Difference Between Big Data, Data Science and Data Analytics

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Data Analytics for business

Data Analytics or DA involves the process of extracting from raw data. A Data Analyst examines a set of data with the help of specialised tools and draws valuable information from the data. Data Analytics aids various commercial industries, scientists and researchers to make more informed decisions. Researchers & scientists mostly use them to verify or disprove scientific models, hypotheses, while businesses use them for making vital business decisions.

Termed as the “Oil of the Digital Era” by Economists, “DATA” is now one of the most valuable resources in the world. While crude oils are renewable, the available data on the other hand is an upsurge. Such a rapid flow of data from everywhere, leaders from all sectors will soon realise the power of big data.

Data Analytics in Business decisions

While all businesses want to use data for responsible business decisions, it’s crucial to know how data can change the landscape of companies.

1. Data Visualisation

An Analyst has to work with an overwhelming volume of data which often keeps piling up rapidly. However the human brain process much better with graphical representations than columns and rows of numbers.

This is why it is essential for every analyst to convert numerical data into exciting visualisations using appropriate tools. Data Visualisations helps anyone in an organisation to learn different concepts with ease. With proper data visualisation, one does not have to do any complex analysis to identify new patterns within the data.

Unfortunately, most businesses treat data visualisations as a luxury rather than putting it in a necessity category. What they do not understand is by doing so they are leaving out valuable insights and the real potential of the data never really is realised. Data should not only be in the hands of the analysts, but it should be available for analysis from all primary business functions.

Businesses can enjoy several visualisation tools available online which not only offer easy UI but also present the data in an effective storytelling way.

2. Data diversity

Data Analytics often deal with a diverse range of data. More data often symbolises more insight, provided how an analyst use them correctly. Obtaining data requires a lot of hard work especially while working on data diversity. Securing data diversity involves advance skills of researchers who must know how to tackle diverse sources, restructure unstructured data, and reassemble varied data sets. Due to lack of skills, many businesses face challenges while handling data diversity.

Storing in data is not even an issue. Various sources like a data warehouse, cloud storage or even a desktop seem fine to store data. However, to safeguard oneself from data loss, its ideal to store (structured or unstructured) data in a premium cloud space. Data analytics helps in combining various data to optimise business. For example, a company can combine sales, social and weather data altogether to decide their advertising budget in marketing.

In data analytics, analysts use data blending tools to bridge the gap between different sources. Anyone new to data analytics needs to learn how to build baseline data for a successful analysis.

3. Agile analytics

In traditional business intelligence projects, the analysts used to take years to come up to a solution. With today’s dynamic market scenario, a business can’t afford such long time to come up to a decision. With constant changing market space, one need agile analytics that change the approach of business intelligence and reduce the time to value. With the emergence of advanced tools in the market, one doesn’t have to wait for a month for productive business insights. Now business insights are achievable within weeks compared to old times, which took months.

Data Analytics helps in collaboration with productive factors and creating exceptional solutions. Businesses can combine raw data, the expertise of analysts, people with domain knowledge and a set of hypotheses to come up with advanced business insights. Analytics provides quicker delivery of results from data, which helps a business to decide whether to take a long-term or an ad hoc strategy.

4. Self-service analytics

Most of the companies believe that working with data requires the skills of advanced programmers, but the landscape has changed significantly with the emergence of new technologies. The barriers between advance programmers and standard analysts are becoming lower as more User-friendly Interface are surfacing.

With advanced analytics tools, anyone can become data scientists without getting in-depth of it. With proper utilisation of advanced analytics tools and providing necessary training for data analysts, a business can quickly reap the benefits of data analytics. A company can hire professional trainers from a reputed organisation who can provide basic training skills to the employees.

Data Analytics may be a self-service facility for businesses, but self-service can never replace self-sufficiency. A company always requires a robust IT infrastructure to execute the plans into actions.

5. Advanced analytics

Advanced analytics involves a wide range of inquiry, leading to significant changes and improvements in business practices. The most significant difference between traditional analytics and advanced analytics is their approach. Conventional analytics examines historical data while advanced analytics tools forecast future events and behaviours. Such forecast assists a business in implementing what-if analysis and predicting the outcome of a potential change in business strategies.

Advance analytics covers all analytical categories like predictive analysis, data mining, big data analytics and machine learning. All prosperous industries like Marketing, Risk management, Economics and healthcare uses advanced analytics, Businesses often use analytics to gather information about the past trends.

What most companies often forget is that they can use data for future predictions too. For example, a marketing team can use advanced analytics to predict the behaviour pattern of web users and can predict which user will click on a link. Healthcare providers can take the help of prescriptive analytics to figure out the likelihood of a patient’s recovery when exposed to a specific treatment. Advanced analytics also helps network providers in predicting network failures, enabling them to take preventive measures.

