The term ‘big data’ refers to data sets that are complex and massive in terms of quantity. Big data are data that can be analyzed to reveal hidden insights and useful information like market trends, unknown associations, patterns, customer preferences, etc.

Sometimes, big data describes the process of collecting and analyzing large amounts of data, enabling businesses to make informed business decisions. Big data cannot be processed by traditional data processing tools. 

Big data solutions can provide unlimited opportunities for businesses and make an impact on businesses, the workforce, and society.

This article provides detailed guidelines for Big Data and Analytics services to help businesses select the right technology partner in 2021.


In the era of data and information, big data is no longer new to businesses and society. Big data has existed for many years. It is known that via big data solutions, organizations generate insights and make well-informed decisions, discover new market trends, and improve productivity. And big data could be more than that. 

Big data can be generated and aggregated from various sources, including social media networks, websites (browsers), mobile applications, text messages, geographical locations from wearable devices, satellites imagery, and other media files, etc. One of the main drivers for the growth of the big data market mentioned include sensors and Internet-of-Things (IoT) devices.


As data continue to grow, organizations are looking for an innovative way to optimize big data and make a decision through the generated insights. One of the major relationships of big data on businesses is that their dependence on the internet increases as well as the amount of data generated by the rapid development and evolvement of technology. 

A whitepaper by Seagate IDC predicted that the global datasphere will be 175ZB by the year 2025. 

Big data provides accurate (if being processed appropriately) and the latest information/insights for businesses so that they can make smarter and faster decisions. The big data analytics solutions also allow businesses to improve operations and efficiency, gain a competitive advantage and explore more big data use case opportunities.

Moreover, organizations can aim to increase revenue by providing better customer services with the help of big data. Improving customer experience is probably the primary goal of most companies. Other goals which can be achieved with big data help may include better marketing strategy, lower cost, and improved efficiency.

Over the past decades, big data has brought social and economic benefits to businesses; leading to some government agencies have established policies to promote the development of big data. 

Big data can be used in many industries like Banking, Financial Services, and Insurance, Retail, Manufacturing, Logistics, Media and Entertainment, Oil and Gas.



  • Enhanced information management: With big data technology, businesses can make the most out of it since it allows them to increase their ability to discover new information, access to the data sets among all departments, as well as see all data sets that are not being used.
  • Increased operations efficiency and maintenance: Via big data and big data analytics, businesses can make better and more accurate decisions, improve productivity, and optimize services.
  • Increased supply chain sufficiency and transparency: Big data can improve supply chain visibility and transparency in real-time.
  • Greater responsiveness: Because companies gain more data, they keep up with the current market trends and conditions. This results in enhancing greater responsiveness.
  • Enhanced product and market strategy: Big data analytics allows a business to understand its customers better, increase customer segmentation, make room for better scalability and mass personalization.
  • Improved demand management and production planning: With big data technology, more insights are provided for product launch and release plans.
  • Innovation and product design benefits: A wide range of data streams can support innovation and product design.
  • Positive financial implications: If Big data technology is used wisely can reduce long-term cost, improve the ability to invest in other businesses’ tasks.
  • Increased integration and collaboration: This new technology enhances integration and collaboration capabilities, especially in the supply chain industry. 
  • Enhanced logistics: Big data enables product traceability, which leads to lead-time reduction, real-time rescheduling, route planning, etc.


Below are some of the business areas that Big Data & Analytics can help transform and improve upon:

  • Sales and Marketing: customer segmentation, customer relationship management, and offering more personalized customer care program and marketing campaigns; launching of new products and services;
  • Purchasing: Inventory management and inventory levels forecast;
  • Administration: optimizing internal resources and reducing costs;
  • Financial services: fraud detection; mitigating risks;
  • Machinery operation: identifying and removing performance bottlenecks proactively; predicting equipment failures;
  • Maintenance and support: Identifying the causes of failures and problems in real-time. 


Top 5 Big Data & Analytics use cases:

Fraud detection

One popular use case of big data analytics is Banks and financial institutions leverage this technology to monitor millions of credit card transactions, to protect credit cards holder from fraudulent transactions on a daily basis.

For an example of credit card transactions, based on historical data patterns, a sophisticated fraud prevention system of predictive analytics or machine learning might be able to assess if the business transactions performed by the credit card holder is typical of such holder and determine if the transaction is likely to be fraudulent or not.

Credit assessment

Along with fraud detection, banks together with financial institutions and insurance companies can also leverage the massive amount of data by applying data science models to assess the creditworthiness of borrowers. 

