In any digital economy, the value of data is priceless. Organizations continuously need data to gain a competitive advantage and make smart decisions. This is why the role of Data analytics has become very significant. In fact today many data-driven startups are making good business just by offering third-party analytics services. Malaysia can be cited as a great example here for being a country that benefitted big time by implementing data analytics in its digital sector.
However, data is increasing exponentially and this can create problems. Especially with the Internet of Things (IoT) a lot of user data is collected from a variety of sources and the volume is tremendously big. Thus traditional data analytics approaches are irrelevant now. In comes ‘Big data’ a term which not only means the volume of data but also the technologies associated with storing, managing, and processing it.
What Big data analytics can achieve?
● Big data analytics can reduce risks in organizations by predicting problems beforehand. The Crime Anticipation System (CAS) of the Dutch police is one very good example where big data analytics is used to predict crimes and schedule patrols accordingly.
● In any organization, big data analytics is used to analyze past failures and recognize the reasons behind such failures. This way problem causing elements can be removed and future failures can be avoided.
● Big data analytics has applications in a variety of fields, Weather monitoring channels use it to forecast weather, sports teams use it to perform better and political parties use it for better election campaigns.
But big data analytics cannot stand without innovative ideas and machine learning (ML) is one such idea that has helped analysts to handle big data efficiently. In fact, most of the above-mentioned application of big data analytics takes the help of ML algorithms.
Importance of Machine learning in big data analytics
The objective of machine learning is to save precious time while playing with sets of data that are not only huge but also dynamic. Machine learning in big data analytics help analysts by reducing the time of performing complex tasks without much hassle. In ML we use algorithms that allow the development of the system when exposed to new data. Models in ML can access data independently and learn automatically. Today ML algorithms can solve complex problems in Big data by applying advanced mathematical formulas themselves.
Some amazing applications of ML are—
1. The speech and image recognition systems used by virtual assistants.
2. The self-driving car of Google.
3. Recommendation systems in e-commerce websites and social media.
4. Financial fraud detection systems used in banks.
Employment scenario in Big data and ML
Due to the increased importance of big data analytics and ML in both the private and public sectors demand professionals skilled in big data and ML is very high. Moreover, in a country like Malaysia which is rapidly digitizing its sectors and hopes to become a hub in analytics, big data analysts with ML skill earns a hefty salary.