Interesting Read: TOP 10 CRM TOOLS FOR SAAS AND TECH STARTUPS

BIG DATA Analytics for business

big data vs data science

Big Data comprises of large chunks of raw data collected, stored and analysed through different means. Big organisations use these data to increase their productivity and making better decisions. Big Data comes both in structured and unstructured form. While structured data are more comfortable to analyse, unstructured data needs more hard work to work on. Structured data are organised and stored in a database, while unstructured data consists of several types of formats. Besides, traditional data models and the process does not work during Interpretation in unstructured data.

Big Data provides several benefits to organisations who want to enhance their business through a more analytical approach, and have a competitive advantage in serving their customers better compared to other companies.

Interesting Read: 7 BIG DATA EXAMPLES WITH ANALYTICS & APPLICATIONS IN REAL LIFE 2019

Big Data features three prime factors

1 .High Volume

Big Data comprises a large volume of data collected from a varied range of sources. Sources include business transactions, social media and information from machine-to-machine or sensor data. Big data gathers data from these sources through observing and tracking down their past trends.

2. High Velocity

Big Data works faster compared to other data as the information comes at a rapid speed and need to deal on time. Analysing the streamed data on a real-time basis, the analysts provide valuable analysis right on time.

3. High Variety

Big Data consists of numerous formats and varieties. They can be structured and come with a statistical database, or they can also come unstructured in the form of text documents, audio, video, stock ticker data and email.

How Data Analysts Create a Successful Big Data Strategy?

To harvest the actual benefits of Big Data, a company must focus on successful utilisation of the collected data rather than concentrating on raising a tremendous load of data. To reveal the real potential of Big Data a company must implement unique strategies. A company has many options to collect data and analyse them.

Successful implementation of Big Data results to

1. Cost Savings:

Some productive tools like Hadoop and Cloud-based analytics works wonder for companies who deal with Big Data. These tools help in identifying methods that are more effective to conduct business and can store a large amount of data. Such factors help a company to save a significant amount of money.

2. Faster Delivery:

Utilising the in-memory analytics business can help in solving complex and time-sensitive business scenarios within a small time-frame. These tools assist a company in identifying new sources of data and make faster decisions.

3. Developing and modifying products:

Big Data reveals the current trend of the customers and what it takes to satisfies them. Based on the newfound data, businesses can create new products or modify existing products according to the need of their targeted customers.

Understanding the market scenario: Understanding the current marketing conditions by utilising Big Data is an easy task. Analysts can inspect the purchasing behaviours of the customers of a particular company and figure out the most purchased products. With such insights, the company can allocate more budget to produce the products to cater to more customers.

Manage or enhance online reputation: The sentiment analysis feature of Big Data Tools help in analysing the feedback from the customers about a specific company. Now, a company can know what its customers think about its products and services. Effective monitoring and improving the online presence of the business can help a business to stay ahead of its competitors.

Most companies use the analysed data to compete in the market, innovate new products for customers and capture values. The usage of Big Data has grown exponentially, and it will keep growing in the future too. Utilising the power of Big Data, Companies can enhance themselves to outperform their competitors and established a stellar presence in the market.

Big Data helps the organisations to create new growth opportunities and entirely new categories of companies that can combine and analyse industry data. These companies have ample information about the products and services, buyers and suppliers, consumer preferences that can be captured and interpreted.

It also understands and optimises business processes. Retailers can quickly streamline their stock based on predictive models generated from the social media data, web search trends and weather forecasts.

DATA SCIENCE for business

big data vs data science

Data Science involves in making valuable decisions and predictions using predictive causal analytics, machine language and machine learning.

What is Predictive causal Analytics?

Predictive Causal analytics is a method applying which a business can predict the futuristic outcome of a particular event. For example, companies who provide money on credit often face issues during credit payments from customers. Using predictive causal Analytics, businesses can calculate the probability of customers paying their loan on time by going through their payment history.

What is Prescriptive Analytics?

Prescriptive analytics is quite advanced in all manners. It can create a business model, which can make its own decisions and can modify itself as per the dynamic trends of the market. While this is new in the field, but it offers quite excellent productive advice for businesses. Apart from predicting the future, it also suggests a wide range of prescribed actions and possible outcomes of those actions.

Google’s self-driving car is the perfect example of Prescriptive analytics. Each self-driving car gathers some data and using the same data on the vehicles; Google can improve the productivity of the Cars. Analysts run various algorithms on the collected data and convert them into intelligence. By implementing that intelligence, Google can improve the accuracy of the cars. The cars may make decisions such as when to turn when to speed-up or slow-down and which path is more convenient.

What is machine learning?

Machine Learning offers a broad scope of data analytics. It includes:

1. Making Predictions: 

Machine learning algorithms determine the future trend of a company in the best possible way. If a financial company wants to build a model to find out future trends all it needs is the transactional data of the company. Data Scientists use data those data and train the machines on it. For example, using a historical record of fraudulent purchases, a company can build a fraud detection model.

