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Tag: Data Analytics Course

The Next Big Thing in Data Analytics

Posted on by Imarticus Learning

 

Data analytics is fast evolving, and with the increasing use of streaming data, machine data and big data only adds to the continuous challenges encountered during analyzing log data, enterprise application data, web information, historical data stored in documents and reports etc.

In the present day, data analyst struggle to provide a solution for business and client request. As it is, there is a substantial deficient of talent in the field of business data analysts and data scientist, with businesses continue to struggle with data reconciliation, data blending, data access, development of data analytics tools and data mining techniques.

Data analyst and data scientist are frequently unable to discover data and information required and are often unaware of the latest data analytics tools such as the self-service data prep tools assist in the improvement of productivity. Furthermore, the continuous development of advanced social technologies and with the incorporation of various social features have caused an increased expectation regarding timeliness and information availability. Similarly, users have similar enhanced expectations towards business information irrespective of where the data originates or how is it formatted. There is an increasing demand for instant access for data and the ease of sharing it with essential stakeholders.

 

Data socialization is the metamorphosis of data mining techniques to enhance data accessibility across companies, teams, and individuals. Data socialization is changing how business think about business data and how employees interface with business data.

Data socialization comprise of management of data platform which enables the linkage between self-service visual data preparation, automation, cataloging, data discovery and governance features with essential features common to a various social media platform. Hereby, it provides businesses with the ability to leverage social media metrics such as user ratings, discussions, recommendations, comments etc. to enable usage of data for improved decision making.

What is Data Socialisation?

It is a data analytic tool which enables business analyst, data scientist and various relevant users throughout an organization to search, reuse, and share managed data. It aids in the achievement of agility and enterprise collaboration. Data socialization allows employees to find and utilize data which is accessible to them within a specified data ecosystem and assist in the creation of a social network of raw data sets which are curated and certified. These data ecosystems have various levels of controls, restrictions, and limitations which can be well defined for each individual person in an organization. These data mining techniques aid the strengthening an environment of data access, wherein analyst and users are allowed to learn from one another, enhance productivity and be well-connected as its sources, cleans and prepares of data analytics.

Some Characteristics of Data Socialisation

Some of the critical characteristics of data socialization include:

  • The ability of understanding data with regards to its relevance about how a particular data is deemed to be used by various users within an enterprise.
  • Involvement of collaboration of essential users with the data set to harness knowledge which often remains unshared.
  • It enables enterprise users to search for data which has been cataloged, prepare data models, and index metadata by users, type, application, and various unique parameters.
  • Data Socialisation enables to perform a data quality score, suggest for relevant data sources, automatically recommend actions for preparing actions designed according to user persona.

With various business applications incorporating features of social media functions towards improvement in business collaboration, at this moment making individuals and companies well informed, productive and agile.

Data socialization aids in delivering various benefits to various data analytics tools and removal of obstacles towards accessing and sharing data, at this moment allowing data scientist, business users and business information analyst in improving their productivity and decision-making. It further empowers analyst, data scientist and other business users across various departments to collaborate using the available data. By providing the right person with the correct data required to make informed, educated and timely decisions, the implementation of Data socialization is deemed to be the next big thing in data analytics.

Join Big Data Analytics Course from Imarticus Learning to start your career in data analytics. 

