5 Data Analytics Trends That Will Dominate 2020

Technology has brought some real game-changers to the world of business, one of which being data analytics – a tool currently seen as indispensable in every business, be it a startup or an established company. It reflects business performance, provides actionable market and customer insights and helps business leaders make informed decisions.
But simply acknowledging the power of data analytics isn’t enough. Any business leader wishing to step up their digital transformation efforts can’t overlook the way data analytics is evolving.
Let’s take a look at some of the ways that data analytics is radically altering the business landscape in the year 2020.
15 data analytics trends 2020

1. Data analysis automation

It is projected that by 2020, over 40 per cent of data-based tasks will be automated, which brings about higher productivity and more extensive use of data and analytics by citizen data scientists. 
What implications does this have for companies?
First, business, especially those who develop data and analytics software platform would want to focus on simplifying data science products to allow better ease of use for citizen data scientists.
Some might wonder “Why citizen data scientists?” which leads us to the long-standing barriers between business users and data scientists. Data science teams consist of experts in analyzing and interpreting complex digital data to assist the decision-making process of business. Yet when working with business, several problems arise. To begin with, data analysis depend heavily on data scientists, yet they are hard to find nor train, so not many businesses can expect to perform data analysis whenever required. Secondly, data scientists often work with data without having proper business context, leading to the resulting analysis failing to fulfil business purposes. Not to mention a large amount of time data science projects require to deliver valuable impact to the business.
This is where citizen data scientists come in. According to Gartner, citizen data scientists are those without the expertise or technical skills that characterize data scientists, and those with the ability to “bridge the gap between mainstream self-service analytics by business users and the advanced analytics techniques of data scientists.” By focusing on simplification, data and analytics software platform vendors can help citizen data scientists carry out sophisticated analysis and create models that leverage predictive or prescriptive analytics.
Second, automation will speed up the pace at which companies work towards advanced analytics. With the growing number of citizen data scientists, companies can enjoy access to more data sources as well as a broader range of analytics capabilities of a large audience of skilled information analysts within the organization.

2. Data as a service

The Techopedia defines Data as a service as “a cloud strategy used to facilitate the accessibility of business-critical data in a well-timed, protected and affordable manner.”
The DaaS market is predicted to reach $12 billion in 2023 at a Compound Annual Growth Rate (CAGR) of 39%.
The drivers behind this impressive growth are increased adoption of big data analytics across different industry verticals and cloud-based services in enterprises as well as rising demand for real-time data analytics.
In the year 2020, up to 90% of large organizations are expected to be generating some sort of revenue from DaaS.
The DaaS approach brings enterprises lots of benefits such as the ability to move data easily from one platform to another, ease of administration, compatibility among diverse platforms and global accessibility. However, being a cloud computing technology, DaaS also come with major challenges concerning privacy, security and data governance, which suggests that DaaS providers will have to actively seek to address these concerns if they are to reach or maintain their position as a key player in this market.
Data as a service

3. Augmented analytics

Augmented analytics is one of the three major waves of analytics. It combines the use of machine learning and AI techniques to transform how analytics content is developed, consumed and shared.
The global augmented analytics market is expected to reap huge-scale revenues from a variety of industries in the next few years. According to Markets and Markets, the augmented analytics market is expected to grow from USD 4.8 billion in 2018 to USD 18.4 billion by 2023, at a CAGR of 30.6% during the forecast period.
The major factors driving the augmented analytics market include the growing demand for gathering crucial business insights from customer data and increasing volume of business data. Also, the field of data analytics has a wide application portfolio ranging from the transportation sector to the defence and aerospace industry. By 2020, augmented analytics will be a dominant driver of new purchases of analytics and business intelligence, as well as data science and machine learning platforms, and embedded analytics.

