Big Data & Analytics: A Comprehensive Buyer’s Guide

Data moves businesses in the modern era. Time has changed much about the business world but the market remains as competitive, if not more, as it was in the past, and businesses that rely on data analytics are more likely to secure greater success for their endeavors. Big data reveals customer trends, likes, dislikes, and much more to allow businesses to mold their strategy as per the needs of the clients. Failing to take this into account will surely lead to futile efforts on part of the business managers and lower revenues than otherwise possible.

Of course, data analytics has come a long way since the 60s, and today, people can go through large chunks of data in relatively shorter instances of time. Businesses can get the answers they seek the most based on user preference data, marketing returns, and the ROIs of other strategies. The data patterns, correlations, and dynamics can help business managers direct their efforts towards areas that matter the most; not only will this produce better results, but also lower the overall business costs.

The benefits of big data handling and analytics are too numerous to list here at the beginning of the discussion, this will be covered in a separate section.

For now, the main concern is how does big data analytics work and why is it needed to begin with?

Big Data Analytics: What Is It?

The title is pretty much self-explanatory, data analytics on a larger, grander scale, can help businesses harness the true potential of their modus operandi. Though the data to study is in bulk, and can’t be studied as simply (without dedicated expertise in advanced analytics and data science), the investment does pay off with smarter, more efficient business decisions, better profits, and satisfied clients. Not only does data analysis help companies redirect their efforts to segments that need attention but also helps them devise smarter and more efficient business strategies.

But higher profits and happy clients don’t happen just like that.

Data analytics and data science are professional services that demand dedicated professionals with relevant experience in the basics of the field, and a working knowledge of software to be able to give desired results. Usually, companies have very diverse data analysis needs, such as advanced analytics, and some companies are undergoing a transformation in this area to be able to save resources from futile efforts. In all such cases, experienced data scientists with full competence in the technology of leading tech services such as Oracle, Microsoft, Amazon, and many more, can make a difference.

In short, big data analysis is an actual science, and only data scientists with relevant experience and expertise can make a difference.

Why Do Businesses Need Data Analytics?

Businesses have one common goal across the board: to minimize the expenses and at the same time, boost the output. In short, spend less and get more. Data analytics can help managers with this and many other business-oriented segments which will make the setup more efficient and profitable than ever. Analyzing the needs and wants of the clients, and the success rate of the past strategies is sure to reveal valuable insights about business growth and success.

Here’s how big data and analytics can help businesses:

Cutting Down The Costs

Cloud-based data analytics software allows experts to go through an endless stream of business data with regard to spending and profits. Such software can not only store data in bulk and process the same but also help experts identify core areas of cost reduction. This way, business managers can divert funds from less productive areas and reinforce more effective strategies. Overall, the business cost will go down and the profits up. Consider this: if certain tiers or packages aren’t selling well, why waste time and energy promoting them, why not remix things and deliver what the clients would want?

Overall, the business would become much more efficient.

More Efficient & Faster Decision Making

Time is another resource that businesses can’t afford to waste. Analytics can help businesses see the possible effects of all possible decisions, and thus help them make smart choices for their setup. A load of information made available by data analytics can help managers make informed decisions, i.e., whether or not to pursue certain marketing strategies, when to launch discount offers, and so on. All of this can help businesses claim the maximum output.

Help With Improving Existing Services

Customer experience is key to a successful business. Without prioritizing customer satisfaction, businesses can’t hope to stand up to their market competitors. Data analytics help businesses see what their customers like the most about their services and can thus do more of that stuff. By modifying existing service tiers to best suit the needs of the customers, businesses can maximize their outreach.

Ideal Product Launch Strategy

This also goes for products. Launching new products and services is a risky business, but that's also how setups grow. Business managers usually monitor customer preference trends and survey reports to see what sort of products do clients want. Competitor research can also help a great deal here. But how to see which features of the said products attract clients the most? This is where data analytics can help businesses create new products that address the needs of their clients.

The Present & Future Of Big Data & Analytics

Businesses can't avoid big data, it's all there. The choice is whether to ignore it or to analyze the data trends to make smarter, more productive decisions. Harnessing the power of big data is already helping private businesses and many public sectors improve their services. The future is only brighter for such setups which aim to gain maximum value from the data. Data can help decision-makers with the latest thinking capabilities, and here's how it all works:

Machine Learning

Machine learning is nothing new, and more and more websites and apps are incorporating the same to deliver the best client experience. But how is any of this possible without data? Learning, after all, only comes with data, and that is how machine learning goes hand in hand with big data analytics.

Healthcare Sector

The healthcare sector is also driven by data analytics, especially in the context of preventing disease outbreaks and providing healthcare services to all affected people. Data scientists collect patient information, store and sort the data, collect medical images, doctor’s notes, R&D outcomes, EMR data, HER data, and so on. Then after careful analysis of the data, patient monitoring trends can be developed to help optimize the level of attention that a patient gets.

Also, data analysis helps healthcare centers personalize their care plans for the patients. On top of this, data analytics can help medical setups to forecast supply-demand trends, allowing the hospital or clinic to buy smartly and maintaining an optimum supply chain.

Manufacturing Businesses

Manufacturing data is also very complex, and detailed analysis can help companies optimize the process of building stuff and to increase the longevity and cost-effectiveness of their products. Data about the model, year, engine temperature, and performance, can help manufacturers monitor the production process much more closely. Also, usage of materials, equipment data, and factors affecting production time are important variables for manufacturing businesses, and data analytics can help make sense of these data groups.

