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Importance Of Building Data Analytics For Enterprises In Today’s World

Data is everywhere and all the things you do create new data. All kinds of electronic messages that you send or receive and even visiting a website contributes to data storage. And this is why data is amongst the most valuable commodity. Big Data refers to the voluminous and vast data sets that can be unstructured or structured.

Data Analytics is examining raw figures and facts to obtain valuable insights from it. Currently, #Data Analytics for business is being used with Big Data in most of the industries. The concept of Big Data has been there for several years now and many firms have realized with time that if they are able to capture relevant data into the business, the companies can easily apply analytics and derive important value out of them. However, some businesses choose to avail services of Big Data Analytics companies because they may not have enough funds or expertise.

Why Does Business Need Data Analytics?

Data is increasing aggressively every day. Data generation happens through many industries, businesses, and users. So, the future of Big Data Analytics is more challenging. Also, amalgamating the data which has been generated via businesses is crucial because if the data gets wasted, valuable information would be lost.

In the past, processing the data needed skilled analysts but now, tools are employed for high-speed data processing, which helps in incorporating Data Analytics during decision-making times. Big Data Analytics is important because it helps in tackling the data and using it to discover new opportunities. As a result, higher profits, smarter business moves, happy customers, and efficient operations are seen.

Once a business adopts a data analysis method, it can analyze the causes of specific events on the basis of data, understand directives and objectives, and have technical insights using a simple language. A business should build Data Analytics due to the following reasons.

Innovation

Data Analytics gives a rough idea about future trends in customer behavior that will allow you to create futuristic inventions of the products. This way, your business can make products and make services that make you stand ahead in the competition in your industry. Also, with these innovations, your business can maintain a high position in the industry. You can also patent those innovations and yield the best out of them while also accumulating high amounts of profits. You can accordingly carry out your enterprise app development as well.

Read Also – Data Analytics vs. Big Data vs. Data Science – A detailed comparison

Segmentation

Segmentation includes dividing the customer data on the basis of shopping habits, product usage, location, age, gender, etc. This information can help you in creating messages that resonate will all the segments individually. Whether it is the people belonging to the demographic group, staying in a specific area, or demonstrating interest in a particular activity or hobby, you can customize your marketing to directly appeal for their individual interests and needs.

Through segmentation, you can also assess which groups are highly profitable for your business, letting you identify your best customer segments as well as avoid wasting money on the segments that would not yield conversion. Many companies have begun assigning ratings to the customers using CLV (customer lifetime value), which determines the worth of the customer for the company as compared to other customers. Knowing which customers you should focus on as well as invest in is important for maximizing your profit.

Product Development

For being competitive in the customer-centric market, your business’s service or product needs to be customized according to the customer. Collecting data from surveys or from using A/B testing in order to experiment with disparate approaches can be a great way to ascertain what works and what does not. You can use the customer feedback to enhance your product’s quality or perform your enterprise app development and identify the right opportunities for innovation which will distinguish you from your competition.

Read Also – How Big Data Analytics Can Create a Billion-Dollar Mobile App UX?

Supports Agility

It has become very important for your business to be agile in this dynamic market. As acquiring new consumers gets increasingly complex, more companies are funneling their resources towards keeping the current customers engaged instead of finding new ones.

And this is not an easy part because successful customer retention implies constantly adopting the changing needs of the customers, anticipating problems, providing solutions, and then being able to adjust your strategy quickly according to it. But if businesses use the services of efficient Big Data Analytics companies, this entire process becomes easier because of the efficient use of Data Analytics.

Operational Efficiency

Through Data Analytics for business, companies can identify potential opportunities to maximize their profits and streamline operations. It helps find potential problems, remove the procedure of waiting for those problems to take place, and then take relevant actions. This allows the companies to know which operations have reaped the best results under varying conditions and find which operational are is error-prone as well as needs to be improved.

Read Also – How to Build Successful Mobile Apps using Big Data?

Saves Money

If you know what the customers know beforehand, you can make your marketing campaigns highly customer-oriented. This enables a business to customize its advertisements for targeting a particular segment of its customer base and yield better results. So, it saves the business’s money they would otherwise spend on advertising to their entire customer base.