For example, financial institutions can obtain data regarding the credit history of borrowers from various sources such as telecom or utility bills and apply analytics to determine creditworthiness. As a result, if borrowers have a bad credit history leading to a low rating score, banks wouldn’t grant the loan. This helps financial institutions to reduce credit risks.


In e-commerce, by analyzing customers’ past purchase history data and shopping behavior, companies can discover various hidden patterns and offered a more personalized marketing program as well as recommending the products and services items that customers are more likely to purchase, thus boosting sales and effectiveness.


With big data analytics, inventory management can move beyond the traditional methods of analyzing historical data on sales and stocks out. By applying data science and analytics with algorithms exploring the relationship between different data elements, retailers can produce insights into customers behavior, product demand level, in-store and online sales performance, and more.

Specifically, by leveraging big data analytics, retailers can forecast the level of product demand, thus plan and optimize their inventory for maximum efficiency. This helps retails stores to allocate resources efficiently, avoiding waste of resources and money on the inventory that’s not required as well as reducing warehouse space cost.

Transportation and logistics

Data Science and Big Data, and Advanced Analytics capabilities can help transportation providers and logistics companies to optimize routing and freight movement by recommending the optimal route and time for delivery, reducing the time taken, saving on petrol, and subsequently increasing productivity.

Moreover, data science and Big Data & Analytics can also help businesses proactively enhance vehicle performance and maintenance through a real-time view of fleet operating conditions and metrics. This helps eliminate bottlenecks and improve efficiency.


Data Warehouse

The concept of data warehouse, in its simplest sense, can be explained as follows: it extracts data from source systems, and is cleanly formatted, validated, reorganized, summarized, and supplemented, these data then is moved to a dedicated server, a data warehouse. 

This warehouse will become the main source of valuable data that is ready to use for reports and analysis. 

Data warehousing can be understood as a place where companies store their valuable cleanly formatted data assets such as customer data, sales data, employee data. Furthermore, data warehousing is the single source of data truth, which is the practice of structuring all the best quality data in one place. It is usually created and used primarily for data reporting and analysis purposes. 

To have a better understanding of data warehousing, let’s have a look at its practical use: 

Taking marketing as an example, if you ask anyone who works in this domain, they are probably already familiar with Google Analytics, Facebook Offline Conversion, Ad networks, and other CRM solutions; however, some of which have caused several tiny issues that somewhat irritate marketers.

With so many data sources, multichannel advertising, business leads are all becoming such a welter. Data warehousing is a solution that allows your business to integrate data from multiple applications and sources into one location and provides an environment for insights mining, analytics reporting support decision making. 

What is ETL and why is it important?

When companies handle big data, they always need analytic data that is available within a hand’s reach to make the most accurate data-driven decisions.

As we have mentioned, a data warehouse plays a big role in providing businesses with a central place to store all the transformed data, but to be more specific, this valuable source of cleanly formatted data ready for analysis is provided through ETL. 

ETL, an abbreviation for Extract - Transform - Load is the heart of data warehousing. ETL performs data cleaning during the extraction process and loads significant data into the data warehouse. 

The whole process full description can be simplified as follow:

- Firstly, data is read within a database. This stage involves data collection and extraction. Extracted data from various sources systems is stored in the staging area, not the data warehouse because extracted data could be in various formats and can be corrupted. No SQL, XML, and flat files into the staging area.

- The next step is transformation. The extracted data is transformed from its old structure into a more standardized format. These formats are a more proper format for analysis and storage. This stage involves processes and tasks like filtering, cleaning, joining, splitting, sorting, etc.

- The last stage involves the load function in which the process of writing the transformed data is implemented. Data has now been loaded into the data warehouse. 

Regardless of the business size, level of complexity, and the number of data sources, companies will always benefit from better access to their data, especially extracted, well-organized data.

ETLs are designed to manage and scale up to huge volumes of data, without risking errors due to the size of the dataset.


Leveraging ETL  will save businesses plenty of time, effort, and money without compromising flexibility and scalability. Moreover, with ETL tools, companies need less help from IT teams and as a result, the IT department can focus 100 percent of their efforts on other tasks. 


Quite a few companies are investing in big data and advanced analytics solutions striving to gain valuable insights. From there, they can make better business decisions. However, sometimes, it seems that the details the new solution provides are of the same data quality that you had - bad data may just provide non-relevant insights. Depending on the root cause, this issue can be resolved through a business or technology lens.

New data analytics solution that fails to provide new and timely insight

Data missing

Due to the lack of data integration or poor data organization, there might be a lack of data to be analyzed and generate for new, actionable insights.