15 Terms Everyone in the R Programming Industry Should Know

Posted on by Imarticus Learning

 
Of late, the R language has gained popularity in the technology circles. R language is counted among the open source program, which is maintained by R –Core development team. This team comprises of developers all across the world who work voluntarily.
This language is used to carry out many statistical operations, while it is a common line driven program. It was developed by John Chambers and his team at Bell Labs in the US for implementing S programming language. There are several benefits of using this language, which give people from different industries a reason to adopt it. It is among the best machine learning and data analysis language. 
People making a career in the domain of data analytics course can find good R programming opportunities. If you are new in this field and want to learn and master, have a look at the list of 15 Terms everyone in the R Programming Industry Should Know, have a look:
1). Mean in R – The mean in R is the average of the total numbers, which are calculated with the central value of a set of numbers. For calculating this number, you simply have to add all the numbers together and then divide by the available numbers found there.
2). The compiler in R– It is something that helps in transforming the computer code, which is written in one programming language (to be precise the source language) into the other compiler language, which is the target language.
3). Median in R – It is a center of the sorted out list of numbers, however, if the numbers of even, things are different to some extent. In the case of the R language, first, you have to find out the middle pair of numbers followed by finding out the value of the midway number. The numbers are added and then divided by two to get the same.
4). Variance in R – It is basically the average of squared difference that is found from the Mean.
5). A polynomial in R – If you break this terminology you will get the meaning. Poly is many and nominal is a term, which means many terms.
6). Element Recycling – The vectors of diverse lengths when coming together in any operation then shorter vector elements are reused for completing the operation. This is known as element recycling.
7). Factor variable – These are categorical variables, which hold the string or numeric values. These are used in different kinds of graphics and particularly for statistical modeling wherein numerous degrees of freedom is allocated.
8). Data frame in R – These have diverse inputs in the form of integers, characters, etc.
9). The matrix in R – These have homogenous data types that are stored including similar kinds of integers and characters.
10). Function in R – Most of the functions in this language are the functions of functions. The objects in function fall under the local to a function, while these are returned to any kind of data type.
11). Attribute function in R – This function has an attribute of carrying out two different functions together. These include both the object and the attribute’s name.
12). The length function in R – This is the function that helps in getting or setting the right length of the vector/object.
13). Data Structure in R – It is a special kind of format that helps in storing and organizing data. These include file, array, and record found in the table and tree. 
14). File in R – It is a file extension for any script written in R language, which is designed for graphical and statistical purposes.
15). Arbitrary function in R – It is any function in a program; however, it is often referred usually to the same category of function that people deal with it.
Conclusion
There are many more things to learn and know about the R Language before you think about the R programming opportunities. The above is the modest list of terms found in R Language.

Data Analytics: Expectations vs Reality

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Data Analytics: Expectations vs Reality

As we see the field of data analytics getting to its peak in terms of career choice, hordes of young people and professionals now want to make their careers in this field. However, data analytics like any other field is not everyone’s baby. It can be a suitable career option for people, who love data, play with figures and are comfortable in handling a wide array of analytics tools that play a vital role while treading this career path. In other words, you must be aware of the myths and reality about this domain, or else you end up messing up your career and start cursing your fortune.
Why is Data Analytics a hot choice?
Of late, the number of young professionals working in different domains has developed an affinity towards data analytics. Some of these have shifted from their career in IT and other fields towards it, while there are many who despite not knowing what is analytics are thinking for a change in their job. Thanks to a growing number of data analytics courses online, more and more people are thinking to take a shift to this career. There are primarily two key reasons to get attracted to this field:

  • It is a lucrative industry to join
  • It can give good salaries and perks if you have a passion for numbers

However, most of the people who do not even know the data analyst meaning still want to enter it. Hence it is imperative to be realistic at this juncture when you are thinking of taking a shift to this field.
Data Analytics – Reality & Expectations
Although the career in data analytics can be lucrative, if it is not your cup of tea, there is no point in heading in this direction. First of all, check these realities:
The deeper you go, the tougher it becomes – Career in Data Analytics can be a lucrative option and could be selling like a hot cake but the deeper you dig, the harder it becomes. Learning and mastering the concepts of data analytics is not often an easy job, you are supposed to be committed and have the knack to play with numbers and play with data. You should own and hone analytical, technical and personal skills. The day you stop studying the concepts and ideas of this field, you just end up becoming obsolete very soon.
Meritocracy – This field is for people who are known for their merits and credentials. You may find it easy to join any data analytics courses online, but if you cannot excel in it, you may end up finding a clerical job in any data science company. You have to be the best in your work, and there are reports of people joining by being a blind follower. Instead, you should be realistic in choosing this career. An average understanding and competence in this field will not let you anywhere.
IT can Tough and Frustrating – Being a Data Analysts is like a software engineer who also has to keep on updating and upgrading himself to survive in this tough world. It can be a frustrating experience for many despite putting so many years and money as an investment as what you get would be too little to celebrate. Having said that, if this career is not addressing your Why, then you are bound to feel its toughness and end up leaving it out of frustration. Unless you are very sure about this career and have the knack and passion for playing with data, numbers, and analytics tools it’s naïve to even think of entering into this field. The field of data analytics is very demanding; you have to be a consistent learner with focus and then only harnessing the best opportunities in this field is possible.
Conclusion
With the rise in demand for data analytics in the market, there seems to be a craze among the youngster to enter into this field. However, it is always recommended to check the reality and expectations of this field and then decide to move ahead. After all, it is naïve to enter into this field if you do not even know the data analyst meaning.