4. Internet of Things merged with data analytics

By 2020, there will be around 30.73 million of IoT (Internet of Things) connected devices. The growth of the Internet of Things (IoT) is having a big impact on lots of areas within many IT companies, one of which being data analytics. With more IoT sensors being connected to objects, an ever-expanding amount of data is generated. But these data can bring business value only when data analytics is involved to explore profound implications and point to possible solutions. It is expected that business will work towards more analytics solutions for IoT devices to provide not only relevant data but also transparency.
Combining IoT and data analytics will positively impact the business. For starters, the data generated by IoT comes in a huge volume and varying sets in terms of structure. Data analytics software will allow businesses to analyze data efficiently, regardless of their volume and structure. Additionally, data analytics and IoT are useful tools for business executives to gain actionable customer insights, which contributes to better satisfying customer demand and later on, drive revenues and profits. In the long run, the use of data analytics in IoT investments can also help businesses create a competitive edge over their competitors.
The growing popularity of IoT in many organizations will entail the need for data analytics, making data scientists increasingly in demand within the next few years. However, Gartner projects that through 2020, “a lack of data science specialists will inhibit 75% of organizations from achieving the full potential of IoT” and as staff with the necessary skills will be scarce or expensive, organizations will “seek ways to use them more effectively or will find alternatives to human involvement, perhaps using machine learning rather than human data analysis.”

5. In-memory computing

Over the next decade, more and more businesses will start using comprehensive in-memory computing platforms. According to a Gartner report, the In-Memory Computing (IMC) market will reach the $15 billion mark by 2021, a significant increase from $6.8 billion.
In in-memory computing (IMC), storage of data occurs in RAM across multiple computers instead of in a centralized database, resulting in fast performance and scaling of data in real-time. However, many IMC solutions have a limitation, which is the high cost of storing all data in memory. A viable solution to this problem is memory-centric architecture which supports the use of other memory and storage types including spinning disks and storage technologies such as solid-state drives (SSDs), Flash memory and 3D Xpoint. Memory-centric technologies are expected to become the key to cost-effective IMC adoption.
For large datasets, non-volatile memory (NVM) will be the favoured method for storage combined with the use of hybrid storage models. When the power goes off, NVM retains its data instead of erasing them like what volatile memory such as DRAM does, so there’s no need for software-based fault-tolerance for IMC platforms. With all its speed and scale benefits, NVM is expected to dominate among data storage models.
IMC platform vendors will support a wider range of machine learning and artificial intelligence use cases. These developments will become new capabilities incorporated into the in-memory computing platforms of many vendors. Integration between a machine learning library and an in-memory computing platform will allow machine learning model development to provide real-time support to mission-critical applications. Further integrating deep learning with IMC platforms will reduce the cost of using operational data to train artificial intelligence models, enabling companies to easily optimize for cost and performance.
The possibilities of data analytics are endless. It’s safe to say that data analytics will continue to drastically change and create significant impacts on business. Keeping on top of the latest developments in data analytics is essential to guiding your digital business transformation along the right path and achieving success in years to come.
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IoT Challenges: What are The Greatest and How to Deal With?

In this world of hyper-connection, Internet of Things (IoT) has, by any means, become a friend of both individuals and organisations. Thanks to the diverse advancements in wireless technology, IoT has been gradually redefining not only the way we work but also the way we live. Smart cities, smart homes, wearables, smart alarm systems, etc were then born to ease and to rouse our boring, complicated human-lives. But what exactly makes IoT such a wonder these days? Maybe 7 categories below could give you a clear explanation for IoT major challenges.
iot challenges what are the greatest and how to deal with by savvycom

What are 7 unique issues with IoT Solutions?

7 great challenges for IoT Solutions by savvycom

Internet of Things – characteristics and challenges (Infographic) | Source: Savvycom.

1. The Capability to Keep Up With the Velocity of Data

One of IoT’s most outstanding trait is its ability to keep data moving in real time. Under the control of IoT, data is continuously transferred from one device to another and end up at your mobile phone, your laptop or your PC – whichever plays the role of a control system. Data collected will, therefore, be analysed so that users could extract useful and valuable information needed for their specific purposes.
Hence, IoT can be seen as a streaming solution. Thanks to IoT, users will constantly be given a heads up on devices’ status, the working state and the process. Any change in data patterns will be identified in no time, therefore, notify users with critical issues. Once the problems are pointed out, users can act on the necessities before anything worse happens.

internet of things velocity by savvycom

Source: Mindinventory.

