With the help of artificial intelligence (AI) and machine learning, businesses are generating more data than ever before. This influx of information is the reason data scientist was crowned the most promising job of 2019, and has held the no. 1 spot on Glassdoor’s Best Jobs in America list for four years running. More data results in more people needed to interpret that data.
Data analytics are now considered a necessary tool for organizations when it comes to making big business decisions, and its use will continue growing in the future. “By 2022, 90% of corporate strategies will explicitly mention information as a critical enterprise asset and analytics as an essential competency,” said Douglas Laney, distinguished analyst in Data Analytics & Strategy at Gartner.
Organizations use data analytics to learn more about business strategies, which helps them to make both long-term and short-term decisions, according to Beverly Wright, Chief Analytics Officer at Aspirent; specifically, businesses turn to analytics when trying to generate revenue and reduce expenses, she said. Through analytics, businesses can see places they are running inefficiently, which can reveal areas where expenses can be reduced and more money can be generated.
However, “this is a fast moving field, so one day to the next can vary,” said Wright. To help companies get a better grasp on the current state of data analytics, and how it is advancing, Wright outlined the following six points businesses must pay attention to:
1. Deliberate data-culture initiatives
Businesses are and will become more data-inspired, integrating data into their company culture, said Wright. “Data culture is a type of absorption, a readiness if you will of acceptance and in a sense trust for the results or solutions that come from analytics,” she said. “Analytics groups or data science professionals are creating solutions and then they’re pushing them out to answer business questions.”
However, this culture has been difficult for more traditional professionals to grasp, but companies are beginning to account for that. “I’m starting to see more initiatives that are deliberately intended to soften an organization to absorb an analytic solution–that can be things like creating a conference or a summit, or just immersion to get the culture right,” Wright added.
2. Unstructured data proliferation
Unstructured elements like audio and video will change the way data is collected, Wright said. In the past, data has remained overwhelmingly structured, which Wright explained as more numeric.
“As we’re getting more unstructured data, we’re capturing more video and audio and non-numeric types of data and on top of that, we’re developing stronger types of techniques for analyzing that data and getting it into structured formats,” according to Wright.
By putting unstructured data into a structured format, organizations can turn it into actionable information.
3. Need for real time models
In the past, analytics mainly focused on long term goals, which means looking at information over the course of the year and then making decisions based on that past information. As technology advances, companies will be able to use analytics in real time. For example, “by 2022, 30% of customer interactions will be influenced by real-time location analysis,” Laney said.
“A real time model would be somebody that walks into the Georgia Aquarium and they’re instantly recognized. Like, [this person] is vegan, she doesn’t visit aquariums very often, she’s probably going to want to know what kind of research we’re doing because she’s vegan, and I know that about her,” Wright said. “This gets into the personalization as well, but the point being that an algorithm that’s enacted in real time is one that will change the actions or how the aquarium can adjust at that moment.”
4. Specificity, granularity of insights, including mass personalization
Personalization is everything when it comes to customer experience, Wright said, and analytics plays a huge part in that. “The personalization is becoming really intimate, it’s helping products and services become closer with their consumers,” she added.
“Like the coupons that you get when you shop–those are not just random coupons, they’re personalized specifically for your kind of purchases, for your kind of household, they know how many kids you have. When I say, ‘they’ I mean the algorithms in the systems. There is data about what color hair you have and what kind of products you use,” Wright added.
SEE: Prescriptive analytics: A cheat sheet (TechRepublic)
5. Tool reliance/citizen analyst
More packaged analytics result in more citizen analysts, or more everyday people understanding the basics of analytics, Wright said. Analytics tools will continue to become more digestible to those without deep statistical expertise, mainly because businesses don’t have time to teach their whole staff about analytics, Wright continued.
“We don’t have to have deep, deep statistical knowledge, but the people who have the really deep statistical knowledge are the ones who are creating the tools, and are also the ones that know enough to get buy it,” Wright said. “Then those who know the business and know how to use the tool are the ones that are applying the tools and answering the actual business questions.”
6. Increased movement toward automation and AI
“Through 2023, computational resources used in AI will increase 5x from 2018, making AI the top category of workloads driving infrastructure decisions,” said Laney. AI and automation will continue progressing, becoming more dynamic and complex. The automation is what will enable real time models to be created, Wright said.
“There are ways to automate processes to create newer, fresher models where they get updated automatically without having to involve as much manual processing,” Wright added.