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Is Data Analytics in The Demand?

Posted on by Imarticus Learning

If you are looking to enter the field of data science, then here are a few tools that you must consider learning. They will not only give you the required skills but also will help build your case. If we imagine these tools in the form of a pie chart then the percent occupied by these tools would be as follows;
R Programming- 26%
Python-23%
SAS- 20%
Tableau- 17%
Spark- 14%
For the first time in data science, the open sourced tools have taken away the crown as opposed to the licensed tools. These tools are commonly used by various professionals in data science like analysts and developers. They mainly are a part of the machine learning operations, data visualizations, and big data operations.
Today, data has taken over quite the ubiquitous nature and is being treated as an asset by many top firms in the industry. Many experts believe that these tools are soon going to be the next big thing to change the way data works. Which is why it is important to learn how to work with them and more importantly, find out which tools fit you the best.
Apart from those mentioned above, there is also a great demand for many open sourced tools. These tools are basically those that can be downloaded free of cost. They are Tableau Public, Refine, KNIME, Rapid Miner, Google Fusion Tables, NODEXL, WolframAlpha, Google Operators, FrontlineSolvers, Dataiku and so on. There are also others like SQL, Big Data Hadoop, and Pig which have great demand in the data analytics market.
Many of these tools help you out greatly in the process of data analytics. When it comes to a data analyst, there are a few end goals that have to be achieved. These professionals have to analyze data, extract valuable information from it so as to boost the performance of an organization and so on.
For instance, let’s talk about Tableau Public. This is a very simple tool, extremely easy to use and it democratizes visualization. It forms the base for data visualization in order to communicate similar such insights to the users. With the help of this tool, one can investigate a hypothesis quickly, explore the data as well as confirm whatever your intuitions about the data are.
Open Refine is another tool which was earlier known as Google Refine. It is essentially a data cleaning software, which ensures that the data is good enough to go in for analysis. There are many uses of this tool. These include cleaning of messy data, data transformation, parsing of data from websites, the addition of data to data sets by fetching it from web services.
Thus, there are many tools available in the industry today to choose from if you are interested in the big data analytics courses. In order to learn most of them, you can definitely take up professional training courses like the ones that are offered by Imarticus Learning, which will help you become industry endorsed.

How can you prepare for an interview for an M.Sc in big data analytics?

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Preparing for an interview is tough, especially when you want to lead the journey of life as a data analyst. Interview for a master’s degree course in Big Data analytics is no different. But interviews for an M.Sc. in Big data analytics are not that bad. With a robust big data analytics course in Mumbai, you can make sure that the interview’s result goes in your favor. First of all, try to understand the importance of big data analytics in recent times.
The top MNCs like Google, Facebook and many more, possess too much data to be managed by one person. Here big data analysts come in the picture. A person who is a data analyst is a person who can use tools like SAS, Python and many other big data tools to come up with complete results for the massive size of data.
The steps to be followed for being prepared in the M.Sc. interview for big data analytics are as follows –