However, a big challenge facing IoT is that it may be difficult for the traditional approaches to data to keep up with the speed of data created by IoT devices because they need to be processed instantly.
Let’s take REALM, a product designed by Savvycom engineer team. It is a smart GPS tracker using IoT technology that allows users to form a group tracking among drivers heading to the same destination and maintain communication with each other in real time.
realm by savvycom team

Source: Savvycom.

Thanks to the ability to connect and notify each person’s location in real time, REALM is able to create a virtual fence which will immediately alert the group leader if there is any member goes out of track. Its velocity of data has indeed helped users to optimise the time spending for finding the right direction and guarantee that every member shows up at the same time without anyone being lost behind.

2. A Massive Amount of Data

IoT is distinct for pulling everything into one single place. No matter it’s image, video, audio, vibration, temperature, humidity, pressure or anything, as long as it is counted as data, IoT can connect them all.

IoT data volume by savvycom

Source: vietsunshine.

According to market research of International Data Corporation (IDC), it is estimated that the data created by IoT devices will exceed 40,000 exabytes by 2020. This makes IoT a giant data storage, which can not only manage but also analyse and process such information as per user requirements. Thus, users can pinch information from this massive library and make use of it.
A good example of this functionality is smartwatches. Let’s take Apple Watch as a more specific case. By collecting one’s personal data, an Apple Watch can measure the heart rate, count the walking steps, update the daily weather, elaborate and convert them into notifications, such as alerting when one’s sitting too long or reminding one to drink an adequate amount of water. 

3. IoT Sensors Challenges

In the IoT network, one device is appointed as the “server”, the heart and brain that control all connected devices around. It takes charge of gathering and managing every data and information other devices emit to IoT, analysing and representing it to users.
In case there is any following action required, the server will send orders back to those devices wirelessly. Therefore, the locations become less of importance: IoT could easily control all of them with just one single device at a single place.

iot sensors

Source: Medium.

Various options can be proposed as an IoT server, such as smartphones, laptops, PCs, or smartwatches. However, smartphones are the most popular choice because of their portability, prevalence and universality function.
By downloading the IoT controller app to smartphones, users will be able to keep track of the working state of devices and identify critical issues beforehand, which will help users avoid lots of negative scenarios in the future.

4. Immaturity of IoT standards

In order to collect data and transmit the analysed information and users’ orders back to the devices, wireless communication is an indispensable companion to IoT. Therefore, the more developed wireless technology is, the more efficient IoT systems become.
Nowadays, wireless technology has advanced to quite an extent. There are many ways that data sensors can communicate to a central point: Bluetooth, Bluetooth low energy, Wi-Fi, Internet USB, 2G, 3G, 4G or even 5G.
Furthermore, various wireless communication can be used at the same time to maximise the efficiency of data delivery, as long as they are well managed. It may be Blue Tooth Low Energy (BTLE) that is used at the edge to connect multiple devices to an interim ‘aggregator’, and only after then that Wi-Fi is used to send data onto the central systems.
With the help of such advanced wireless communications, the standards of IoT go higher and higher as days pass, promising an era of automation and absolute convenience ahead.

5. IoT Cloud Challenges

As mentioned in the second category, IoT can not only collect and analyse data but also store and manage those data for further use as well as other revisions in the future. The reason why IoT is capable of saving and dealing with such a giant amount of data is that its core system is located in Cloud.

iot cloud challenges by savvycom

Source: Workplace Insight.

With Cloud, any change of resource needed for application will no longer be a barrier. Cloud ensures the connection among devices and that every information is stored automatically and constantly updated. The processing requirements to model, predict, simulate and visualise the stored history can all be managed from a single point as well.
Cloud can also eliminate integration’s need and prevent data’s overloading, which helps devices run more smoothly.

