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Data Analytics vs Data Science

Data Analytics Vs Data Science – Company data is an accumulation of a company’s growth in numbers in different fields.

With the ever increasing competition the quantity of this data has increased exponentially over the past few decades.

In addition to the rapid explosion of data, a structured system for handling big data has become a field with untold possibilities & growth.

So, in order to handle big data effectively & efficiently the necessity for having a system has given birth to 2 sub categories of data management :-

  1. Data Science
  2. Data analytics

What is Data Analytics?

Data Analytics is a branch of data science which deals in techniques of analyzing data to enhance business efficiency & gain.

Data analytics deals with analysis of data being extracted from various sources and is sorted and categorized so that a pattern for results pertaining to decisions which were made or are to be made can be recognized.

The techniques and the tools used for data analytics may vary according to the organization or individual & the methods can be different and subjective as well.


Importance of Data Analytics vs Data Science

With Data Analytics becoming the new norms for business, it has transformed the world of data. But for an average person the impact of data analytics in business is still something new & one should opt data analytics course for better overview. Hence to enlighten the field of analytics to an average person, here are some of the ways this has impacted the business include the following:

1. Improving Efficiency

The data collected by firms can not only be used to counter external factors affecting an organization, but also the productivity & efficiency of the staff working towards the common goal growth.

A lot of data collected by the data department is also analyzed by for better functionality of internal operations.

With advancements in technology it has become convenient to collect data & use it to understand a pattern and plan strategies useful to maximize employee & business performance at the same time.

2. Authentic data mining

With the development of complex algorithms huge datasets can be examined and analyzed more effectively & efficiently than ever before.

The process of extracting specific data from a pool of datasets is referred to as Data Mining.

For comparison purposes it can be stated that compared to the old school method of data collection via physical surveys from multiple sources and compilation & study of this data which used to take days, data analytics does all of that whilst providing refined results for potential strategy making processes in a matter of hours. 

This also proves fruitful as the data collected is directly from the source without any chances of it being tampered with & helps the management of the firm gain an understanding of the market & what the public really wants.

3. Cost Reduction

Big data technologies often resort to cloud-based analytics which tends to be a highly cost-effective procedure.

Especially when it relates to the storage of large amounts of data on cloud servers which aids to cost effective ways of doing business.

The client/user not only saves money in terms of infrastructure but also on the cost of developing a records management product/procedure which helps in reducing cost.

4. Faster and Better Decision-Making

Integrated complex algorithms of data analytic tools have unmatched computing powers of computing & filtering detailed queries from a vast pool of data in combination with the ability of analyzing new data sources.

Businesses with the help of data analytic tools can analyze pools of information almost instantly.

It is less time consuming and more efficient in providing a better clarity of the strategies which were executed or the planning and execution of new strategies enabling management of deadlines with ease.

5. New Products/Services

With the computing power associated with the field of data analytics vs data science.

The ever changing needs and satisfaction of the customers are made clear and met in a more detailed and efficient manner.

This helps the user/business to make sure that the product/service aligns with the requirements of the target audience and helps capture the market.

6. Market awareness

Data analytics helps in gaining awareness of the market and can also be used to comprehend and carve a path to help with the smooth running of a business.

It helps the business to understand the market & whether the economy is available for business expansion purposes.

This not only opens new avenues for businesses to grow but also helps them to build a strong ecosystem around the brand.

7. Witnessing the Opportunities

Although the economy is ever changing, a successful venture always wishes to keep up with the changing trends.

Then there is also the aim for profit-making, analytic tools aid the venture in charting out a detailed analysis of the scope of enhancing the company profit by adapting to the changes of the ever changing economy all while keeping the costs in check.

Data Analytics offers refined sets of data that can help in observing & pursuing the opportunities that come with the ever evolving economy.

Types of Data Analytics

When building strategies for something as vast as data analytics, including solutions across different situations becomes a  necessity. These solutions can be categorized into three sub categories : –

1. Descriptive Analytics

Descriptive analytics are what firms generally use as they access past data and try to extract information pertaining to the most important trends, occurrences, and areas of improvement.

Doing so allows companies to uncover not only what has happened, but also look into the factor(s) that may have had a direct/indirect influence on the situation.

Also, discover how it may have impacted another metric down in the long run. Whether looking at a bigger picture or diving into one aspect of a data set, the analytics for these solutions includes building a path to effective statistical operations that can be used by the businesses once the required data has been accumulated.

Across all departments, Descriptive analytics are used for communicating with the workforce as to what has happened and how it relates to the success of the business as a whole.

From operations and financials to human resources and management and numerous other operational departments that vary from company to company, employees can benefit from summarizing their raw data into meaningful and applicable measures.

The more in tune a business is with its past data, the more effective they can be in adapting to their future strategies for data optimization.


2. Predictive Analytics

As The name suggests predictive analytics does what the name suggests – it predicts. Using the insights provided by descriptive analytics it can charter a pathway for businesses toward efficient predictive analysis to understand the future course of action.

They take control of past trends and data distributions and utilize them to predict the outcome results and restructure strategies or set new ones.

Depending on the scale at which a company is making the predictions , future strategies can be decided to cater to short term or long term goals when entered into models and algorithms.

However, it is important to keep in mind that no analytics can ever give a 100% accurate prediction of what might happen in the future.

Predictive analytics can help paint a picture of what can be expected after a change or formulations of certain strategies and provide probabilities of case scenarios keeping in mind the variables that were looked at while deciding those strategies.

But, never an accurate depiction of what the future holds.