  • Research about the organization: This is an age-old trick which appears to work more so than ever. After applying in an organization for M.Sc. in data, analytics don’t just sit and wait for the call. Research about the institute too. In this era of social media connecting to people is not a difficult task. Try to seek out the alumni of the foundation and ask them about the interview process. Try to learn from their experience.
  • Strengthen your mental maths skills: It is of no surprise that an M.Sc. interview for big data analytics will judge your mental math skills, i.e., the power to analyze in quick. For example simple questions like calculating the company’s yearly revenue based on the given information viz. price of products, number of products sold, etc. The quicker you answer these type of question more significant become the chance of selection.
  • Practice hard skills too: After establishing the power of your basics, the next job is to answer the hard question as well. An interviewer can ask questions from anywhere like Basic SQL, SAS, Python and many more. Be sure to have a bold grasp on most of them. Prepare for these while doing big data analytics course in Mumbai.
  • Rehearse the interview session: Try to imagine the scenario and act the way you want to be in it if you like download practice set the question of M.Sc. interview from the internet and practice them in person or with any of your friends.
  • Prepare some questions for the interviewer: This step is not as vital as the others but is an important one after all the primary motive of an interview is to communicate and check the eligibility. After you have proven your talent in basics as well as in advance data analytics processes, you may want to show off your communication skills also in front of your interviewer as it can increase the chances of your selection.

The importance of big data analytics in the modern world is not one to avoid. People in metropolitan cities like Mumbai are registering themselves in big data analytics course in Mumbai. Next time you think about the importance of big data analytics, think about how you get friend suggestions on Facebook, Suggested search results on Google, suggestions from SIRI or Google Assistant about the daily routine to follow and many others. Big data analytics are ensuring a better and bigger future to the communication sector as well as humanity. So try to grab hold of this big data analytics courses over platforms like Imarticus Learning to contribute your share to a better future.

7 Skills That Data Scientists Need To Know Via Big Data Analytics Courses

Posted on by Imarticus Learning

Data analytics is one of the most sought-after careers of today. Being a good data scientist involves developing a lot of skills essential to the job.
Here are a few skills you need to have on your resume if you want to become a good data scientist:

1.  Being capable of handling unstructured data

Unstructured data refers to any data that cannot be made to fit into any database tables. This data can include customer reviews, audio clips, blogs, posts, or even videos. Arranging such data into specific categories can be quite the daunting task. As a data scientist, you must be able to work with a lot of unstructured data. Some software that you need to know how to use for this purpose are NoSQL, Microsoft HDI insight, Polybase, Apache Hadoop, Presto etc.

2.  Good knowledge of Mathematics and Statistics

A good understanding of statistics is essential for anyone looking to become a data scientist. You must be familiar with all kinds of statistical concepts such as distributions and tests. Also, making predictions requires that you are familiar with the basic operation of calculus and linear algebra.

3.  Using data to tell a story

It is always easier for clients to understand data analytics if it is presented in a visual format using graphs, charts etc. Therefore you must have the capability to visualize raw data in a form that the layman can understand.

4.  Programming Skills

As a data scientist, you will be working with a lot of software that will require you to enter the code manually. As such, you must have a good knowledge of programming languages such as R and Python, which are normally used in data analytics. You must be able to write, understand and correct any code no matter the circumstances.

5.  A Competitive Spirit

As a data scientist, you will have to work on your toes more often than not. Therefore, it is essential for you to have a competitive spirit that will help you thrive. Hackerearth and NMIMS are two of the platforms that conduct hackathons, seminars and other competitions where you can gain more knowledge and understand all the latest trends in data analytics.

6.  Working on Projects

You must take up some live projects so that you get some hands-on experience in the field. This is important since most companies are looking for data scientists who are experienced in the field.

7.  Academic Qualifications

Most companies prefer their data scientists to have done their master’s degrees in the fields of computer sciences, mathematics, statistics and physical science. If you’re interested in working with research companies, then it will be advantageous to have a PhD in the same subjects.

