheets usually require in-depth explanation before the average employee or board member can understand them. It is much easier, data scientists have discovered, to communicate through easily-digestible methods – like a graph.
Like NLP, graph analytics is not necessarily a new concept (if you’ve used Google Analytics over the past decade, you’ve seen more than your fair share of graphs). However, it is a trend that doesn’t seem to be going away; Gartner predicts that the use of graph analytics will grow 100% per year from 2020-2022!
Analytics from the IoT
The internet of things (IoT) has been steadily growing since the 1980s, as more companies connect their products to the web. But the 21st century has seen an explosion of “smart products” – cell phones, vending machines, refrigerators, and even baby monitors – which has caused the IoT to become bigger than ever before.
As industries like transportation, healthcare, and consumer tech expand their IoT product options, it’s believed that they will also advance their methods for collecting data from those products. Some data input companies are already working on software that collects data from millions of smart devices, creating an IoT grid that industries can study to shape their business operations.
As the world become more and more connected, so too will our data become more and more complicated. But thanks to data scientists and software designers, businesses will always be able to mine their mountains of data for a few nuggets of golden insight.
To learn more about analytics trends and data storing software, visit https://imply.io/druid-university/intro-to-druid-university