6, Integration Challenges Facing IoT

IoT solutions often need much more than just sensor-based data to become useful. Reports from engineers held in content management systems, ERP (scheduling), and Asset management systems within the enterprise may also need to be brought in.
However, the more components there are in the system, the more complex and confused it would be for users. Therefore, integration across all system factors is needed so that the whole process won’t be messed up:
“Integration helps capture data from smart devices and move it into business applications to automate processes, support real-time monitoring and apply analytics for insights.” – Perficient guide, The Why, What and How of IoT: 50+ examples across 11 industries

7. Security & Privacy

Because IoT can connect with a variety of devices, each of which is distributed different geolocations and run by different principles and standards, maintaining a consistent level of security across the whole system becomes critically hard. Therefore, IoT’s firewall is not really strong and later results in information breaches as well as outsiders’ illegal infiltration.

iot security privacy challenges by savvycom

Source: Arcserve

Moreover, IoT is often the target of cyber-attacks for many reasons apart from the crack in security, such as IoT’s ability to multiply the number of botnets. By hacking one single IoT, attackers can manipulate all the data stored within the IoT platform. And IoT, as it functions, will automatically send the infected code to other devices, putting users’ private information in danger.

Understanding customers’ concern on major IoT challenges, Savvycom dedicates to put forward the quality of product and enhance user information protection. We focus on strengthening 2-factor identification, device synergy and product testing in order to guard against any error in the security wall.
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Helo – Turn the whole world into one stage

Ability to serve up to 300 000 cross-continent users at the same time.
Ultra-low latency live streaming video using RTMP protocol.
WebSocket in the real-time chat is utilised to ensure a constant conversation between streamers and followers.

Helo – the solution for talents and talent seekers

Livestreaming mobile app gives you token Savvycom
Helo provides the right tools for talents from every walk of life to be discovered.
That is the quest that Helo has brought to us: to collaborate and create the first-ever live media streaming application that will enable people to showcase their gifted abilities by getting noticed by their respective industries.
Combining our in-depth knowledge regarding mobile strategy and design with their creative solution, Savvycom is proud to introduce the Helo Livestreaming Mobile Application.
No longer the days of exclusively signing contracts with mainstream celebrities to endorse their product. Now, each and every single individual can become a key opinion leader – as long as they have the skills to be loved by and the platform to do so.
Sometimes, their recommendations are even more persuasive than the traditional celebrities’ ones because of their down-to-earth personalities and close connections with the ordinary crowd.
This rising trend is incredibly useful for not only the businesses to develop a highly cost-effective marketing campaign and target the right audiences, but also for creators to prove their talents while earning additional income.

Helo customised application consisted of:

With a live streaming application that will serve 300,000 users simultaneously, all data needed to be shown in real-time across a wide geographical area.
To solve the problem, Savvycom’s engineering team apply the ultra-low latency live streaming video using RTMP. This protocol ensures the stream’s size is negotiated dynamically between the client and server, thereby effectively maintains persistent and smooth connections. The instant interaction between streamers and their followers is also an absolute must for a successful streaming application.
So, to make sure that Helo application can maximize its users’ engagement, Savvycom has utilized WebSocket in real-time chat to ensure a constant conversation between streamers and followers.
Since the targeted audience has been identified as Asian citizens ranging from 18 to 40 years old, the UX/UI of Halo also stands as a challenge.

Entertainment technology solution for music industry by Savvycom
Helo is customized to serve hundreds of thousand users simutenously
Savvycom livestreaming app
Helo’s interface is minimalistic yet optimal
With such a broad range of users, the interface needs to be simple, easy to use yet inclusive, dynamic and evoke creativity.
Therefore, Savvycom developers have applied a 2-colour theme with a simplistic layout using basic geometric shapes. This helps creates a modern vibe for the application while lower the first-time users’ learning effort.
As an addition plus side, it also ensures that the application does not take too much memory space or battery to operate like a dream.
Understand the constant update and changing nature of mobile devices, Savvycom makes sure to prioritise the application’s compatibility with multiple platforms and operating systems.
MySQL, MongoDB, Angular, NodeJS, PHP 7, Laravel 5 were all brought into the development of this application to ensure the highest level of adaptation, easy update, and limited error.

Focus on getting emerging talents recognized in the most effective way possible.

