Your password, favourite food, address, and shopping wishlist — everything is data. Your searches in Google, or the information you put up on several sites, or the tweets you make — it is all a capitalizing opportunity for businesses.
Google itself manages about 20 petabytes of data per day. Facebook generates 500 terabytes per day, and Twitter produces around 12 terabytes per day. According to the WE Forum, by 2025, the world will create 463 exabytes of data each day! And this graph will only scale higher at an unprecedented pace as we continue to experiment with and leverage technologies in our everyday lives.
While it’s great that businesses have access to a massive data pool, this data is of no use unless it is processed, analysed, and interpreted. In most cases, data is intangible. For instance, your password is simply a combination of alphanumeric data, and without the physical or digital device it unlocks, it is pretty useless. Thus, data is of value when organised, structured, and used in conjunction with other elements. This is where data science enters.
Data science is a study of a combination of subjects like mathematics, statistics, data analytics, and programming. Thanks to data science, we can now gather and organise big raw data and manipulate it to extract valuable information from within. Using various manipulation and statistical techniques, data scientists and data analysts can create a sense out of incoherent and unorganised organisation
The Relevance of Data Science today
Data is information — or rather, information that matters. We live in an era where massive data is generated each day. From small businesses to big industries, everybody needs data to function optimally. For example, an organization wants to set up a school in a city. If they have proper data on the number of children in various areas of the city, it will help them understand exactly where (locality) they should set up a school.
This is just a random example of how data and data science technologies can help. The modern industry is replete with plenty of use cases of data science.
Do you ever wonder why certain types of ads pop up on your social media pages and some different kinds on your friend’s or colleague’s page? This is because of the different kinds of information you and your friend have provided to the site. The data provided by you online is analysed by data science experts who then decide what kind of ads would be more relevant for you. This “personalisation” strategy is what every big player in the industry is doing right now, aiming to improve the overall customer experience.
When it comes to business operations, you get only a limited shot at success — you definitely cannot afford to make erroneous decisions that can break your position in the market. You must make conscious and well-planned decisions, and what better than real-world data to drive that? When decisions are based on data, there are higher chances of getting expected results.
Top Data Science Trends in 2021
Data science is an emerging field, and hence, new trends keep paving their way into the industry. Let’s learn more about the trends that dominate the data science field in 2021.
- Artificial Intelligence and Machine Learning
The powerful algorithms of AI and machine learning can help figure out outcomes from the available data accurately. This is something known as Decision Intelligence, and it can help businesses reach their target goals. Around 23% of companies are using machine learning and AI to reach out to their customers more effectively.
While artificial intelligence has been around for some time, it is still evolving. In 2021, we can expect to see AI solutions like voice-based searches, recommendation engines, AR/VR, etc., being used across multiple industries.
- Cloud services
Big datasets take time to be transmitted and processed. And storing such colossal data can also be a tedious task. Many small businesses cannot afford to store their data in dedicated server warehouses as Amazon and Google — cloud computing solutions are what they need.
Data as a Service(DaaS) is software that uses cloud computing to store data, process them, analyze them and draw insights out of them. It can be used by companies to figure out their target audience and cater to their needs. Also, as per the figures, 90% of big companies in the Asia-Pacific region use cloud services to manage their data. Cloud services can free up storage space, and with shared cloud hosting on the menu, small businesses can enjoy the benefits of cloud services at a fraction of the price!
- Augmented Analytics
The augmented analytics industry is expected to grow about $18.4 billion globally by 2023. It is an approach that makes use of machine learning and processing to automate what would otherwise be left for a data scientist. It makes handling data much easier, and also, the generated patterns out of the data are reliable.
Augmented Analytics essentially uses a combination of machine learning, artificial intelligence, and tracked analytics. Businesses today are dealing with large amounts of user data, and it is possible to miss out on patterns through manual work. Augmented Analytics can uncover hidden patterns and metrics that owners can then use to plan their next move.
- Data Stories
Everything gets clearly understood when presented in the form of a story. Similarly, companies get a better understanding of the stats when they get it in a story format. That’s what data stories are all about.
Data stories are gaining huge popularity these days as they allow companies to understand the data better. Data stories make use of graphs and charts that help the decision-makers to understand the data like a story by seeing the entire journey of the data. It enables data analysts to draw tangible conclusions.
- Edge Computing
If the topology where the data is generated and the place having physical data storage devices for the cloud are far away, then a lot of money and time gets lost in data transmission. Enter edge computing. Edge computing makes sure that the data storage centers are close to the topology where it is being created or consumed.
Edge computing offers multiple local sources from where you can access data. Think of a person in an apartment complex, watching a Youtube video. Now multiply that by a hundred! Imagine the amount of data being transmitted. With Edge Computing, users can free up the bandwidth, and consequently, systems will run smoother and faster.
- Blockchain
The intangibility of digital data means that it can be lost! So, it must be preserved and secured. It can be hacked, destroyed, or misinterpreted if left irresponsibly. Companies are thus concerned about their data security because it could incur huge losses if lost. Most big companies contain large amounts of sensitive information about their clients. A data breach is one of the primary concerns of the digital age.
Blockchain is a technology that provides data security. In this technology, digital information is stored in the form of blocks. These blocks are connected to each other using cryptography. Generally, blockchains help to keeps track of transactions on a digital ledger via a de-centralised system. The digital ledger allows anonymity, security and can be programmed by a data scientist who has expertise in the field.
If these data science technologies fascinate you, you can even learn them through upGrad. They have an online course called Master of Science in Data Science that teaches you all about the data science fundamentals. This course includes natural language processing, business intelligence, etc., to help you clearly understand data analytics and apply them in industries using tools like Hadoop.
To Conclude
As the data science domain expands, new trends will continue to emerge over time. For now, it’s interesting to see how businesses and companies worldwide are using these trends to pioneer innovation.
Data science can be of great help to various sectors, not just businesses if used with proper skills and knowledge. AI tools and ML algorithms enable data science experts to generate highly targeted and accurate business outcomes. These outcomes drive the strategies across the length and breadth of an organisation. And when data science is penetrating all industries, why should you stay behind in this exciting game?
Make the most of this opportunity and acquire relevant data science skills to increase your professional value!
