Contact Us
×

Tag: data analytics

Will Data Analytics Ever Rule the World?

Posted on by Imarticus Learning

Of late, there has been a sudden surge of data analytics in the world. This will undoubtedly change the way people live and trade in the market. The use of data analysis tools is increasingly used in different technology devices for carrying out several day-to-day decisions in professional lives. It helps people to drive the business smoothly by identifying waste and blank spots seeking the help of different data analytic tools.

Although the companies are finding crunch in leveraging the ideas of this field, yet several global surveys reveal that it has the capacity of make the impossible possible, and it is still in the early stage of the data age. Today, most of the companies are investing in data analytics capacities by creating data analyst jobs are merely to remain in the competition. Data analytics have a great future, and it has the potential to rule the world.

Data Analytics- The Present and the Future
The data analytics development cycle can be defined in different stages. It starts from Descriptive to Diagnostic stage – The former deals with what happened, while the latter explains why did it happen? Then comes the stage of discovery followed by predictive. The former deals with everything that helps us to learn from and the latter talks about the things those are likely to happen.

Lastly, the prescriptive analytics that deals with what kind of action is to be taken. Generally speaking, the organizations today are in the first stage (diagnostic and discovery stages).

In order words, the data analyst jobs are simply helping companies to make informed and better decisions than before. With proper use of data analysis tools, it has become simple to blend a number of multiple data sources giving away the insights.     Thus experts feel that it would be the backbone of a decision-making process, which will end up in producing a better outcome. The Google Car is the classic example of it.

The impact on Business
There will be a radical change in business with the use of data analytics. More and more new data analyst jobs will be created and the job profiles would change with the growth of the market by unleashing the power of this field. With the passage of time, the number of data analysis tools will keep on adding new capabilities, which will help in managing and storing the data effectively.

Also, there will be newer methods of analyzing the data will emerge seeking the help of cognitive analytics and machine learning ideas. This will further help in giving few professions. Currently, IBM Watson and MS Cortana are among the forerunners in this domain. So, the days of asking what is data analytics, are now gone as the world are in the transition phase and soon would have data analytics dominating everywhere.

The Opportunities
The modern day smart devices are easily able to share data with the Internet of Things and are able to deliver massive amounts of data. These include the sensor data including location, health weather, machine data, and error messages to name a few. This will help in honing diagnostic and predictive analytics capabilities. Things would turn inexpensive, as people will be able to exchange the supplies even when it is not required, however, with this you can boost up the uptime.

Also, the coming time will make things simple and user-friendly to connect all types of data from numerous sources to each other. This will end up giving the insights in real time. You will be able to solve all your issues in minimal time duration, which will further settle down the challenges of business and IT alignment. These challenges will not be seen in the coming years with the advancements in data analytics courses and technologies.

Wrapping up
Needless to say that data analytics will rule the world. Currently, the world is passing through the transition as data analytics remain in the nascent stage. However, with ongoing research and development in this field, the data analyst jobs with better insight and capacities will increase and change the phase of the world. So, if you are planning to join any data analytics course, it’s the right time to invest.

Sports Analytics: How Data Analytics Is Changing The Game Strategy?

Posted on by Imarticus Learning

Big Data Analytics Training Courses

We have witnessed how Big Data and its analysis have reshaped the operation of many businesses. Recognizing the facts of big data analytics courses, the true scope of data analysis, the sports world making use of analytics. As we speak, the world of sports is improving its capabilities using sports analytics.

So, What Is Sports Analytics?
Sports analytics can be roughly translated to the use of data related to sports such as statistics of players, Weather conditions, pitch information, etc. to create predictive models to make informed decisions. The primary objective of sports analysis is to improve team performance. Sports analytics is also used to understand and maintain the fan-base of big teams.

The Sports Analytics was brought to the public eyes in 2011 by a movie called “Money ball” featuring attempts of the Oakland Athletics Baseball team’s 2002 season. The coach, Billy Beane restored the team using empirical data and statistical analyses on players’ performance.