For example, if a skincare brand wants to introduce an age-reverse serum meant for women above 50 years of age, advertising the serum to women in their 20s would not yield them many profits. So, through Data Analytics, the brand can reach its potential customers only.

Better Decision Making

When it comes to decision making, analytics and data are invaluable, as they provide insights about whether the business is moving in the right direction or not. It helps find market needs and trends while finding why a particular product is doing well while others are not.

Such data can aid in taking vital decisions including pricing policies, market expansion, customer care, widening its product range, etc. A business that takes decisions on the basis of Data Analytics usually enjoys an upper hand over its competitors. This is because instead of ambiguous information and guesswork, it can base the decisions on real and genuine data.

Summing Up

The future of Big Data Analytics is extremely wide and important. Big Data is increasing exponentially each day and if it is interpreted correctly, it can prove to be a blessing for the business. If the business is able to understand and interpret data rightly, it can exercise numerous benefits like better decision making, innovation, product development, targeted advertising, and improved operational efficiency. As a result, a lot of money can be saved. This is why Data Analytics for business is highly crucial.

Data Analytics vs. Big Data vs. Data Science – A detailed comparison

In the digital world, the volume of unstructured data is rising every day. With this elephantine data, various avenues have been developed in the Big Data landscape, including Data Analytics and Data Science. Although people generally use the terms interchangeably, all of them perform varying but significant jobs. Also, there is a huge difference between these concepts.

To know the difference between these three jargon, let us first understand data. The collection of facts, as well as bits of information, is known as data. There are two types of datastructured and unstructured data.

  • Structured data is the one that has a well-defined structure and order. Because it is well-defined and consistent, it is easy to store as well as access it.
  • Unstructured data is the inconsistent type of data that does not have any format, sequence, and structure. It is error-prone when indexing is performed on it. This is why it is not easy to operate and understand unstructured data.

Data is amongst the biggest assets a company possesses in the current time. Consider bank payments. When you shop online, merchants collect your data related to your transactions, as well as of other shoppers, like

  •  Mode of payment
  • Time of transaction
  • Date of payment
  • Amount
  • Frequency

Then, the merchants analyze this data and make data products based on these parameters, which exclude customers’ personal information. These data products are then sold to banks that use this information to target potential customers by offering exciting offers to them based on these data products. As a result, they are able to increase their customer base.

Understanding Big Data

Big Data refers to a significant amount of data and deals with complex and large sets of data that conventional data processing systems cannot handle. Big Data has techniques and tools that extract structured, semi-structured, and unstructured data, store this data systematically, and then produce useful information from it. There are six characteristics of Big Data, which are mentioned below.

Variety – There is a wide range of data collected from various sources, which can be videos, images, audio files, unstructured data, or documents. Big Data tolls help in processing all types of data.

Volume – The volume of data generated each day from various sources is immense. Using Big Data tools, storing huge amounts of data has become easier.

Veracity – This relates to the quality of data collected. Businesses should take care of data quality while gathering it so that information is relevant.

Velocity – The number of users of the internet is growing aggressively in the digital era. Because of this, the velocity of data generation gets enhanced. Velocity here means how fast data generation along with its processing is occurring. Velocity is used to comprehend trends in data as well as meet market demands.

Variability – Market trends keep on changing, and how often they change implies variability. Big Data aids in managing it that benefits businesses to design and offer the latest products.

Value – The aim of Big Data is to collect data that is valuable for the business. This helps them increase their profits and compete in the market.

Read Also – How Big Data Analytics Can Create a Billion-Dollar Mobile App UX?

Understanding Data Analytics

The aim of Data Analytics is to give operational insight into complicated business situations. The primary concern of data analyst is to look into historical data from a contemporary perspective and then, find challenging and new business scenarios to develop the data analytics solutions. After doing so, they apply powerful methodologies to search for better solutions.

A data analyst predicts upcoming opportunities that can be exploited by the company. So, the duties of a data scientist and a data analyst are similar to one another. But they vary in the way they are implemented. This is how a data analyst functions –

  • A data analyst collects data for their company from different sources.
  • They perform the exploratory Data Analytics for visualizing the data.
  • The data analyst then cleans and filters the data by checking reports made with the help of Data Analytics tools.
  • Then, the data is examined with a data visualization tool.
  • They also build useful strategies for optimizing the data’s statistical analysis.
  • This helps businesses note down the market trend or growth.