If this happens, you should run the data test to see if the existing data integrations can provide the necessary insights. Incorporating new data sources can also eliminate data shortages. It is also worth seeing how the raw data comes into the system and seeing that all metrics and indicators can be put in place for analysis. Lastly, diversity in data storage can also be an issue. This problem can be solved with Data Lake.

Long response time

When your business analytics system is designed for batch processing, your system will encounter a long response time when you need real-time insights. Therefore, the data requested is still being collected or preprocessed and is not yet available. 

What you can do is make sure that your ETL (Extract - Transform, Load) can process your data on a more persistent schedule. In some cases, a batch solution allows you to reschedule twice as fast. Another option for you is to combine an existing batch pipeline with a fast real-time stream using data-processing architecture like Lambda architecture

This approach is known for its ability to handle massive quantities of data. Lambda architecture emergence has been associated with the dynamics that minimize delays in big data development, real-time analysis, and the drive to minimize map-reduce latencies.

The old-school approach applied to the new system

Although you have transferred your report to the new integration, it will be difficult to get new answers and generate new insights when asking the same old questions. This is mostly a business problem, and the possible solutions to this problem vary widely from case to case.

Therefore, it is best to consult a big data services provider experienced in analytical methods and understand your business area.

Incorrect data analysis

There is nothing worse for a business than incorrect data analysis. If your business encounters such a problem, you need to fix it as soon as possible. 

- Poor quality data source: Most of the time, you’ll get poor results if your system relies on flawed, errors, or incomplete data. Data quality management and validation process cover all stages of the ETL process that can help ensure the incoming data’s quality at different levels such as semantics, grammatical, business, etc. This makes the data accurate by identifying and eliminating errors, making sure that one field's modifications are immediately displayed across the board.

- Data flow affected by system errors: When system requirements are ignored or not fully met due to human error intervention during development, testing, and verification, you’ll encounter this problem.

The development life cycle’s high-quality testing and verification can minimize data processing problems by reducing the number of such issues. Even when working with high-quality data, the results of your analysis can be inaccurate. In this case, you’ll need to carefully review the system and make sure that there are no errors in implementing the data processing algorithm.

Expensive maintenance

All systems require continuous investment in maintenance and infrastructure. And all business owners want to minimize these investment costs. As a business IT consultant ourselves, we strongly recommend you review the system and make sure that you aren’t paying too much, even if you’re happy with the maintenance and infrastructure cost. 

- Obsolete technologies: New technologies that can process large amounts of data in a faster and cheaper way appear every day. Therefore, why do you have to pay for outdated technologies that cost you much more? Sooner or later, the technology on which big data analytics is based will become outdated.

These technologies will then require more hardware resources and cost you more to maintain than the latest ones. Moreover, it’s even more difficult to find experts who are willing to develop and support solutions based on previous technologies.

The best solution is to keep up with the latest market trends and try to migrate to the new one, of course. In the long run, it will cost you less to maintain while increasing reliability, availability, and scalability. It is also essential to gradually carry out a system redesign, slowly replacing the old elements with the new ones.

- Infrastructure which is not optimal: Infrastructure always costs more when it comes to optimization. If you’re still working on-premises, moving to the cloud can be a smart move. With cloud solutions, you can pay for what you use and reduce costs. 

Even if you have security-related restrictions, you can still go with the private cloud option. And if you’re already using the cloud, make sure you’re using it effectively and have already implemented all the best practices to reduce your spending.


Chief Data Officers and Chief Analytics Officers will face new challenges from entrepreneurs, who need to acquire more data faster to transform data into business insights and improve results. So how what trend will dominate Big data & analytics for the rest of 2021? Here are the four major trends that will take place this year:

Business users drive demand for on-demand data.

“Time kills all deals”, this is the popular saying among sales executive and teams. A slightly different sentence applies to companies, “time kills opportunities”. If a decision needs four months of data gathering to be made, then the opportunity is lost. 

In order for enterprises to quickly obtain key results and opportunities, they need to obtain data quickly. This means rethinking the old norms of lengthy ETL jobs, complex data migration, and slow manual data preparation.

This also means that the wall between business and data needs to be removed. Technology-oriented departments do not always see things in the same way as business teams.

In fact, there is evidence that personality types that stand out in tech roles may be significantly different from those in other departments, like marketing or sales.

In the discussion on improving cross-departmental collaboration, it is important to manage a collaborative culture to ensure the maximum benefits from adopting Big data and Analytics can be achieved.