He used trials with sabermetrics to improve his team and ended up finishing at the first place American League West on that season. Today with the advancement in Big Data technology every sports team is crunching data to gain a competitive advantage.

Changing the Strategy
Sports analyzers nowadays use wearable devices to collect data from players. The miCoach is such a wearable device developed by Adidas. This device attached to players’ jersey records data like heart rate, speed, and acceleration of the player. Analyzing this data, the team management is able to select the suiting players for the game. It also enables them to track the condition of players and allow them to rest before they get injured.

Video analytics is also being increasingly used across various sports for collecting data. In the NBA games, a company named SportsVU installed 6 cameras around the arena. Using advanced metrics, they were able to produce information about which move and which shots are best suited for each player. Such analytical results help teams to derive game strategies matching the strength of their players.

Big Data Analytics Training CoursesThe same is used to learn about the players of the opposite team’s players to find their weaknesses. Arsenal is one of the major football clubs to make huge investments in big data analytics courses.

They use a system that tracks 1.4 million data points per game and analyses all the data using an automated algorithm.

The Future of Sports Analytics
Without any doubt, sports analytics will continue to evolve, and the game strategies will heavily rely on the insights from the analysis than instinct. The next breakthrough sports world expecting from analytics is in the area of predicting a player’s mental ability to adjust with the rigors of the professional sports world.

There are already researches about finding the correlation between emotional regards of responsibility and on-field performance.

The current analysis is not capable of measuring an athlete’s desire to be the top performer. Lack of such features brings a slight chance for drafting busts. Looking at the rate at which the sports analytics have grown to today’s state, It is sure that more of these data-driven advancements in sports can be expected in the upcoming years.
 

10 High Value Use Cases for Predictive Analytics in Healthcare

Posted on by Imarticus Learning

Healthcare organisations are having their moment when it comes to Big Data and the potential it offers through its analytical capability. From basic descriptive analytics, organisations in this sector are leaping towards the possibilities and its consequent perks of predictive insights. How is this predictive analysis going to help organisations and patients? What are the top roles a Data Analyst look for?
Let’s break this down into ten easy pointers:

  • Predicting Patient Deterioration

Many cases of infection and sepsis that are being reported among the patients, can be easily predicted via predictive insights that Big Data offers. Organisations can use big data analytics to predict upcoming deteriorations by monitoring the changes in the patient’s vitals. This helps in the recognition and treatment of the problem even before there are visible symptoms.

  • Risk Scoring for Chronic Diseases

Based on lab testing, claims data, patient-generated health data and other relevant determinants of health, a risk score is created for every individual. What this does, is that it leads to early detection of diseases and a significant reduction in treatment costs.

  •  Avoiding Hospital Re-admission Scenarios

Using predictive analysis, one can deduce risk factor(s) indicating the possibility for re-admission of the patient to the hospital within a certain period. This helps the hospitals design a discharge protocol which prevents recurring hospital visits, making it convenient for the patients.

  • Prevention of Suicide

The Electronic Health Records (EHR) provides enough data for predictive algorithms to find the likelihood of a person to commit suicide. Some of the factors influencing this score are substance abuse diagnose, use of psychiatric medications and previous suicide attempts. The early identification helps in providing the mental health care potential risk-patients will need at right time.

  • Forestalling Appointment Skips

Predictive analysis successfully anticipates ‘no-shows’ when it comes to patients and this helps prioritise giving appointments to other patients. The EHR provides enough data to reveal individuals who are most likely to skip their appointments.

  • Predicting Patient Utilization Patterns

Emergency departments of regular clinics have varying staff strength according to the fluctuations in patient flow. In this case, predictive analysis helps to forecast the utilization pattern and the requirements of each department. It improves patient wait-time and utilisation of facilities.

