Data Analytics Vs Data Science – Everything You Need To Know!

Still confused between Data Analytics & Data Science?

Don’t Worry! this article will clear all your doubts & give you detailed view of the both terms.

What is Data Analytics?

As the word suggests the meaning of data analytics can be explained as the techniques to analyze data to enhance productivity and business gain. Data is extracted from various sources and is cleaned and categorized so that it can be analyzed and the user can identify the different behavioral patterns. The techniques and the tools used vary according to the organization or individual,Guest Posting the methods can be different subjective as well.

So, in short, if the user understands his/her Business Administration and can perform Exploratory Data Analysis, to gather the required information and analyzing it on a different level then he/she is good to go with certification course in Data Analytics for better future.

Importance of Data Analytics for Businesses

Even though there is an increasing importance of Data Analytics online training for business has changed the world in the real sense but an average person remains unaware of the impact of data analytics in the business. 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 the business mustn’t be only related to the individuals external to the organization. Most of the data collected by the businesses are also analyzed internally for better functionality. Along with the advancements in technology, it has been very convenient to collect data. The collected data would also help to know the performance of the employees and also the business at the same time.

2. Market Understanding

Moving onto the development of algorithm these days. The huge datasets can be collated and analyzed. The process of analyzing the data is also called Mining. For explanation, we can state that other kinds of physical resources, data collection is done in raw form and thereafter refined for potential results. The process would enable the collection of data from a wide perspective and different sources/people, which further proves out to be fruitful for better marketing strategy since the information is available from different perspective and resources.

3. Cost Reduction

Big data technologies like cloud-based analytics and Hadoop bring cost-effective procedure, especially if it relates to the storage of large data. They can also identify efficient ways to do business. The client/user would not only save money in terms of infrastructure but too, save on the cost of developing a product which would have a perfect market-fit since the cost plays a very important role.

4. Faster and Better Decision-Making

The high-speed in-memory analytics and Hadoop in combination with the ability for analyzing the new data sources, businesses can analyze the information almost instantly. it comes out to be a big time-saver as the user can now deliver more efficiently and manage the deadlines with ease.

5. New Products/Services

With power associated with the field of Data Analytics, the needs and satisfaction of the customers are met in a better way. This might help the user/client to make sure that the product/service aligns with the values of the target audience and it helps them all in one.

6. Industry Knowledge

Industry knowledge can be also comprehended and it shows how a business should be run shortly. Also, it tells the user and the kind of economy is already available for business expansion purpose. This, not only opens new avenues for businesses to grow but along with that it also helps them to build a strong ecosystem around the brand.

7. Witnessing the Opportunities

Although the economy is changing and the businesses want to keep pace with new trends, then there is one more important thing that most of the organizations aim for is profit-making. Herewith the help Data Analytics offers refined sets of data that can help in observing the opportunities that can be availed.


Importance of Data Analytics is truly changing in the world. The segment of Data analytics has a variety in it. Therefore, data analytics is used for sports, the business field, or just the day-to-day activities of human life. Data analytics have also changed the way people used to act in different scenarios. It now, not plays a major role in business, but too, is used in developing artificial intelligence, track diseases, understand consumer behavior and mark the weaknesses of the opponent contenders in sports or politics, it means to name sector and data analytics is there. This is the new age of data and it has unlimited potential.

What is Data Science?

Without the expertise of professionals, who have turn cutting-edge technology into actionable insights, Big Data would stand for nothing. In these days, where more and more organizations are opening up their doors to big data and unlocking its power is increasing ever the value of a data scientist who knows how to tease actionable insights out of gigabytes of data.

It has now become a universal truth that for any new era of businesses are bottlenecked with a huge amount of data. It is now becoming clearer day by the day that there is enormous value in data processing and analysis and that is where the data scientist steps into the spotlight. Executives have also heard of how data science is an industry filled with potential. Along with that how data scientists are like modern-day superheroes, but most are still unaware of the value a data scientist holds in an organization. Let’s take a look at the benefits and features of data science.

Major features of Data Science

1. Empowering the management and other authority to make better decisions based on data.

Any experienced data scientist is more likely to be as a trusted advisor and strategic partner to the organization’s upper management by ensuring that the staff maximizes their analytics’ capabilities. The organization must take up the best option for the organization. A data scientist also communicates and demonstrates the value of the institution’s data, majorly to facilitate improved and better decision-making processes across the entire organization, through measuring, tracking, and recording performance metrics and other information.

