5 Data Analytics Trends To Observe In 2016
As each aspect of a customer journey is now being monitored and metered real time, the role of data analytics is now being prioritized to enterprise wide implementation. Big data analytics has been in the forefront of providing insights and predictive power to businesses, digital analytics is now a vital ingredient in the planning process, as it provides tremendous insights on consumer behavior and market dynamics to managers and decision makers. Analysts share their thoughts on some of the most important data analytics trends for 2016.and beyond.
1. Self-service big data discovery becomes center-stage
Historically, self-service data discovery and big data analyses were two separate capabilities of business intelligence; but business will soon see an increased shift in the blending of these two worlds. There will be an expansion of big data analytics with tools that make it possible for business users to perform comprehensive self-service exploration with big data when they need it, without major hand-holding from IT, predicts a new study by business intelligence (BI) and analytics firm Targit.
“Self-service BI allows IT to empower business users to create and discover insights with data, without sacrificing the greater big data analytics structures that help shape a data-driven organization,” says Ulrik Pedersen, CTO, Targit.
2. Data science, predictive, prescriptive analytics merge
As self-service BI proliferates, the New Year will also bring a huge increase in advanced analytics projects across industries. However, unlike before, when big data discussions made it sound as if CIOs and analytics practitioners had to think of advance analytics as something wholly separate from traditional analytics, according to a IIA research, that perspective is changing.
“These things are, in many cases, being combined, and data science, if anything, is becoming a specialized branch of the central analytics group,” IIA co-founder Tom Davenport said in a recent webinar. In his research paper titled: “Big Data in Big Companies (2013),” too, he mentions, analytics on big data have to coexist with analytics on other types of data.
In 2016, the distinction between data science and analytics will continue to blur and become muddled, as big data gets more deeply integrated into more traditional businesses. “While many companies will face challenges as they take on projects involving big data, organizations will have to work diligently to successfully implement these projects.”
3. Analysts need to move beyond analytics
Christopher Arnold, Senior Vice President, Wells Fargo India Solutions believes analysts need to move beyond analytics to succeed. It’s no longer permissible for analysts to provide insights and then be excused from the board room while decisions are made by “the business person.”
Today’s world is too analytic to allow laymen to take the final decision. Analysts need to hone their business and persuasion skills in order to earn their permanent place in the board room. For this to happen, Arnold believes universities should be looking to expand their curriculum to include applied coursework and domain training in order to better prepare tomorrow’s analysts, says Arnold.
4. Smarter thinking about data inclusion
Many early adopters of big data analytics struggled with analyzing too much, which resulted in small amounts of information about many different areas, and an incomplete picture of the business overall. As analytics become more accessible to business users, there will be a shift toward more focused and realistic big data discovery projects, which will in turn provide valuable data for business decisions.
Data preparation is one of the biggest time sinks for analytics professionals and data scientists. But new tools are emerging that apply “the analytics we use typically to analyze data to curate data,” Davenport said in a recent TechTarget article. Rather than approach data management from a centralized, top-down approach, CIOs will work from the bottom up, leveraging machine learning to curate and clean the data.
5. Data governance to gain prominance
The traditional way of handling data governance—centralized, strict, and secure—is still valid for enterprise multidimensional data warehouses. But it is inefficient, riddled with unavoidable bottlenecks, and stymies experimentation. In order to promote innovation and experimentation among teams, a decentralized data governance strategy is necessary for any type of ad-hoc data discovery. Therefore, in 2016, BI platforms must establish various levels of permissions and settings to ensure high data quality is delivered to the right people, at the right time.
2016 will also be the year when we will bypass brilliant analytics for more traditional approaches because we need to focus on risk issues such as privacy and increasing regulations across all industries, concludes Arnold.
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