Data analytics is not new to business. What’s new is the amazing range of technologies and techniques that are revolutionizing the way data is gathered, analyzed, and stored today. Big data, machine learning and deep learning are helping enterprises gain valuable insights into customer behavior, enhance system performances, and explore new revenue opportunities.
According to an International Data Corporation (IDC) report, the big data and business analytics market is expected to reach $203 billion by 2020, almost double the $112 billion in 2015. Enterprises need to keep up with the industry trends to leverage data analytics for actionable intelligence. Here’s looking at 7 trends that are transforming the future of data analytics:
1. High on automation
How can we effectively analyze the plethora of data that’s available today? Thanks to artificial intelligence and machine learning, much of the process is being automated. Enterprises are bullish on techniques like pattern identification and anomaly detection that help sort through the sea of data.
With minimal human intervention, advanced analytics tools can not only clean the data and find statistically-valid correlations, but also suggest optimal storage and visualization options. What’s more, self-service business intelligence (BI) tools, such as Tableau, Qlik Sense, Power BI, and Domo, are changing the way business information is analyzed in companies. What’s not to love about automatic updates from the latest data!
2. Real-time access
Running batch jobs overnight to analyze data is becoming a thing of the past. Enterprises need real-time data, not day-old data. In an industry survey, nearly 70 per cent of the respondents (data architects, IT managers and BI analysts) favored Spark (in-memory, real-time stream processing) over MapReduce (batch processing done overnight or during off-peak hours). The writing on the wall is clear: Hadoop, HBase, HDFS and MapReduce will continue to fade in favor of faster technologies.
Similarly, monthly BI reports will find no place in a competitive business environment, where decisions have to be made here and now. Not surprisingly then, the popularity of embedded analytics will continue to grow in BI, courtesy its promise of higher quality data and quicker business insights that can be shared in real-time.
Foreseeing the future
Research suggests that one’s personality traits can be deduced using machine learning algorithms by analyzing not more than half an hour of web browsing history. In other words, digital marketers will have substantial data about their customers to chalk out effective sales and marketing strategies.
From predictive analysis to prescriptive analytics, enterprises have much to gain from the massive amount of data generated from the myriad Internet-connected devices of today. By learning to detect patterns and anomalies for future scenarios based on the data, businesses can be better equipped to deal with eventualities.
3. Seeking order in chaos
Experts estimate that about 90 per cent of enterprise data is either semi-structured or unstructured. Traditionally, companies have relied on structured data analytics to describe what’s happening in the organization. However, the wealth of data included in PowerPoint presentations, company records, social media posts, official documents, etc, in the data lake (storing huge volumes of raw data in its native format) have remained largely untouched.
With machine learning and data visualization tools, it’s now possible – and imperative – to give unstructured and semi-structured data their rightful place in analytics. As organizations demand quicker answers, they will turn to the data lake for those solutions.
4. Call in the experts
What makes a great data scientist? Well, there is no definitive answer, as different problems require different skill sets. However, there’s a definite rise in the demand for data scientists – and it will continue to in the coming years. For traditional programmers to stay relevant, they will need to add data science skills to their resume.
Even as AI becomes the new standard, data scientists will be here to stay. Albeit in more creative and non-conventional roles. Enterprises today are keener than ever before to invest in analytical talent. And in the absence of in-house experts, they are partnering with specialty shops to process their data and give them intelligible insights.
5. Moving to the cloud
Providers of cloud-based data warehouse services are already developing solutions to enable companies to move data to the cloud seamlessly. Eventually, all data will move to the cloud, as there’s only so much storage that any data center can offer. And once that happens, it’s prudent for data analysis to migrate to the cloud, too.
By 2020, experts say, public cloud data and streaming analytics services will be the order of the day. Cognitive computing and machine learning in the cloud solutions will become more affordable for enterprises.
6. Matter of security
In their rush to connect “things” to the Internet, manufacturers have exposed vulnerabilities that are being exploited by hackers today. The Internet of Things (IoT) has opened the proverbial Pandora’s Box for Internet security. It’s not just about data security, but also about protecting product design and other IP from theft and sabotage.
Technology of the future will need to be designed from the ground up through a security lens. Because until the security issue is resolved, the data analytics potential of IoT will remain a risky proposition. Especially for enterprises operating in countries with strict data privacy and security regulations.
7. The final word
Business analytics has reached a new level of maturity today. Enterprises are using data and analytics not only for critical resolutions, but also to make informed everyday decisions. In time, experts believe, the IoT will become an all-pervasive tool, deriving data from every available system, device, sensor, server log and machine. Expectantly, security concerns will be duly addressed by then.
As the scope of data analytics gets bigger, the internal workings of the system will get simpler. The new-age data scientist will get ample opportunities to sharpen his creative streaks. As Arthur C. Clarke famously said, “Any sufficiently advanced technology is indistinguishable from magic.” Is the data analytics industry ready for magic?