2. Directing actions based on trends—which in turn help to define goals

A data scientist would also examine and explore the organization’s data, after which they recommend and prescribe certain actions that may or may not help to improve the institution’s performance, better engage customers, and ultimately increase profitability, which is the deadline.

3. Challenging the staff to adopt best practices and focus on issues that matter.

One of the major responsibilities of a data scientist is to make sure that the staff is familiar and well-versed with the organization’s analytics product. They would need to prepare the staff for success with the demonstration on how effective it would be for them to use the system to extract insights and drive action. Once the staff understands the product capabilities, their focus can shift to addressing key business challenges.

4. Identifying opportunities

During the interaction with the organization’s current analytics system, data scientists shall also question the existing processes and assumptions to develop additional methods and analytical algorithms. Their all-day long job requires them to continuously and constantly improve the value that is derived from the organization’s data. Since the main focus would be to enhance the data associated with the current scenarios.

5. Decision making with quantifiable, data-driven evidence.

With the arrival of data scientists, the process of gathering and analyzing from various channels has ruled out the need to take high stake risks. Data scientists also create models using existing data that simulate a variety of potential actions in this way. Any organization can learn about the path that will bring the best business outcomes.

6. Testing these decisions

Half of the battle involves the procedure of taking certain decisions and implementing those changes for the enhancement itself. What about the other half? It is very crucial to know how taking and making decisions would have affected the organization. This is where a data scientist comes in. The organization pays to have someone who shall measure the key metrics that are related to important changes and quantify their success.

7. Identification and refining of target audiences

From Google Analytics to customer surveys, most companies will have at least one lead source of customer data that is being collected. But it would not be taken into use if it isn’t used well. For example, let’s take an instance, to identify demographics, the data isn’t useful. The importance of data science is entirely based on the ability to take existing data, the data which is not necessarily useful on its own. And combining it with other data points to generate insights an organization can use to learn more about its customers and audience for better implementations and outcomes.

A data scientist can help with the identification of the key groups with precision, via thorough analysis of disparate sources of data. With this in-depth knowledge, organizations can tailor services and products to customer groups, and help profit margins flourish.

8. Recruiting the right talent for the organization

Going through resumes all day is a daily chore in a recruiter’s/HR’s life, but with a new era of analysis, there is change due to big data. With the amount of information available on talent—through social media, corporate databases, and job search websites—data science specialists will be able to work their way through all these data points to find the candidates who best fit the organization’s needs.

By mining, the vast amount of data that is already available, in-house processing for resumes and applications and even sophisticated data-driven aptitude tests and games—data science can help the recruitment team to enhance their speed and more accurate selections.


Data science will add more value to any business who utilizes their data well. Therefore, from statistics and insights across workflows and hiring new candidates, it will also help senior staff or the management to work on better-informed decisions. Data science is valuable to any company in any industry, it is not specified in one dimension at all.

What Is the Difference?

Although the term is used by many people and they use those terms interchangeably, data science and big data analytics are unique fields, with the major difference being in the scope and advantages. Data science is best described as an umbrella term for a bunch of fields used to mine large datasets and simplify them for better outcomes and results. Data analytics is a little more focused version of data science and sometimes it is considered as a part of the larger process. Analytics is completely devoted to realizing actionable insights that will soon be applied immediately based on existing queries or newly made queries at the same time.

Another, significant difference between the two fields is a question of exploration and explanation. Data science isn’t concerned with answering specific queries, instead, they parsing through massive datasets in sometimes unstructured ways to expose insights. Organizations must figure useful information from a sum of data. In this field of expertise, Data analysis works better when it is focused, having questions in mind that need answers majorly based on existing data. Data science will also produce or offer a broader insight that would concentrate on which questions should be asked, while big data analytics mainly emphasizes discovering answers to questions being asked.

More importantly, the major difference in data science is that it stays more concerned about asking questions rather than finding specific answers to them. The field majorly focused on establishing potential trends based on existing data and along with that it also realizing better ways to analyze and model data.

These two particular fields can be considered different sides of the same coin, and their functions are highly interconnected. Data science plays an important role in the foundation and parses big datasets to create initial observations, future trends, and potential insights that can be important. This information by itself is useful for some fields, especially in modelling, improving machine learning, and enhancing AI algorithms as it can improve how information is sorted and understood. However, data science asks even more important questions that people are unaware of before while providing little in the way of hard answers. By adding data analytics into the mix, the user can turn those unaware concepts or information into actionable insights with practical applications.