The Complete Guide on Choosing The Best Data Analytics Course

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Are you interested in data analytics and application and data analysis tools? Well, just having an interest in this field will not take you any further. When it comes to choosing any data analytics course, you should know that there are two different phases of your chosen specialization. The first phase will help in finding out the apt type of analytics training you want, while the second phase would tell you the proper understanding of the practical implications regarding the analytics training. In other words, you need to understand the data analytics jobs and check the perspective of the same, which will further define your training.
The right approach
 After you have the answer for the question, what is data analytics?, the next common question that is posed by most of the people include, what kind of analytics training will be appropriate for me as per the educational background?
This will be your starting phase when it comes to choosing a Big Data data analytics course. However, a majority of people end up failing to find the right training institute since they tend to follow the modern trends. One can see many courses and degrees dealing with data analytics; however, your choice should not be based as per the nice ads or marketing gimmicks but finding out the answer of the question, what do I need to get success in this field and get the data analyst jobs.
The 1st Phase – Choosing the best Data Analytics Course that you require! 
First things first, have your analytics aptitude assessment test. This assessment test may not be a fun thing, but if you are really keen on pursuing your career in this field, then you should have some basic aptitude for data. Once you are clear on this part, you are then supposed to find answers to the following questions and then think of joining any data analytics course:
Are you sure about why you really need this course?
Your perfection should be apparent when it comes to choosing any training on data analytics. This will help you in choosing the right program and course content. So, depending upon the requirements of your chosen domain or data analyst jobs, you should select the course. In other words, you should be crystal clear about what you want from your course.

Check the skills where you lag behind 
You know better about your strengths and weakness. This will eventually help in getting the insight about the kind of skills that you need for your career path. The data analytics jobs would need good technical skill sets along with a good understanding of mathematics for being competent in your work. The popular analytical skills required for the job including the following:
• Good exposure and experience with Data-to-Decision Framework
• The Basic knowledge of business and data analytics
• SQL skills
• Learning skills in working with stakeholders
• Good exposure to predictive analytics
• Experience in handling stats and data analysis tools including SAS, Knime, R, to name a few
Besides these, a working professional would need focusing on stat tools, DTD framework, advanced stat methods, collaborations with analysts, etc. So, depending upon the gap you have in your skill sets, you can further choose the apt data analytics course. You have three key options when it comes to choosing any course, which includes the following:
• The master’s program in data analytics
• A short-term semester program
• Enrolling for any professional workshop
The 2nd Phase- The Analytics Career
If you are keen on making a career in data analytics, you should know the fact that these are not the same as the IT industry in terms of requirement, placement and perks. Since the data analytics courses and subjects are not covered under any undergraduate program, hence getting direct placement in campus is not at all possible. Companies advertising data analyst jobs look for candidates having prior experience in this field.
In other words, you should know how to use the data analysis tools then only you will be called for the interview. Hence once you go beyond the question what is data analytics and start pursuing any program make sure you keep on getting some hands-on experience by being an intern in any company or try connecting with people working with real-time data. This will add you credibility.
On your Toes
Data Analytics is constantly growing and with every passing day, there is something experimented and added to it. If you are choosing any data analytics course without any consideration, then you are committing a blunder. Data is becoming complex with every passing day. You can find a good number of data analysis tools being added to the list that help in handling the data with great security. Data is the future currency of any company, hence if you are considering this career just for fun think again. This is because it would be difficult for you to crack any interview for data analyst jobs.
Different domains have different requirements
Every industry is different, and so are the requirements. This goes without saying that the data analyst jobs you need in one domain would be different than that of other domain. The techniques and data analytics tools you one in one could be outdated in the other. You should know this reality before you join any data analytics course. Be very sure about the domain you choose and get an edge over it rather than trying different things at one time.
Wrapping up
Choosing a data analytics course is not less than rocket science provided you do not know anything about it. If you have a fair about understanding about what is data analytics, and its various other aspects, the above tips can help you in choosing the best course.