2. Discovering Patterns:

To make an accurate prediction, a company need to have sufficient parameters, but the unavailability of parameters limits the accuracy of forecasts. In such case, a data scientists or analysts have to use his/her skills to find the hidden patterns within the provided dataset and come out with a prediction. Unavailability of a predefined grouping of data may be an issue but using the clustering algorithm, this issue can quickly be resolved.

For example, a telephone company that wants to put towers in a region for establishing its network can use a clustering technique to find the tower locations. The cluster technique read the patterns of the dataset and predict which region is ideal to establish a tower so that customers will receive optimum signal strength.

How Data Scientists are different from Data Analysts:

  1. A data scientist uses their skills to predict the future using past patterns while the work of data analysts is to find meaningful information from the provided data.
  2. A data scientist analyses the data and raise questions while data analyst finds the answer of all the various issues arising in the mind of businesspersons. In short, a data scientist is more about what if, on the other hand, a data analyst involves in the day-to-day analysis.
  3. The work of data scientist is not only to address business problems but also to provide accurate predictions about the business. However, data analysts only address business issues, but the rest lies in the hand of the administration.
  4. To extract information from a data, a data scientist uses machine learning while data analyst uses R/SAS tools.
  5. Data Scientists combine different sources and establish a link between them. Primarily data scientists use diverse sources, explore and examine them. However, data analysts use only data from a single reference to investigate and examines.
  6. The accuracy rate of data scientists is as high as 90 %. Whereas, data analysts work is to work on the questions provided to them by the management.
  7. Data Scientists formulate questions whose answers will prove beneficial for the businesses. Data analysts on the other hander only solvers a set of questions and hand it to the authorities.

Role and Responsibilities of Data Scientists

A data scientist starts their work through data cleansing and processing it. Through accurate predictions of business problems, a data scientist offers future results of a business. A data scientists must have skills for developing machine learning models and analytical methods. Apart from that, they must create new questions keeping in mind about the advantage of the business. They must also learn excellent skills in using state-of-the-art methods to harvest data mining. Data Analyst’s work is to present the results in a precise manner along with working on the ad-hoc analysis.

Role and Responsibilities of Data Analyst

The first thing a data analyst has to do is identifying errors and checking the credibility of the acquired data. With successful mapping and tracing data, they helps in solving problems arising in business. Analytics has to establish excellent coordination with engineers so that to acquire new data. An ideal data analyst performs statistical analysis on given data to develop meaningful output from them.

Data science is a blend of skills in three major areas

1. Mathematics Expertise

The work of data scientists is to gain quantitative skills. Data scientists mines insightful data and build data products by utilising their quantitative skills. Data contains textures, correlations and dimensions that only a person with sound mathematical skills can only realise. Application of heuristics and quantitative techniques to find solutions becomes a puzzle for data scientists. While building a successful analytic model for a business, it requires the underlying mechanics of high-end mathematical applications.

A popular misconception in data science is about the utilisation of statistics. Most believe that statistics is the only skill required during data handling, but the truth seems different. In reality, there are two types of statistics, classical and Bayesian. What often people think statistics is just traditional statistics, but it’s crucial for a data scientist to know about Bayesian statistics as well.

2. Technology and Hacking

In data science, hacking doesn’t imply breaking into someone’s computer but means utilising creativity and ingenuity to build things & find intelligent solutions to problems. Data Scientists need to hold superior knowledge about how to handle significant data and solve complex algorithms. To do that, one needs tools far more advanced than Excel. Data Scientists must have adequate knowledge of coding languages like SQL, R, Python and SAS. Other words one must know includes java, Julia, Scala etc. To make the code work, a data scientist must have exceptional skills to redirect their way and cut through technical challenges.

One of the most common technical challenges while procuring data is geographical barriers and unnecessary government rules when it comes to access websites from different countries. Using a reliable proxy service like limeproxies.com can solve the problem within a matter of seconds.

A data scientist should know how to connect all the pieces to come up with a productive result. A data scientist must be hardcore algorithm thinker and have the knowledge to tackle critical problems and reintegrate them to solve the issues.

3. Strong Business Acumen

A data scientist also has to act as a tactical business consultant. Solving core business problems does require not only technical and mathematical skills but also strong business oriented skills as well. Data scientist have the responsibility to see through the data and translate them into something which helps towards the growth of the company along with solving various problems. They should come with such a narrative solution and prediction that it will provide valuable insights into the business and guide others.
Data scientists should stand in proper alignment with data science projects and business goals. Data and data scientists are codependent with each other. A vast number of credible data won’t be able to help a business if there are no good data scientists to reap the value of the data by utilising math, data and technology.

About the author

Rachael Chapman

A Complete gamer and a Tech Geek. Brings out all her thoughts and love in writing blogs on IOT, software, technology etc

By Rachael Chapman

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