While thinking of these two disciplines, it’s very important to forget about viewing them as data science vs, data analytics. Instead, the user should see them as parts of a whole that are vital to understanding not just the allocated information whi

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How Can Businesses Best Leverage Data scrubbing?

The hype around data is hardly news. It is a critical part of a business and rightly so. It is the ultimate competitive differentiator and referred to as the new oil of the world that of course, can’t be used raw and needs to undergo various processes of refinement such as data scrubbing, #cleansing, and modification.

In this blog, you can learn How Can Businesses Best Leverage Data scrubbing?

The hype around data is hardly news. It is a critical part of a business and rightly so. It is the ultimate competitive differentiator and referred to as the new oil of the world that of course,Guest Posting can’t be used raw and needs to undergo various processes of refinement such as data scrubbing, cleansing, and modification.

Poor quality data remains the major challenge, and as per the Data Quality Market Survey, 2017 report by Gartner, poor data costs businesses approximately $15 million yearly. However, it is not a direct financial loss but several indirect and discrete impacts on business processes causing the dent on the business value such as loss in reputation. A higher-risk decision due to poor quality of data and missed opportunities- hitting the businesses where it hurts most. Poor data not only means financial impact but also results in an overall lack of brand value. It is the Achilles’ heel that makes even the massive organizations crumbling down to their knees.

Eighty-four percent of CEOs are concerned that the data they are basing their decisions on might not be at par. Pitney Bowes in “The Data Differentiator: How Improving Data Quality Improves Business” explains that a company might be basing its decisions on poor data due to the volume of legacy data, silos within departments and difficulty in acquiring buy-ins from the executives. However, we also have companies and innovators like Amazon and Airbnb that are solely operating on the sheer power of good data. Which lets them know about their customers, needs, preferences, and challenges they face.

It also means a data deluge due to the massive cloud, mobile, and IoT data. As the companies assess data management infrastructure and want to make the most of big data. It is the mess that the bad data is creating and the most exhausting part they are dealing with. While companies have to deal with CRM data decaying at a rate of more than 30 percent, little do they know that CRM cleaning and scrubbing can boost their sales, reputation, and revenues!

Interesting Read:

Why do businesses need data scrubbing?
Successful data-driven organizations have a competitive advantage over the messy ones. Poor data is the culprit that doesn’t let businesses reach up to their true potential and reduces them to a dog chasing its tail.

More than twenty percent of revenue is lost because of bad quality data.
Over forty percent of companies are dealing with messy data plaguing their systems across BI, marketing and CRM and eventually hampering their growth.
Only sixteen percent of businesses acknowledge that their data is accurate and secure.
When data isn’t clean and is used for Business Intelligence and analytics. It is like mixing Nitrogen and Hydrogen with ignitable liquids. It is just a ticking time bomb waiting to corrode and combust. A business process running on low-quality data puts an entire business at stake. It is a domino effect that results in poor insights and poor results. That can be fatal and irreversible for a business.

Neil Patel explains how every organization has created a mountain of data that is difficult to navigate through. In a rush to achieve this information overload, businesses forget to pay attention to the origin of data.

People change jobs, locations, e-mail IDs, phone numbers and job functions. When the information isn’t synced with the database, it results in bad data. Often businesses utilize a third-party database, which leads to duplication of incorrect data, flooding the system with wrong, outdated and incomplete data.

A lead generation company wastes 546 working hours of a sales rep every year due to bad data.

What is data scrubbing?
Data scrubbing is akin to a system cleanse, a detox that every business is in dire need of and should opt for. It is the most exhaustive and complex part of a business process- like a body system used to junk food resisting to the greens and liquid diet. But it is also very important.

Often confused with data cleaning, it is a process of eliminating incomplete, incorrect, inaccurate, out-of-date, duplicate and inconsistent data.

To keep your data spotless- the backbone of your business- implement Data scrubbing. It ensures stronger campaigns, increased quality leads and competitive advantage for a business along with stronger and more targeted marketing campaigns, enhanced customer satisfaction and higher ROI.

Data scrubbing services include
Data scrubbing services are semi-automated, automated and manual. The data can be customized following a business’ specific needs and challenges. The expert and seasoned data engineers work through the vast sets of data in the following phases:

Rectification of records:
An up-to-date customer database is the key priority of data scrubbing. Data scrubbing implement a range of tools, techniques, and methods to identify incomplete and inaccurate customer records and take appropriate action. It ensures proper communication with a targeted group of customers.