How Can You Learn About Healthcare Data Analytics?

Posted on by Imarticus Learning

 

Data analysis positions are becoming more and more hard for the organisations to fill, thanks to the skyrocketing demand for data professionals. With a tons opportunities lying ahead, the time and effort spent to learn data analytics is worth every penny. Wondering how to start learning it? keep reading the article.

What is Data Analytics?
In simple words, data analytics is the process of sorting massive chunks of unstructured data and delivering important insights. The insights play a crucial role in making important decisions in business. Be it a small or large organisation, the service of a data analyst is vital to their operations.

The Data Scientists are an entirely different job than Data Analyst. The former one is involved with more programming, creating algorithms and building predictive models while analytics is much less complex. With proper knowledge and certification in data analytics, you will be able to pursue careers such as Data Analyst, Business Analyst, Product Manager, Digital Marketer and Quantitative Analyst.

Learning Data Analytics
 Data analytics demands problem solving and communication skills to be successful. However, you will also need some technical skills to perform the jobs relating data analytics. Some most common skills you will need are the following: 

• Excel (Spreadsheets)- Microsoft Excel is a spreadsheet program widely used for complex data analysis. The built-in pivot tables in Excel are one of the most popular analytic tools.
• SQL (Database Language) – SQL or Structured Query Language is used to add and retrieve data quickly from a database. It allows operations on millions of rows of data.

• R (Programming Language) – R is a programming language built for statistical computation and graphics. It is a popular tool among statisticians, data miner, data scientists, data analysts and business analysts. It is an essential tool for developing statistical software, machine learning and data analysis. Many high profile companies like Google and Facebook have adopted R as their preferred language to analyse data.

• Data Visualization – Data visualisation is an important part of communication in data analysis. It helps the key decision makers of organisations to identify the insights and trends easily and understand the complex information. With a touch of creativity, this skill is comparatively easy to have.

Other few important skills you can consider for a better leg in data analytics role are:
 • Google Sheets – It is similar to Excel but a Cloud version.
• Tableau – It is a GUI data visualization tool. allows easier data visualization.
• Data Studio – A Data Visualization Tool from Google
• Google Analytics – It is a free web analytics tool from Google.
• Math Skills – Linear algebra and multi variable calculus are an added advantage.
• Primary understanding of Machine learning.

Conclusion
 With companies flooding with data, there is a large gap between the demand and availability of people who can use data right. With technologies like the Internet of Things coming up, the demand for such people will only go up.

So, it is the best time to start learning data analytics. Imarticus Learning is providing a data analytics course to help those who aspire a career in this field. Be sure not to miss this opportunity.

How Can You Make a Good Career in the Data Analytics Industry? What are the Skills You Need to Develop If You Have to Start From Scratch?

Posted on by Imarticus Learning

Careers don’t just happen. Especially those in Big Data and Analytics! Let us look at the needed skills and what you need to do to make a happening career in this field. Here are the basic steps in the path to success.
Do the math:
Your game plan and strategy counts! Firstly, research your thoughts, options and why you want a career in this field, what will the payouts be, what is the scope for the job roles you aspire for, which are the top companies, how you plan to put your plan into action and have a great SWOT analysis. Some relevant information here may help.
Some high-ranking companies in Business Analytics to watch for are Cognizant, TCS, IBM, Wipro, Infosys, Accenture, HP, Deloitte, Capgemini, Genpact among others and in no particular order. Some startups like Mu Sigma Analytics, Fractal Analytics, AbsolutData can offer you the best opportunities in the field of business analytics.
Top roles are

  • Data Analyst
  • Business Analyst
  • Product Manager
  • Digital Marketer
  • Quantitative Analyst