Read Also –  How to Build Successful Mobile Apps using Big Data?

Understanding Data Science

You can understand Data Science as the dicing and slicing of huge chunks of data. Data Science uses techniques for obtaining insightful trends and patterns from the data. It is the responsibility of a data scientist to uncover hidden facts in the complicated web of unstructured data. This is important because it helps in making crucial business decisions as per the market trends.

Data Science involves making of Machine Learning models as well. To understand Data Science deeply, let us understand how its life cycle.

Knowing business needs – Data scientists perform a structural analysis of a business model. They understand customer needs and market trends. This allows them to understand business requirements.

Gathering data – On the basis of business requirements, Data Science involves the collection of relevant data.

Data comprehension – After collecting data, understanding it using visualization techniques and tools is the next step.

Preparation of data – The data functions as a basis for creating an effective model and strategy. So, it needs to be prepared accordingly.

Creation of system – Using Machine Learning, Data Science helps in building models and systems on the basis of the prepared dataset. These models are used to fulfill their business needs.

Evaluation of system – Making the system is not sufficient, assessing its accuracy is also important.

Implementation of the system – After checking the system’s performance, it is placed for implementation. After that, the system is implemented.

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How Are Data Analytics, Big Data, And Data Science Different?

The three concepts are contrastingly different from one another but they work together closely and deal with the same thing i.e. data. Big Data refers to gigantic and complex data sets, and deals in the types of data formed (unstructured or structured), volumes of data created, and the rate at which data is generated. Its application areas are communication, retail, financial services, and education industries.

Data Analytics refers to the procedure of analyzing data sets to draw meaningful conclusions and information from it by using specialized software and systems. The techniques and technologies of Data Analytics are broadly utilized in the commercial industries to allow businesses to make better business decisions. Data Analytics can also be understood as the process of comprehending and devising effective trends from recorded data utilizing statistics, predictive modeling, mathematics, and machine learning techniques.

Data Science refers to the interdisciplinary field about scientific processes, systems, and methods to extract insights and knowledge from raw data in different forms. In simpler terms, it is a combination of programming, statistics, problem-solving skills, and mathematics for looking at things differently, deriving trends from data, cleaning it, preparing it, and aligning the data in order to retrieve valuable information and insights from the raw data.

How Big Data Analytics Can Create a Billion-Dollar Mobile App UX?

In the past couple of years, there has been a constant evolution of the technical aspects of the procedures of app development, and this has also been instrumental in bringing a major change in the non-technical side of it. And, big data needs to be thanked for all these changes.

There is nothing new about the fact that more and more people use multiple mobile apps at present, which is exactly what makes it a possible multibillion-dollar business in the future. And, for you to make sure that the apps help you earn the billions, you need to deliver the best possible user experience. The role of big data is undeniable in that regard.

With the modern smartphone users generating a huge volume of data on a daily basis, it gives developers a chance to make better mobile apps through improved Mobile App UX. The apps developed this way not only meet users’ expectations to the optimum, but also help businesses earn the bucks that they were aiming for.

So, how can big Data Analytics for Mobile Applications assure a major breakthrough in user experience? How does it help in creating that billion-dollar mobile application UX? Read on to find out the answers to all that.

The value of user experience in building the finest mobile application

What makes an app truly amazing? Of course, it has to be fast, attractive, and easy to use, but most importantly, it has to make sure that the users’ needs are met to the optimum. This is the reason why it is important to study user experience in as much detail as possible. After all, it helps the developers come up with better mobile apps because they get a clear and precise idea about what the user looks for in an app.

Think of user experience as this rich source of information that helps in identifying the right ideas needed to build new apps. Analyzing the ways in which the users interact with the mobile apps and their actions at the time of using it help the Mobile App Development Companies to pinpoint the solutions that can help in improving and upgrading the existing apps, along with garnering enough user-based ideas for the new applications they come up with.

Read Also –  How to Build Successful Mobile Apps using Big Data?

Detailed analysis of user experience through big data

As already mentioned before, developing a mobile app requires a thorough analysis of the users’ experience and understanding it deeply. Big data comes as a blessing in that regard because it covers all that you need to know about users’ experience using a mobile app. It gives you a precise idea about the important pain and focuses points present in the users’ experience.