Data Visualization

For anyone who is not familiar with the concept of data visualization, data visualization is the graphical representation of data. Data can be visualized using charts, graphs, and maps. It is everywhere. A recent report predicts that the visualization tools market will grow from $8.85 billion to $19.2 billion in 2027.

Visualization is no longer just for analysts. Data-aware employees currently use it to help drive their organization forward.

Artificial intelligence-driven data storytelling and narrative

Although there is an old saying that a picture is worth a thousand words, using data to tell a story is not always that simple. The pictures made from data usually require explanation, and not everyone has the same (Data) visualization and storytelling skills. Or the story cannot be told consistently to everyone.

Some of the best practices for data visualization are understanding your audience, making visualization as simple as possible, and also providing a platform for your team to work together on data visualization.

This is where AI will play an important role in explaining more accessible, consistent, and engaging answers to your data. If applied properly, an organization will spend less time discussing the analysis results and more time for action.

Data & Analytics Team collaboration 

It is expected to see specialist collaboration solutions in 2021 that enable big data and analytics teams to communicate with each other and interact with customers.

IT companies have transformed from telephone and email interactions to modern multichannel customer experience solutions. IT is acting as a partner for service providers more than ever. Data and analytics could follow the IT’s lead.

It is reported that many businesses tried to improve the performance of a metric-gathering application, but were unsuccessful until they chose to use an embedded analytics solution. Here, embedded analytics analyze the data resided within the software application into which the analytics platform is embedded.

Why should you hire a Big Data consultant?

A big data consultant will help your company to solve current technical problems related to your Big Data project, improve what your team can do, and give the C-suite a clear conclusion.

With his or her technical knowledge, he or she can help in the preparation and analysis of data to generate insights, support the decision-making process in sales and inventory forecasting, minimizing financial risks, targeting market segmentation, scheduling maintenance actions, etc.

Why is a big data consultant indispensable?

The answer to the question is simple: because big data is a vast area of technology with many challenges, including capturing data, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and so on. Let’s look at an example to have a better understanding of how complicated the process can be:

In many financial institutions, the credit assessment department gives its customer a score based on their credit history. This results in different types of classifications and ratings in credibility for loan approval.

The credit system, in order to be run smoothly, it incorporates many technologies like machine learning system, big data analysis technology, and artificial intelligence, etc. Considering the number of customers has risen dramatically over the past few decades, in order to analyze the data of million customers, big data analysis technology is a critically needed point and a big data consultant is vitally important at this point.

Big data experts can leverage their methodologies and technical capabilities to help with your projects.

Some companies hire a big data consultant or big data consulting company when they reach the point of taking the entrepreneurial leap, or when they need to fill a knowledge gap.

How to hire one?

There are some specific steps you can take to choose a big data consultancy company, here are some basic considerations which you should take into account, including:

  • Match your problems with solutions
  • Evaluate off the shelf products 
  • Evaluate Company Listing
  • Look at Vendor Portfolio Case Studies
  • Interview Consulting Company Evaluation Phase

Simple as it sounds, a big data consultant should also design strategies and programs to collect, store, analyze, visualize data from many sources for specific projects. He or she, at the same time, needs to lead or support leading a team and project in a way that ensures quality and punctual delivery. 

He or she should also have sufficient technical knowledge to give educated information and advice regarding big data.

Preferably, a big data consultant should be able to program in different languages like Python, R, Java, etc.; has an understanding of Hadoop, Hive, HBase, MapReduce; be familiar with disciplines like text mining, outlier detection, clustering analysis, predictive analytics and so on.

A big data consultant also needs to be able to communicate well in English in this global context.

Additionally, A big data consultant is someone who:

  • Has excellent oral and written communication skills
  • Has a terrific interpersonal skills
  • Has a logical approach to problem-solving  and outstanding analytic ability 
  • Has researching skills and is able to integrate best practices into problem-solving and continuous improvement
  • Is self-reliant and capable of both independent work and as a member of a team
  • Develops and reviews project plans
  • Is able to identify problems and opportunities

It is not questionable that a big data consultant holds an important role in businesses if they want to develop a big data strategy. Same as a Data Scientist and Data Analyst, the role of a Big Data consultant is to make your data coherent and understandable.


Large scale organizations and enterprises often use big data & analytics technologies to set their footprints in the industry, and big data consultants are hired to make advanced analytics, predictions, evaluations, and even to provide a better understanding of customer behavioral patterns.

At TP&P Technology, we offer IT consulting services and software solutions that will help you to get such competitive advantages. Talk to our specialists today to know more about what we can bring to your businesses.