BAs bring analytic and business skills to the table and receive good remuneration. The average annual salary is 859,025 INR/ year for a Senior Business Analyst as per the figures of Payscale. The annual average payouts of a BA is INR 6,44857/year as per Glassdoor.
Plan your career strategy:
BA job roles call for a fusion of project management and analytics. A foundation in engineering and mathematics with excellent communication and analytical skills is essential. However, those who already have some background in business opt to enable a career as a BA by upgrading skills with online short courses and a career in data analytics courses. 
These aspirants are empowered with a good skill set in business analytics that helps them start a career with some certification. Graduates in engineering tend to move towards the information management and data engineering fields, while aspirants with some business experience easily transform into roles as a Business Analyst. An MBA graduate should enhance skills by doing a business analytics course.
Plan acquiring your skill set:
Here’s a list of technical skills required. A Business Analyst must have proficiency in the application of statistics with conceptual knowledge of suites like   SQL, R, SAS, testing framework, SPSS, Hive and tools in BI such as Tableau, Excel, Spotfire, Qlik, among others. Skill sets required change depending on the infrastructure and organization’s requirements and functional roles needed.
Do a course to acquire them:
The business analytics course in Mumbai offers a good grasp of fundamentals, concepts, theoretical knowledge, practical skills and certifications that could help enhance your resume and career. They also offer boot camps, short term workshops, and basic knowledge of SAS and R. While certification definitely helps you need to be an excellent communicator and work diligently to acquire the best analytical and business skills. Another advantage in such courses is of mentoring by certified and experienced industry aces that helps garner the latest best practices, techniques, skills, and practice on the latest trending technologies in the field of Business Analytics.
Apply what you learn:
Knowledge implies having the ability to translate theory into action. Accumulated skills get rusty in a while. So continue to hit the refresh button on your skills and do relevant courses in garnering additional skills that will set you apart from the aspiring job queues.
Get some experience:
Apply for internships to get some valuable experience on your resume. You can also volunteer and work part-time for some of the larger companies to ensure you have practical skills.
Now that you possess most of the skills required, do an internship and land a job based solely on your skills.

Data Lake And Big Data Analytics

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If you have been in the IT space and data analytics space for some time now, you might have come across the term Data Lake at least once. But since the technology is in its early days, not a lot of people known what it is all about and thus in this article we will discuss all about data lakes, their benefits and how they are helping in data analytics.
What is a Data Lake?
In the most simplest of terms, a data lake is a centralized storage or repository that allows you to store all your structured and unstructured data, be it of any scale. The main significant difference between a data lake and other centralized repository options available in the market is the fact that a data lake will allow you to store your data without the need of any restructuring and also allows you to run various kinds of data analytics right on the repository.
The various data analytics option present in a data lake starts from dashboards and goes all the way up to visualisations and big data processing, and even real-time analytics and machine learning to help the user for making better decisions.
The Need For A Data Lake
As you might have already guessed, the need for access to a data lake is more important in this day and age than ever before, since the number of companies dealing with big data is constantly on the rise. A recent survey, conducted by Aberdeen found that companies which used data lake facilities were able to perform 9 per cent better to those who didn’t; this fact alone can contribute to the need of using a data lake.
The Benefits of a Data Lake
Similar to any other technology in the market, Data Lake too comes with a host of advantages which helps it stand apart from the rest. Some of the most significant ones are as mentioned below.

  1. Capability to store and run analytics, thus deriving results from unlimited data sources
  2. Capability to store all types of data, both structured and unstructured, thus covering everything from social media posts to CRM data
  3. Increased flexibility from other systems in the market
  4. Option to eliminate data silos
  5. Ability to run unlimited queries at any point in time

Data Lake and Data Analytics
As mentioned in the earlier paragraphs, data lakes in today’s world have multitude applications, one of the most significant being the ability to run data analytics on a host of different data types.
Companies which deal with a massive amount of big data, often face with the difficulty of storing different formats at different locations, thus making data analytics a virtually impossible option. But with data lakes, all forms of data, both structured and unstructured can be stored in one place, thus allowing the user to run analytics and visualization from one dashboard and derive results. On top of that, having a single data lake, companies save up on huge amounts of money and make higher profits in the long run.