Financial Sector

The same goes for the financial sector where big data encompasses customer response, transaction data, events of interactions, AML transactions, risk models, patterns of fraudulent schemes, and so on. An in-depth analysis of this data group will help financial sector businesses better understand the risk associated with certain types of transactions, allowing them to make smart decisions and minimizing the risk of any major loss.


Once again, the set of data is different here, but the result is pretty much the same. Logistics data includes location, details of the cargo, distance, maintenance needs of vehicles, driver performance, traffic insights, weather details, fuel expenses, delivery route data, and so on. All of this can help companies optimize their scheduling, routing, and cargo assignments for the delivery workers.

Retail & eCommerce

Retail businesses, both physical and eCommerce based, are reliant upon supply and demand. Forecasting changes in supply and demand is key to maximizing sales in certain areas and to a certain demographic. The same also goes for marketing strategies. Data from past purchases, applications, social media engagements, surveys, demographic data, and customer behavior can help businesses provide customers with what they want and when they want it. Also, forecasting the trends in demand for certain products will help businesses provide more of such products when they are in high demand and save up on money by not stocking up when they are not likely to be sold (especially for perishable or seasonal items). Also, certain products tend to be more popular in certain areas, and data analysis can help businesses figure out that as well.

Customer Support

Social media reports, data from websites, calls, and other records can help businesses improve their customer support features. Data analytics is especially helpful if businesses use chat-bots, and can help in improving retention rates for startups. Also, spending trends, transaction preferences, and the lifetime value of clients are important to big data groups.

Fossil Fuel Business

For oil and gas, drilling data, equipment maintenance reports, seismic monitor data, historical trends, and so on can help businesses improve their search protocol.

There may be several other examples that make the same point, but the idea is pretty much solidly entrenched: there are many big data points in all sorts of businesses. Data analysis experts can help businesses make sense of all the information that their experiences have generated.

But how does it work?

Key Technologies Involved In Big Data Analytics

Big data and analytics are not backed by a single technology – there is no single focal point for the service, instead, this is where many technologies and ideas converge. Many technological advances have made it possible to gather and analyze data is done today. Though advanced analytics is a relevant technology, there are other key elements as well.

Here are the most relevant segments of big data and analytics:

Machine Learning

Machine learning is a particular element of AI that instructs a machine on how to learn. It makes it possible to rapidly and automatically produce models that can evaluate bigger, more complicated data and convey faster, more precise results even on a very large scale.

And by building precise models, an association can better identify profitable opportunities or prevent unspecified risks.

Data Management

Before being analyzed, data must be well-governed and of premium quality. The data has to be moving constantly in and out of an organization, therefore it is essential to organize repeatable processes to create and maintain standards for data quality.

Once the data is reliable enough, the organizations should establish a master data management program that can get the entire enterprise on the same page.

Data Mining

The technology helps examine large amounts of data to discover patterns in the data. Further on this information is used for further analysis to help answer complex business-related questions.

A data mining software can also allow the user to refine through all the unsettled and repetitious noise in data. It can pinpoint the relevant data, use that information to assess likely outcomes, and then stimulate the pace of making informed decisions.


It is an open-source software framework designed to store large amounts of data and run applications on groups of commodity hardware.

Hadoop is used as a key technology for businesses due to the constant advancement of data volumes and varieties, and its distributed computing model processes big data fast.

An additional benefit is that Hadoop's open-source framework is entirely free and uses commodity hardware to store vast quantities of data.

In-Memory Analytics

The software allows users to conclude rapid insights from the data and act on them quickly by assessing the data from system memory. This technology can eliminate data prep and logical processing latencies to evaluate new scenarios and develop models.

It's not only a convenient way for organizations to stay flexible and make better business decisions, but it also enables them to run interactive analytics scenarios.

Predictive Analytics

Predictive analytics technology is intended for identifying the likelihood of future consequences based on historical data. It uses data, statistical algorithms, and machine-learning techniques to offer precise and swift results.

The software is all about creating an accurate assessment of what will happen in the future. Therefore, organizations can feel more confident, whether they're making the best possible business decision. Some of the most common applications of predictive analytics include fraud detection, risk, operations, and marketing.

Text Mining

By using text mining technology, enterprises can evaluate text data from the web, comment fields, books, and other text-based references to discover insights that hadn't been noticed before.

The process is based on a machine learning or natural language processing technology that can sift through documents like emails, blogs, Twitter feeds, surveys, competitive intelligence, and more. It helps analyze enormous amounts of information and uncover new topics and term relationships.

What Is Encompassed In Big Data Services?

Big data and analytics are two faces of the same coin, however, the services themselves are distinct. One aspect deals with the gathering of data and the other is about analyzing the same, i.e., making sense of the said data.

Here are the most important services/packages in this regard:

• Consultation services for data implementation, support, and analysis

• Setting up or implementing data solutions, a data lake of sorts, for the business

• Administering, updating, handling, management, cleaning, backup, recovery, health checks, monitoring, troubleshooting, and other big data support services

• Lastly, the big data analytics services

How Xpeer Big Data & Analytics Services Can Help You?

We have listed some of our most in-demand big data analytics services, all available at your disposal, even at short notice. Our team of experts is dedicated to providing the best services for affordable rates, and you will see the return on investment soon enough. It is about time you stopped leaving things to mere chance and instead engaged in business based on statistical data rather than intuition or something else of the sort.

In addition to the aforementioned service, we offer 100 IT services to suit all needs and business models, such as blockchain development, WordPress development, user interface (UI) & design, web development, user experience UX, Shopify development, mobile app development, and many more. You can reach out to us whenever you feel like it, and we will deliver exceeding your expectations of top-tier services in all areas mentioned.

We are all committed professionals and will help you make the impact you want!