How big data analytics can improve your business?

Oksana Zabolotna
HBD at Qubit Labs

In the last decade, Big Data business has flourished globally. Let’s start with what big data is. In plain, it is extremely large data sets that can only be and usually are analysed computationally. That analysis can reveal patterns, trends, and associations, especially relating to human behaviour and interactions.

So, let’s look closer at how big data analytics work, what’s so important about it and how big data can help businesses.

Big data analytics is that complex process of examining big data to uncover above-mentioned information. This information is extremely valuable for businesses and can help them make informed strategic decisions about their operations and lead to positive outcomes such as new revenue opportunities, more effective marketing, better customer service, improved operational efficiency, and competitive advantages over rivals.

How big data analytics works?

Popular approach scientists resort to in data analytics in business is the concept of a Hadoop data lake where streams of raw data come into and said data is stored. In such architectures, data can be analyzed directly in a Hadoop cluster or run through a processing engine like Spark.

When it comes to data warehousing, safe storage is the first key to the complex process of data analytics. To achieve effective performance for ETL (extract, transform, load) integration jobs and analytical queries, information stored in the HDFS needs to be organized, configured and split into portions properly.

Once those processes are complete and the data is ready, data scientists can proceed with the analysis using the software commonly used for advanced analytics processes.

How big data analytics can help you to run your business?

Even though there are plenty of big data challenges, data is undoubtedly one of the most valuable things for a business these days and there are multiple uses of big data. Extracting information from customer data gives a more precise and complete image of what the customer wants, needs and is ready to obtain as a good or service. It’s no surprise more companies are gradually turning towards a data-based business model as it’s much more objective and brings tangible results times faster than any other method used to make consumer behavior predictions in the past.

Here are a few examples of how big data can help businesses and what organizations should do with big data:

  1. Increasing Customer Acquisition and Retention: A client base is naturally the most important asset a business has and keeping the customer happy with your product is something every business is after. Not knowing what exactly your customer wants and how their wants and needs change can easily lead to a drop in product/service quality and consequent loss of client base. Naturally, one of the challenges of big data in business is keeping your customer.

Big Data Analytics lets the company understand their customer’s needs by observing trends and patterns in the clients’ behavior which allows these businesses to follow the patterns and market their product/service specifically tailored to the consumer. Needless to say, a happy customer is a loyal customer, and this is the basic yet the best way to increase customer retention.

  1. Risk Management: Diving into business without a proper risk management strategy can often mean commercial suicide for a company. A risk management plan is a vital investment for any business regardless of the field or product they offer. Being able to predict a potential risk and mitigate it before it damages the business flow or even before it occurs is critical for a business to remain profitable. And that doesn’t just come down to selecting a proper insurance plan.

Big data analytics can be used to collect and analyze the abyss of internal company data and help in developing short and long term risk management models. Considering the increasing availability and diversity of statistics, big data analytics has a huge potential for enhancing the quality of risk management models.

  1. Product development: Big data analytics basically allows businesses to run ahead of the consumer, figure out what they want and then create the product/service tailored exactly for them, as opposed to creating something and seeing if the customer likes it. Data helps keep track of the company’s products, its competitors and customer feedback.

Companies can use data like previous product responses, customer feedback forms and info on competitor products to understand what kind of products the buyer wants and then develop products that are maximally tailored to those needs/wants

  1. Chain management: Especially for large companies, the supply chain is a gigantic organism that’s quite hard to handle at minimal risk. It’s a process that consists of thousands of people, materials and products moving all over the place while the result of this needs to end up in a customer’s hands in the precise form and at the exact time they wanted it.

So among other big data challenges, this is a big one. Big data provides greater accuracy for supplier networks while delivering clarity and insights. Putting big data analytics to use helps suppliers achieve contextual intel across the supply chains.

  1. Marketing Campaign: Not only is it important for a business to attract new customers, but it’s also vital to retain the old customer base and keep it satisfied. One can see companies using big data for marketing all over the place. Big data analytics does a lot of good for advertisers since companies can use this data to understand customers purchasing behaviour.

