Top 3 Data Analytics Trends to Watch in 2020



Data analytics has grown tremendously over the past few years. It has impacted many areas of people lives and businesses. Industry experts believe big data will transform businesses like how the internet did.


Here are the top data analytics trends to look out for in 2020:


1. Growth and increased complexity in cloud computing services


Cloud computing is a safe and cost-effective way of storing and sharing data. With the increasing need for data storage, the big benefit of the cloud is that infrastructure can be extended when needed.


It reduces cost and complexity of owning and operating computers and networks. It also allows the flexibility of expanding into another country without owning or building a new data center which could cost hundreds of thousands to set up potentially.


In a recent cloud computing survey done by the venture firm North Bridge, 50% of the organizations surveyed used a cloud-first philosophy, or used the cloud exclusively for their needs.


Cloud has evolved to allow more complex analytics services. Leading cloud service providers, such as Azure and AWS, have sets of tools to uncover additional insights while also automating data retrieval. AI and machine learning features are also available to help develop and automate more advanced processes.


For example, AWS has pre-trained artificial intelligence ("AI") models for computer vision, language, recommendations, and forecasting. Companies can buy these models that have been optimized for scale, accuracy, and performance in the Amazon marketplace.


These providers support a number of programming languages, such as Java, Python, C#, Ruby, as well as open APIs using open standards to ensure the most flexible and accommodating service.


We are in the opinion that companies will use cloud computing providers for services where possible, or otherwise build analytics model from scratch where flexibility is required.


 

2. Augmented analytics and continuous data from consumer devices


Augmented analytics is the use of statistical and linguistic technologies to improve data management performance from data analysis to data sharing and business intelligence ("BI").


It provides availability and access to refined analytical procedures, algorithms, and methods for the average professional user, without training or knowledge of data science or analysis.


It automates the processes of data collection and data preparation to save data scientists 80% of the time.


Data scientists and technical analysts will no longer have to run routine and basic reports. This frees up data scientist's time to solve more complex queries and data science projects with advanced AI and machine learning.


Augmented Analytics, Data Analytics, Business Intelligence
Augmented Analytics Process (Source: modefinance)

Internet of Things ("IoT") devices such as smart TV, smart meters, smart machines, smart appliances and wearables is increasingly providing streamed data for continuous intelligence.


We earlier covered an article on Internet of Things and some of its impact (read more here) and how manufacturing analytics maximises value (read more here).

Gartner predicts that by 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.


 

3. Increased emphasis on data and privacy


Data privacy concerns the ability of an organization or individual to control what data including customer data, can be shared with third parties.


There has been increasing awareness on data privacy, partly driven by the rise of regulations. Enforcement of rules is also becoming stricter. For example, General Data Protection Regulation (GDPR) implemented in European Union countries carries a heavy fine when breached. There has been a trend of higher and more frequent GDPR fines in the last few months of 2019. Therefore, in 2020, we can fully expect more protrusive and aggressive behavior by supervisory authorities.


"Customers have stated that they would be more willing to trust companies that gave control over the collected information, are transparent in the way they use that information, have a strong privacy policy or ask for explicit consent." - Salesforce Research

Consumer awareness on data privacy is also growing through campaigns held by regulatory bodies and NGOs. They are reminding consumers about the value of personal data and the need to protect it.


Therefore, companies will need to integrate privacy into all core operations and as part of the product design, cultivating trust with consumers. Transparency will become very important especially for B2C companies.


Companies should communicate clearly and concisely to the public what privacy means and the steps it takes to achieve and maintain privacy. This also means informing consumers how they may expect their data to be used.


 

Data is useful when we can extract meaningful insights from it. Ideally, data analytics helps eliminate much of the guesswork involved, instead systemically tracking data patterns to best construct business strategy and operations to minimize uncertainty. It helps in better decision making.



Do you see the same trends? Share by leaving us a comment. If you require more information or assistance on data analytics, contact us. We want to be an extension of our clients. We assist in systemising processes and decision making. Subscribe to our newsletter for regular feeds.


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References


Dataversity, Cloud Computing and Cloud Architecture Trends in 2020, https://www.dataversity.net/cloud-computing-and-cloud-architecture-trends-in-2020/, published 19 November 2019

Gartner, Top 10 Data Analytics Trend, https://www.gartner.com/smarterwithgartner/gartner-top-10-data-analytics-trends/, published 5 November 2019

Netwrix, Data Privacy Trends Issues and Concerns for 2020, https://blog.netwrix.com/2019/11/05/data-privacy-trends-issues-and-concerns-for-2020/, published 5 November 2019

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