As the entirety of crucial analysis gets completed, knowing the collective preferences of the users can help in establishing a complete list of wants and needs that the users have in terms of a mobile application.

Furthermore, it can be of huge help to the developers in terms of the new ideas and apps that they are coming up with. Analyzing big data related to the other top apps that belong to the same niche as the new one, the app developers can improve their latest creations. They can find out new and improved ways to make sure that their new apps are more in line with the expectations and needs of the users.

Read Also – How Artificial Intelligence is Driving Mobile App Personalization?

Let’s understand it further with the example of fitness apps. Building a great fitness app leads the developers to go through the other popular applications in this particular niche, like Fitstar, Runkeeper, or Argus, and find out exactly what works for these apps, and what makes users prefer these apps over the other.

Now, a detailed analysis reveals that there are features like a calorie counter, pedometers, along with many other unique aspects that make these apps gain an edge over those other in the market. So, now the mobile app developer understands the features that he can include in his new app, and the already popular features that he can do better. You can guess how big data analytics helps you in edging past the competition.

Understanding the use of big data for mobile app UX

Big data is helpful in delivering a complete user experience because it is larger than life. The amount of data that users generate has already moved way past the petabyte level into the zettabyte level, and even then it continues to increase at a brisk rate every single day.

In the next couple of years, the amount of data stored all over the globe is expected to reach yottabyte levels. In fact, it will not be an exaggeration to say that the volume of unstructured data made only in the last couple of years has gone way over the total amounts of data that was produced right before it.

In this situation, it is important for high-end analytics to come into the picture if these massive amounts of important data have to be put to good use. But, the efforts definitely make a lot of sense because it can make relevant data, which can be utilized for maximizing the success of present applications and developing more effective and inventive new apps.

Read Also –  Reasons Why Everybody is Using Cloud Mobile Apps

Big data: the recipe for the multibillion-dollar app industry

Going by the recent studies, the mobile app market has already hit the 365 billion US dollar mark, and it is expected to go up to over 935 billion US dollars by the year 2023. This is no surprise given the fact that most of the global users have completely transitioned from desktops to smartphones and tablets. In this situation, creating better mobile apps is clearly the next important step in the world of digital development.

The thing is computer apps are not as volatile as mobile apps. The mobile applications are much more inclined to be simplistic in their displays and features. Thus, it is of absolute importance that those specific but simple features catch the attention of the users.Big data analytics comes here as the finest way of collecting this kind of accurate and relevant information, which is slated to make big data just the ingredient you need for creating a billion-dollar mobile app UX.

Thus, it is clearly understood that the future of mobile apps goes hand-in-hand with big data. The analytical specialists come up with new and improved strategies for better interpretation of the massive amounts of unstructured data for discovering the important features of the next generation of applications. With that, more and more apps will end up being perfectly in sync with users’ expectations.

Summing up

Of course, one cannot know everything going on in the users’ minds, but one can know enough using the data that is available. It will not only help in improving the apps you have delivered already but also help you with the apps that are still in the process of being developed.

It is all about taking advantage of this huge storehouse of information in front of you and delivering the finest user experience. And, big data can be just the tool you need in this regard to propel you into that billion-dollar club.

How to Build Successful Mobile Apps using Big Data?

Going by the current reports, the number of users for smartphones is going to hit a whopping 3.8 billion by the year 2021. This sweeping increase in the number of smartphone users has also led to a rising demand for better mobile apps. Now, these modern apps also use tremendous amounts of data, and thus, a robust management tool for analyzing and managing this data has become a necessity. And, this is where the use of Big Data technology for building apps comes into the picture.

The fact that big data technology is nothing short of a boon for a Mobile App Development company is evident in the fact that the big shots of the game like Amazon, Starbucks, Uber, and more, have turned to it for successful app development. After all, the need of the hour is to get the right insights to know what the end-users want, and big data apps offer a huge help in that regard.

So, what are the ways through which big data helps in building successful mobile apps? Is it really worth the investment? Let’s find the answers to that today!

Delivering apps driven by customer preferences

First off, for any app to be successful, it has to understand the preferences of the customers and deliver the services exactly according to that. Big Data helps companies to understand the interests of the customers, and even track real-time information about their activities.

