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Data analytics has taken over the world by storm and has been integrated into almost every sector. Thousands of terabytes of data are being created every day by businesses, google searches, digital content and watch time of its viewers in popular social media platforms, etc. The data, if used wisely, can go a long way in building a successful company with minimal costs, maximum customer penetration, and maximum profitability. The usage of data generated by the consumers and customers every day will decide the winners and losers in the market for the years to come.
Challenges in the field of Data Analytics
Data Analytics has been extensively used in the Business-to-Consumer (B2C) applications and business models that surround it. Healthcare, travel, entertainment, gaming, finance, consulting; you name it, data analytics has been used by the industry giants of the field. Data Analytics is considered the ultimate tool that can optimize businesses, and increase revenue and profits. However, data analytics is relatively new and faces some serious challenges that will be addressed in this article.
1. Handling and Representation of Increasing Data
The data that is produced by any business has been increasing significantly since the past decade. This can be attributed to the availability of smartphones, and the cheap internet services around the world which have led to a skyrocketing in the number of users relying on the internet and online services for their day-to-day activities. Handling the data itself is a serious challenge, but when the data available becomes more than what can be processed, can lead to many other problems including the selection of relevant data, cleaning the data and removal of noise, etc.
Another challenge faced is the time delay. Business decisions should be taken effectively as well as in a time-bound manner. Analyzing huge amounts of data will require more time, and can cause a delay in the results or the service provided. A workaround to this problem is provided by Hadoop. MapReduce in Apache Hadoop can split the data into smaller fragments for better and faster analysis. This renders the data measurable and significantly reduces the time delay.
Any data is useful only when it can be represented and analysed easily to detect trends and anomalies. It is very tedious and extremely time-consuming to work with unstructured data and to represent it in a structured and visually pleasing manner. Most data analysts make use of charts, graphs and other visual tools to represent the data efficiently.
2. Scalability of the model
Most technology startups and businesses have data analytics at their core. It is easy enough to analyze the data that comes in at the start when the traffic is little. But as the number of users increases, the volume of data that has to be processed and analyzed increases. For an application, there are multiple layers between the front-end which is what the customer sees and the database. It takes time to pass the ever-increasing data, and hence decreases the user experience and comfort while using the application.
Organisations, in recent times, have been investing significantly in modifying the application to optimise the user experience. This includes changing the application architecture and also the technology behind it to decrease the performance issues, malfunctions, and enhance the scalability of the application. This is a necessary step for companies and individuals who are aiming for broader markets rather than regional services.
3. Agility and Governance over Data Usage
With the abundance and democratisation of data, it is high time we think about the risks associated with them and how we plan on controlling them. Problems like privacy violations and security threats need to be addressed and policies and clear rules and guidelines should be drafted regarding the usage of data. The real challenge of the authorities is to create a framework that enables the growth of businesses using data analytics without compromising the privacy and security of the users. Here are some ways which can bring clarity to the system or work environment handling data daily:
- Control the data access by multiple parties.
- Clear and concise logging and monitoring of the data.
- Encryption of data to avoid access by unnecessary third parties.
- Clear rules must be laid out to decide who should have what data.
Steps That Are Required To Be Performed For Data Analytics Project
Now that we have addressed some of the major challenges faced by the field of data analytics, we can move on to some practices that can be adopted by individuals and companies to face the challenges mentioned above. The thing to note is that most MNCs and companies have enough and more resources, workforce, and technology, but lack the skills and insight necessary for data management. Here are some practices that will go a long way in creating a business or in your data science career.
1. Have an analytical view of data
With an abundance of relevant and irrelevant data flooding your database, it is important to first list out the necessary questions that need to be answered and work towards it. This reduces ambiguity in the process and leads you to the model that is required to gain important information.
An important practice that can be implemented is to classify all the data types beforehand. This makes it easier to find, analyze and represent data and saves time in the process.
2. Obtain Data logically and strategically
It’s not about how much data you have, but how much you can use efficiently. There are often problems regarding not getting enough volume of data to draw proper conclusions from and also not getting good quality data. In data analytics, it is often very hard to get the right data having decent volume. Experts suggest that more than focusing on perfection, it is important to work with what we have and keep updating.
In case of lack of data, one can always opt to acquire them from open source databases or resort to buying them from providers. It is good practice to make sure that the cost to acquire data will be balanced out by the value the data brings to the project/product. Sampling the data can also help in reducing the time delay and renders the data more useful. Machine learning models can also be deployed to extrapolate the existing datasets and create a smarter dataset for analytics.
3. Refine analytical models continuously
Building analytics models is not a one-time process. For providing consistent or better results, the model should be adjusted to changing market patterns and events, business changes, and incoming data. Analytics models are not constant or static; rather they must be ever-evolving and robust to stay relevant in the future. The data has to be processed accordingly and the existing model must be adjusted to fit the newly acquired data.
4. Compliance is key
Data is considered the new “oil”. However, it can be a liability if not handled properly, especially with so many concerns are being raised in the data privacy and security field. To balance out the innate risks associated with acquiring and using data analytics, proper and rigid compliance policies should be drafted out. It should be in terms with the government regulations of the concerned country and should also keep in mind the internal business rules of the field and the industry standards. Proper governance can also be put in place overseeing the handling of data to maintain transparency and ensure trust.
5. Build a great team
A high-performing team consisting of like-minded people will go a long way in creating an analytics model. It is not enough to have highly skilled data scientists in the team and hope for the best. Creating an analytics model required hypothesis-based thinking and an organisational mindset. This is far more important than the knowledge of financials or revenue growth. The team should be able to complement each other on their ideas to make the model more robust and efficient.
Data analytics is a growing field and still has much room for growth. It is regarded as the ultimate solution for businesses to grow their operations. It is necessary to keep updating with trends, and new systems, infrastructure, technology to stay ahead of the competition.
Data, in its raw form, can be turned into useful insights that learn from the past and adapt for the future. The usage of data analytics has been limited by the data quality and the existing processing power in existence today. But, prospects seem to be brighter than ever for data analytics and can help create business models with maximum optimisation and minimal downside risks.