When businesses take services of different databases, chances of having similar information run higher. When a sales team sends information, e-mail or unsolicited content. The customers just hit ‘spam’ without giving it a second thought. Data scrubbing utilizes the verification and validation techniques to remove duplicate data. De-duplication ensures that only relevant information makes it to the database, and the rest of it is flagged out.

Data Appending
When the engineers find out key datasets to be missing, they verify and check the current information and complete the missing record. Digital footprints and offline attributes such as phone numbers, jobs, and locations are used.

Standardization of data
Several business processes use data in a different format. Some may use a standard title, or some may not. Some may want to categorize the data for specific parameters, whereas, for others, it could be a completely irrelevant criterion. Data scrubbing ensures a uniform pattern of the datasets across the business verticals for easier access and consistency. The entries are similar across the datasets.

Enhancement of data
The data is validated and verified for the information it contains. If needed, the engineers can also assign value to the datasets that can be used to generate insights. The data can be formatted in any format such as Excel, CSV, PDF or XML.

Difference between Data Scrubbing and Data Cleansing
The terms are used interchangeably. However, if you dive deeper into the technicalities:

Data scrubbing is an evolved and more technical process encompassing the merging, translating and filtering out the inconsistencies in data.

Data cleansing is referred to as the identification and elimination of inaccurate and incomplete records in a data set. It also includes the process of deleting and updating the ‘bad’ part of it.

…And that’s about it. Data cleansing and scrubbing go hand-in-hand. This is why it is better to ignore the minute technicalities and tackle the bad data, which is crippling your business.

Interesting Read:

Best Data Scrubbing Practices for Businesses
Data scrubbing is complex, but it is an indispensable process. Properly scrubbed data ensures value-driven insights that can drive a business towards its unprecedented growth.

A 2017 report by Forrester established that a Fortune 1000 company could add more than $65 million in revenue to its annual net income with a ten percent increase in data quality! Who knew bringing in a whopping million is ‘this’ easy and doable!

It is ideal for taking data scrubbing as a journey, a continuous process to keep cleaning it up for the greater good. Good data has always been an elusive goal and organizations need to strive constantly for that. However, it can be managed and monitored regularly to ensure the database in their system remains accurate, consistent, complete and valid. If you want to know how to best leverage data scrubbing, read on!

Data should be standardized.
Data should be standardized and categorized at each entry point. Whether a salutation needs to be followed, the serial numbers, postal codes, filters, location, job title, etc. –each parameter needs to be followed thoroughly. A uniform pattern of data across the vertical helps in setting a process that eventually leads to the sanitation of data. Log all changes, take the backup of both raw and scrubbed data as you make changes and label it.

Align your approach
Believe the experts when they say less is more! You don’t need junk data; you need crucial, concise and domain-specific data that can be useful for your business! Uber’s disruptive God-View Mode isn’t about big data. It is about how smartly they have put their data to use. If your data is accurate, complete and valid, the small data well, maybe, even bigger than the big data! It is time to let go! Don’t sit on the vast dead and obsolete data and instead, start asking the hard-hitting questions:

Do you have a data quality plan?
A data quality plan ensures the integrity and accuracy of data by analyzing and finding the root cause of the error. A plan also includes assigning the metrics and setting a contact point in every department to regulate and monitor the quality of data. You should be able to use and read each detail of the data to generate insights out of it. Unless you can do it, there is a scope of improvement.

Tools and Metrics for data accuracy
Does your business implement proper tools and techniques to measure the accuracy of data? If not, it is time to invest in data solutions right now. Alternatively, you can also outsource CRM cleaning and cleansing to a data solution company.

Training and Education.
Change is inevitable, and so does resistance towards it. Rather than pushing your ground and on-field staff to maintain data quality, it is better to educate and train them about the benefits of good-quality data. It is recommended to assign a contact point to help them with any query and question as well as to monitor the quality of data at each entry point.

Your need is specific.
What works for others may not work for you. It is better to assess and analyze why you need to harness the power of data. Hire a cleaning and scrubbing company to discover business-specific insights and data solutions. Knowing the goals and objectives that you want to attain via data will help to figure out the customizations, changes, and enhancement.

Validate and Verify.
Data cleaning is just an aspect of good-quality data. Scrubbed data should be enhanced with correct information and verified for accuracy. A quality review

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