Companies using big data for marketing means observing customers’ online activity, looking into the point of sale transactions, and making sure the reaction to the dynamic changes in customer trends is immediate. More targeted and personalized campaigns provide for more efficiency and less spending which opens more time and funds to be invested in other areas of the business

The biggest challenges of big data implementation

We’ve mentioned some problems, so what are the challenges of big data in business? The handling of big data is very complex and there is no progress without an obstacle, of course, so here are a few bumps often faced by the big data analytics industry:

  • The Uncertainty of Data Management

The wide range of NoSQL tools, developers and the status of the market is creating uncertainty with the data management. It’s often difficult to estimate what software to use for optimal storage, analysis speed or output of results. So, if you’re new to data analytics, it is definitely advisable to hire help.

  • Talent Gap in Big Data

The tools designed for data analysis keep evolving and the range of software available is so vast, there’s not always enough talent to use that software. So the slightly surprising reality is that there is a lack of skills available in the market for big data technologies. This also means if your company relies on complex data analysis, you’ll probably have to cash out for all the experts and software.

  • Getting Data into Big Data Structure

Even though the whole concept of Big Data Analytics implies dealing with large data sets, some often forget what kind of scale is in fact in question. The complexity behind the transmission, access, and delivery of data and information from a wide range of resources and then loading these data into a big data platform is often underestimated and hence a lot of doomed expectations are created.

  • Dangerous big data security holes

As advanced as data analytics software is becoming, security continues to not be its strongest suit. Often in data software development, the security features are left for last or neglected since it’s hoped that security would be granted on the application level. This can simply be fixed by paying more attention to security and prioritizing it for once.

  • Troubles of upscaling

Big data’s favorite thing to do and one of the biggest big data problems is to grow and this “hobby” often poses a real challenge for the data scientists. So, a couple of important things to keep in mind while developing big data solutions are a decent architecture and designing your algorithm with upscaling in mind.

Best resources to learn big data analytics

  • Big Data and AI Toronto

Big Data and AI Toronto is going virtual for its 5th edition! This year, Canada’s #1 data and analytics event will be delivered to you exclusively online, featuring over 200 speakers and leading solutions providers. Join thousands of attendees on September 29-30 from the comfort of your home and engage with global leaders in Big Data and AI innovation. Stay on top of the latest developments, trends, products, and innovations redefining the tech industry with 2 days of exclusive sessions, case studies, panel discussions, and demos.

  • LinkedIn Learning

This is something to start from, a set of data analytics courses to help you learn more about how big data can help businesses. LinkedIn Learning offers an impressive range of courses, video tutorials, and learning paths. You can take advantage of those individually or set up for your whole team or even company to benefit from the courses. Linkedin Learning also has more advanced courses can be accessed as a Premium subscription for $29.99 a month or for $299.88 a year.

  • Coursera

Coursera offers a full spectrum of Big data analytics courses for you to choose from – from beginner to expert. As the tech constantly develops, every data scientist, no matter how experienced, has to update their knowledge constantly. You’ll find data science courses from PwC, John Hopkins University, UC San Diego and Duke University on Coursera costing between $39 and $79 per month.

  • DataCamp

You’ll find a great variety of online data analytics courses on DataCamp, many involving key programming languages R and Python. It works on a monthly subscription of $29 (slightly cheaper if you commit for a full year) and will provide you with courses on regression models, predicting analytic, marketing analytics, etc.

  • EDX

EdX is another great source of learning how to use big data. Here one will find courses like Statistical Thinking for Data Science and Analytics and Machine Learning for Data Science and Analytics from Columbia University or a more specialized course in Marketing Analytics from Berkeley, University of California, taught by industry expert Stephan Sorger. A lot of courses are free and you will only need to pay if you’d like an official certificate.

How to implement big data? 

As mentioned previously in this article, if you’re new to the world of Big Data Analytics or even if you’re not a noob but not an expert either, the best way to start is to hire help. The world of big data is quite overwhelming and starting the journey is always safer with someone who’s already walked the path of data analytics in business.

Qubit Labs is a globally well-loved offshore development center where you can hire experts or teams of experts to help you with your data analysis endeavours and to empower your business with Big Data. Qubit Labs can help with Big data processing and warehousing, big data collection and analysis. To obtain a wise business strategy, a company must have a precise understanding of the processes that matter to its business success, so let professionals navigate you in the ocean of big data.


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Oksana Zabolotna
HBD at Qubit Labs

HBD and Head of Partnerships at Qubit Labs. Oksana is proficient at Search Engine Optimization; performs as a speaker for international tech conferences; author of webinars and guides on peculiarities of remote recruitment, top markets for hiring IT experts, and latest tech trends. Oksana is one of the partners of Women in Tech Ukraine – large-scale social project created to increase the number of women in IT industry.