Firms can make use of predictive analytics to open topics of conversations to numerous potential possibilities and scenarios that current data may not be showcasing.

Instead of just forecasting a certain metric or trend, analysts can fill in data with predictive analytics to explore hypothetical situations with an extremely detailed predictive approach.

Some common paths taken when using predictive analytics guide queries are:

  • If a particular metric is changed in a certain way what might happen to x,y and z metric and which of these are correlated?
  • Why is the market experiencing certain results in a particular time frame – will this continue or is this a one time event?
  • If this happens, then what…?
  • How is this gonna turn out …?
  • If zero changes are made to the present pattern, then what?
  • Can this change auto correct the process, if yes then to what extent and in what way?

3. Prescriptive Analytics

Prescriptive analytics stretches beyond the past insights of descriptive analytics and the possible future outcomes of predictive analytics to provide suggestions for the next course of action that need to be taken.

Firms can analyze and decide upon multiple case scenarios depending on the outcomes of the result from simulation of various potential case scenarios.

It takes the forecasts and likelihoods from predictive analytics one step further by creating advised solutions that will align with the goals of the company’s key metrics.

Every business wishes to be efficient and prescriptive analytics helps create the opportunity to optimize resources and have an upper hand in effective decision making before they have to be made.

It provides further insight as to why certain situations may arise so related factors can be monitored when new actions are taken.

Although prescriptive analytics is also a form of predicting future situations, it still utilizes quantitative tools to make sense of the data that already exists and the real-time data being collected – such as algorithms, statistics, and machine learning.

The data can span beyond organization’s own sources and include external data that may have a direct influence in a metric being assessed and impact the business at a departmental or organizational level.

What is Data Science?

In today’s adaptive & competitive world, many organizations are generating terabytes of data every single day .

It has become imperative in today’s time to be able to manage these humongous amounts of data with ease & efficiency and here is where Data Science steps in.

People who are connoisseurs of data science are often referred to as Data Scientists.


Major features of Data Science vs Data Analytics

1. Empowering

Data science empowers the management and other authorities to plan & execute decisions based on data collected.

A seasoned data scientist is most likely to be the most trusted advisor and strategic partner to the higher management in an organization.

The management must take up the best option for the organization which is communicated and demonstrated by the assimilation of the institution’s data to facilitate improved and better decision-making processes across the entire organization.

2. Redefining global trends

A data scientist assists the organization in redefining global trends by comparing the company’s data with the competitors to tackle or reinvent current market trends.

Setting up of innovative trends helps firms in earning goodwill in the emarket and eventually drive sales, growth & revenue.

3. Demanding excellence

Every organization is hard wired to demand excellence from it’s CEO to the staff working directly or indirectly for the firm.

Excellence is a derivative of perfection and just like the staff, data science also deals with the similar excellence that is needed to plan out a course of action for the firm’s future strategies.

4. Identifying opportunities

With data science dealing with tons of information & processing heaps of data to numbers which can be used to interpret  a whole bunch of information.

Data Analytics vs Data science professionals are shown a window of identifying opportunities to either conclude the end of a decision taken in the past or to look for another insight of dealing with the situation or to change the approach altogether.

During the interaction with the organization’s analytics system, data scientists can also question the existing processes which are in place and initiate ideas to develop additional methods and analytical algorithms.

5. Charter a business approach

Data science is part of a very big ecosystem and is responsible to find out ways of making the business more profitable and productive by the means of gathering sensitive data from various sources including the competition.

Data science helps create a model for carving out multiple approach paths for a business that it might want to follow.

With data science, the process of gathering and analysing data from various channels has ruled out the need to take unnecessary high stake risks to understand the implications of hypothetical decisions being taken to drive profitability.

Data science also allows it’s handlers to create models using existing data that simulate a variety of potential paths of actions which helps organizations learn about the path that will help bring the best business outcomes.

6. Probing decisions

The key to an effective data science module involves the process of taking vital business related decisions and implementing those changes.

The second half is investigating the results generated because of those actions.

It is very crucial to understand how taking and making decisions would affect the organization. This is where data science comes in handy to understand this, by studying the results from the data being generated.

A well reputed firm requires a professional who shall measure the key metrics that are related to the important changes and justify the decisions made or those that will be made in the near future.

7. Identification and refining of target audiences

The importance of data science is based entirely on its ability to compute existing data, the data which is not necessarily useful on its own but merging it with other data points to help generate new insights.

An organization can use it to learn more about its customers and audience for better implementations of decisions and predict outcomes.

Data science helps with the identification of the key audience with precision, via thorough analysis of different sources of data.

This specialization aids organizations to create tailored services and products to cater to the consumer needs and help in maximizing profit margins.

8. Acquisition of skilled professionals

Going through a number resumes is a part of a recruiter’s/HR’s job profile.

Data science specialists are able to work their way through multiple data points to find the right candidates for the organizational needs.

Skillful handling of data science helps in extracting these mass volumes of data that is readily available.

In-house handling of portfolio’s, applications and even complex data-driven aptitude tests could help the recruitment team to speed up their hiring processes with highly accurate selections.

Data Analytics vs Data Science Conclusion

 Data ScienceData Analytics
SKILLSET-Data modelling
-Predictive analytics
-Advanced statistics
-BI tools
-Intermediate statistics
-Solid programming skills
-Regular Expression(SQL)
EXPLORATION-Search engine exploration
-Machine Learning
-Artificial Intelligence
-Big data - often unstructured
-Data visualization techniques
-Designing principles
-Big data - mostly structured
GOALSDiscover new queries to drive innovationUse existing information to uncover actionable data

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