Understanding the demand and preferences helps the companies in getting ready for their future. They can begin planning in time and save themselves from future failures as they can meet the expectations of their valued customers.

It is also important to note in this regard that big data analytics helps businesses to plan in advance according to the demands of the customers. So, there is less and less scope of them ending up making any errors.

Besides, companies are able to come up with ways to improve their present apps, which enables them to retain the present users and draw in new users. This is great news for the revenue and the reputation of a company.

Read Also – How Artificial Intelligence is Driving Mobile App Personalization?

Using big data for end-to-end marketing

Big data is important to understand the present market trends and developing the right app ideas according to that. However, what most people do not know is how big data can come to their aid even after the formulation of that idea.

Mobile Big Data Analytics makes it easy for developers to store and extract open source codes. Streamlining the development process significantly reduces the time needed for coding, and thus, companies even save up on production costs.

The efficient marketing tools offered by big data analytics help in creating lists of target audiences and attaining considerable improvements in traffic. Predictive analytics as enabled by big data can help the companies in understanding long term use and success of their application, along with letting them take the necessary steps for boosting their product’s popularity.

Furthermore, there are services, such as data mitigation, to let companies access faster data transferring channels to connect data used between different apps.

Read Also – Reasons Why Everybody is Using Cloud Mobile Apps

The value of in-memory databases

At present, it is difficult to store data because it cannot be stored using RAM. The major tech-giants like IBM, Microsoft, and Oracle have come up with different in-memory databases, which are quite expensive.

Big data integration helps companies to store huge amounts of data easily in an organized way for long term usage. It is also helpful in making better use of the stored data. When data is stored in a secure and organized manner, it helps companies in multiple ways. It not only aids in better decision making but also comes to their aid when analyzing user choices and delivering the needful.

Effective analyzing of user experience

As discussed earlier, creating a successful mobile app involves analyzing the user experience critically. Big data can help in studying customer behavior to direct the attention to the core app functions. A thorough analysis of the user’s behavior lets the developers have the information they need to build an app. Thus, the user gets an app that lives up to their expectations.

Moreover, big data analytics let the developers determine the glitches that a customer can face, and update their apps accordingly to deliver the best possible user experience.

It takes nearly six months to create big data apps like Uber, but that is not where the role of big data ends. There’s customer usage, payment, banking information, location, and more to be analyzed to ensure increasing popularity and smooth functioning of the app. As big data studies user responses to the different app features, it enables the developers to come with more features for better user experience.

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Getting access to important real-time information

Customer preferences and needs are changing constantly, and, so are the trends. So, for the companies to survive in this highly competitive market, they need to be well-aware of these ever-changing trends. Accessing such real-time information becomes easier with the help of big data. It lets the companies make informed choices that improve both sales conversions and customer satisfaction.

Let’s get a better idea about this through the fitness tracking apps. These apps monitor the eating, sleeping, activity patterns, and the overall health conditions of the users. These apps need to have real-time information about the users to work effectively and help users have a healthy lifestyle.

The statics shown by the apps let the healthcare providers and doctors detect the health issues that a user might have. Furthermore, the use of accurate data also helps the advertisers to comply with the market trends in the middle of their campaign.

Role of big data in improving sales conversions

Mobile apps are made in a way that they help in increasing the company revenue. The role of big data extends far beyond local data collection. Thus, a company is able to establish its products to be effectively launched in different countries or states to widen the reach of their apps.

Further, by enabling the option of in-app purchases, you get to monetize the products. Successful attainment of this goal is only possible through studying the buying trends of your target audience. There are elements of big data like edge computing that lets companies study vital information close to its source.

It brings down the distance that the data has to travel, which in turn, reduces latency and the costs of transferring data. Moreover, big data lets the companies develop customized app settings depending on the needs of the users. Ultimately what it does is boost the performance of the application and encourage greater user engagement.

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Summing up

These days, mobile apps are like the core of the digital marketing strategies employed by businesses. And, improving user experience by taking into account all the aspects of app usage through big data technology lets a company stand out from the rest and be noticed by the target users.

Thus, developing mobile apps with the use of big data increases app visibility and draws in more users. Isn’t that what every company, jostling to make its name in this highly competitive era of online businesses, wants?