Source:-e27.co
As most of the industries are being transformed by data science and analytics, organisations are taking notice
According to a Harvard Business Review report, 92 per cent of respondents reported an increase in the pace of their investments in big data; 88 per cent said their organisations faced greater pressure to invest in big data; and 55 per cent reported that their investments in big data had exceeded US$50 million—up from US$40 million the previous year.
But data analytics, being the dynamic science that it is, is constantly in a state of flux. As corporations continue to pump money into analytics to fuel their digital transformations, small business owners must pay attention to new best practices and trends to achieve their business goals in the months and years to come.
In an effort to help you stay ahead of the curve, here’s a list of DataVLT’s own top data analytics trends and predictions to watch out for in 2020.
Trends that will shape data analytics in 2020
- Merging of IoT and analytics
When people talk about the Internet of Things (IoT), the focus tends to be on the number and range of devices that are and will be connected to the Internet, and for a good reason. It’s estimated that there will be close to 30.73 billion IoT connected devices by 2020, ranging from kitchen toasters, smart cars, and thermostats to video doorbells, refrigerators, wireless speakers, and so on.
We predict that IoT will eventually evolve from being a hardware challenge into a data one, with each IoT-enabled device generating data that should be analysed and acted upon to be of any value. This brings data analytics into the picture, and it’s a relationship that will only get deeper as IoT devices become more commonplace.
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As a Seagate and IDC report on digitisation notes, “In 2025, each connected person will have at least one data interaction every 18 seconds. Many of these interactions are because of the billions of IoT devices connected across the globe, which are expected to create over 90ZB of data in 2025.”
- Conversational analytics and natural language processing
A Gartner report on data analytics trends published earlier this year predicts conversational analytics and Natural Language Processing (NLP) will transform data analytics shortly, adding that 50 per cent of analytical queries will be generated via search, voice, or NLP by 2020.
Gartner notes that the rise of NLP means that program queries no longer have to be programmed into an analytics tool, which then opens the floodgates for new classes of users, such as office workers and support staff, to take advantage of analytics software.
In other words, NLP and conversational analytics make data analysis more approachable, especially for users who are still building their data literacy.
- Data security and privacy
The Cambridge Analytica data scandal of 2018 brought the issue of personal data collected under the spotlight, revealing the frightening effect of exploiting user data to influence people’s behaviours. In the case of Cambridge Analytica, Facebook data was mined and used without authorisation to build software that predicted and influenced voters during the 2016 US Presidential Election.
The scandal, however, is by no means the only instance of a high-profile data breach. Other organisations such as Yahoo, Sony, Equifax, Uber, and JP Morgan Chase have been hit by cybersecurity attacks that compromised the real names, email addresses, and telephone numbers of millions of users.
It’s no surprise, then, that consumers and consumer protection groups are pushing for legislation to force businesses to disclose what data they collect from people and what they do with that information.
In 2017, IDC predicted that 90 per cent of large enterprises would generate revenue from Data as a Service (DaaS) in 2020, up from 50 per cent that year. DaaS refers to the purchase, sale, or trade of machine-readable data, metrics, and insights in exchange for something of value between two or more organisations.
As cloud computing technology continues to mature, enterprises can expect to gain better access to more extensive and richer caches of digital files over the Internet. The globalisation of DaaS could also facilitate faster and more efficient exchanges of information, particularly best practices and research data, in sectors such as healthcare, manufacturing, telecommunications, retail, and transportation among many others.
- Further specialisation of job roles in Data Science
As data analytics is becoming mission-critical to more and more organisations, the demand for data scientists has also increased with each passing year. In 2020, however, we expect roles in the field of data science to continue to branch off into different specialisations—even more than the ones we’re already seeing today.
In the past, the title data scientist was broad and encompassed pretty much everything involving data, from data capturing and KPI measurement to data insights and forecasting.
Today, however, the job description has diversified into specific roles, including data analyst, business intelligence analyst, data engineer, data architect and more. There is also plenty of opportunities, even for non-STEM members with no programming background to enter the field.
Growing specialisations and job complexity are due to the sector’s rapid growth. Worldwide revenue for big data and business analytics (BDA) solutions are predicted to reach US$189.1 billion by the end of this year, exhibiting a 12% increase over 2018. Experts forecast the sector will maintain this pace throughout 2022 with a five-year compound annual growth rate (CAGR) of 13.2 per cent.
- Augmented analytics
Augmented analytics is poised to be one of the next big things in big data and analytics. Research by MarketsandMarkets valued the augmented analytics market at US$8.4 billion in 2018. This market is estimated to grow to US$22.4 billion by 2025, according to Grand View Research.
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Augmented analytics uses machine learning and artificial intelligence to augment how analytics is developed, consumed, and shared. The most obvious benefit of augmented analytics is its ability to automate many analytics tasks, such as data preparation, analysis, and creation of accurate models.
- Continuous confidence in cloud computing
The cloud will continue to come of age in 2020. According to IDC, spending on cloud IT infrastructure will reach $82.9 billion in 2022, accounting for 56.0 per cent of all software, services and technology spending in the same period.
Meanwhile, in a recent report, Gartner is placing its bets on distributed cloud technology, which distributes public cloud services to data centres across different geographic locations, without losing control over the infrastructure. The transition from centralised public cloud to distributed cloud is seen as a turning point in cloud computing as it solves nagging problems such as latency and data sovereignty.
- Increased regulations
Laws such as the EU General Data Protection Regulation (GDPR) and the highly publicised California Consumer Privacy Act (CCPA) represent a turning point in data privacy regulations. While 2018 was the year of GDPR, we predict that 2020, which is when the CCPA takes effect, will bring renewed attention to the issue of data protection.
Like the GDPR, the CCPA’s scope does not depend on where a company’s location, but on the consumer’s data that it stores. That means that a website hosted in Sydney must comply with CCPA if it collects and/or sells the personal data of any California resident. And while the law is state-based, CCPA is predicted to influence the rest of the US as well as the world due to the clamour for better data privacy laws. To date, more than a hundred countries have some form of data regulations, and we believe this number will continue to go up in the years to come.
- AI continues to make data analytics even more accessible
The continued evolution of AI and machine learning algorithms will pave the way for further automation and optimisation of data analytics processes. This, in turn, will provide organisations with more accurate business insights to act upon.
Apart from augmented analytics, AI is bringing predictive analytics into the spotlight, allowing organisations to explore their historical data at a deeper level using data mining, statistics, modelling, and machine learning. While this type of advanced analytics is far from new, the surge in organisations using platforms like Azure, which houses powerful and scalable enterprise predictive analytics resources, will drive investment in machine learning by up to US$12.4 billion by 2022.
Data analytics is in constant motion
Data analytics is becoming the norm but is also evolving at a rapid speed. Enterprises that are ahead of the curve are leveraging analytics to make informed decisions on subjective strategies involving recruitment, marketing, and branding.
On the other hand, objective decisions involving inventory, supply chains, and fraud and risk detection among others, are based on increasingly complex data, which help determine statistical probabilities and predictions with better accuracy than ever.