Simple Set-up
Download easily through Google Play and the Apple App Store. Singing up quickly through Facebook, Twitter or Email. That’s it. That is all it takes for the users to start broadcasting live.
Gift and rewards
HELO got a special incentive system for its creators. Once the user is live and got viewers, he or she can encourage the viewers to send them stars. With every 250,000 stars received, 120$ will be sent directly to the user Paypal account – a worldwide recognized online payment system.
Feature Streamers
Once the user starts gaining traction and engagement on their live stream, Helo’s algorithm will feature that creator’s stream in a special section so to amp up their recognition.
Discover the community. Share the community.
The user can either ‘add friend’ or ‘follow’ another user. While streaming, a user is just a click away from sharing their live stream across social media platforms (Facebook or Twitter) so that friends and families can follow their journey as well.
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User Surveillance

Behind the beautiful words, in fact, a system of tracking, watching and studying customers or users. Although the actions are evil to users, its benefit is overwhelmed to any business. Companies or organizations no longer wonder the morality of the actions but seek for the best way of abusing the action to survive in fierce, competitive, and evil markets.

“We are passionate about creating engaging and customized experiences for people.” [facebook privacy policy]

Surveillance seems to be the wrong word when salesman approaching customers as no one would like to be monitored, followed, or watched. It will be a disaster for a salesman to start a conversation with a customer as “We put you under our surveillance systems to understand your need” etc. No user or customer would give him/her a chance to finish the sentence.

Unfortunately, to watch users, to predict what users need before they know, and to track users turn out to be the next generation of business.


Human behavior is highly predictable. We talk and act in the same manners, read the same kind of books or news, make same styles of friends, shop in the same malls, and commute and travel in the same pattern [1].

In a big picture, a shop serves more or less same kind of customers, a newspaper attracts more or less the same styles of audience, etc. In addition, modern technologies provide a rich comprehensive data that reveals information before experienced experts can tell. For example, information from smart watches can tell a person being going to get sick before the best doctors can diagnose [2]. Human behaviors, in fact, can be easily predicted.


Many companies have advanced in applying AI systems to predict users behavior to their business. Google uses many methods to predict users’ next move to serve the best advertisers. They can learn from users’ email a booking flight to suggest other services (e.g., renting cars, or accommodations). They would also know if users are businessmen with large sum budgets or students with limited spending to recommend appropriate solutions. Amazon, another example, pushes the systems into its edges by introducing pre-ship system where they ship the good close to customers’ doors before they place orders [3].

They learn from users’ data to predict what would be the next buying and process the “virtual” order before customers click the purchasing button. Recently, Google has the permission to use medical data of 1.6 million patients in the UK for their deep mind analysis [4]. We will never know what would they learn or predict from the data. They may be able to predict when and what would be the main cause of the death of a patient.

Although it is evil to track and watch customers or to apply AI methods to predict users’ behaviors, the benefit of the knowledge is overwhelmed to be turned down. Companies equipped with users’ knowledge learned from big collected data have too many advantages in competing. They would know customers’ need to provide the appropriate services that users or customers will not find elsewhere. They can organize marketing campaigns targeting customers individually or set up traps to lure customers from other competitors. In another, they have one steps in advance in comparing to competitors.

Let’s take a look at one scenery. A mobile providers will be able to collect user’s information including handholds, purchasing behaviours (e.g., what range of phones they bought), budgets, storages, etc. Having the data from history and a model learning from big collected data, they will be easily to predict when a customer would need a new handset or new deals.

For example, they can learn from the database the number of time a customer empties the handset’s storages seeking for a new handset. They also would know the number of time users overuses the current data plan before moving to a new deal.

Having the knowledge will help the carriers to customize a deal to that a particular customer just before he/she knows that he/she seeks for a handset or new deals. In the other words, the provider knows in advance when and what services a customer will need in short coming future. Obviously, it gives the providers a chance to approach and provide the best-personalized services to customers.

They can bring the services to customers before customers would know they may need that. This business model beats completely conventional models out where providers can only propose services or offer to users when users need. In future users or customers may never be in the status of seeking for services or offer.

User surveillance is a next business generation. It is no longer a question of morality as it is the only chance for business to survive in competitive markets. The only question companies or organizations ask now: how to adopt that evil for their business.

Author: Quang Si Le


[1] Human behavior, highly predictable.
[2]: Smartwatches know when you are sick before you even know
[3]: Ship before customers order
[4]: Predict what customer know.
[Facebook policy]
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