  • The Supply Chain Management

Predictive analysis can be used to make efficient purchasing which in turn has scope for massive cost-reduction. Such data-driven decisions can also help in optimizing the ordering process, negotiate the price and reduce the variations in supplies.

  • Development of New Therapies and Precision Medicine

With the aid of predictive analysis, providers and researchers can reduce the need of recruiting patients for complex clinical trials. The Clinical Decision Support (CDS) systems have started to predict the patient response to treatments by analysing genetic information and results of previous patient cohorts. It enables the clinicians to select treatments with more chances of success.

  • Assuring Data Security

By using analytic tools to monitor the data access and utilization pattern, it is possible to predict the chances of a potential cyber threat. The system detects the presence of intruders by analysing changes in these patterns.

  • Strengthen Patient Engagement and Satisfaction

Insurance companies encourage healthy habits to avoid long-term high-cost diseases. Here, predictive analyses help in anticipating which communication programmes would be the most effective in each patient by analysing past behavioural patterns.
These are possible perks of using tools like predictive analyses in healthcare that optimise processes, increase patient satisfaction, enable better care mechanisms and reduce costs. The role of Big Data is clearly essential as demonstrated and a targeted use can show high-value results!

The Startup Wave Gets a New Accelerator : OceanPro by Maersk

Posted on by Imarticus Learning

Ahoy! There’s a new sailor on board! The Indian startup revolution is just about to witness a sea of change. For years now, startups have struggled with finding the right mentors for when they need an expert opinion on the challenges they face on a day to day basis, finding investors during the seed stages, funding, regulations and even public outreach. Thus, startup accelerators that provide all this under one cohort program are indispensable for business analysis.
Maersk, the Danish shipping giant, has recently launched its very own startup accelerator, called ‘OceanPro’ and is inviting applications from startups for the same. Anyone can apply, including individuals as well as business analysts.
The Juggernaut

Maersk, a multinational company founded and headquartered in Denmark, is the market leader in the shipping, supply chain and logistics industry. It operates in over 130 countries and employs a workforce of  roughly around 76,000 people. Currently, Maersk.com is one of the world’s largest B2B transaction sites, with roughly an average hourly revenue of 1.3 million US Dollars.
The company seeks to simplify its customers’ supply chains and to ship around the world, using the power of business analysis.
OceanPro in a nutshell
Maersk has launched its startup accelerator programme, OceanPro, to accelerate technology in India.
Maersk Group CEO Soren Skou said recently during his visit to the Bangalore center that they recognize India’s potential and talent in the digital space and that they are looking to reinvent the wheel with the logistics industry, leveraging on this wide pool of talent.
The programme, mainly in the silicon city and startup hub, Bengaluru, seeks to collaborate with startups with the aim of simplifying supply chain. It’s a 120-day programme, taking place in the co-location space of Maersk’s digital centre in Bengaluru.  With OceanPro, startups looking to connect with the shipping, supply chain and logistics industry’s who’s who can do so easily, as well have real customer interaction, while leveraging on the talent and insights provided by Maersk. The icing on the cake : Maersk may provide funding to companies they deem fit. This is  also one of its kind opportunity for business analysts to sharpen their business analysis skills in a precocious setup.
Essentials and features of OceanPro
One of the aims of setting up OceanPro is to bring about changes in the logistics industry, by incorporating the latest technologies of Blockchain, Artificial Intelligence (AI), Internet of things (IoT), Advanced Text analytics, FinTech and augmented and virtual reality, for the benefit of the industry, making it smarter, intelligent and more efficient. Today, with AI and FinTech revolutionizing every industry and the way we operate, it becomes important the logistics industry too realizes this to step up and be an even player.
So far, the shipping giant has invited eight startups to join and participate in the interactive programme, viz., Dhruv, KrypC, LinkedDots, La Vela Pictures, Unido Labs, zasti, Inatrix and MintM. The company also aims to form partnerships with universities, venture capitalists, investors and other accelerators. Also, Maersk has a sponsorship program for two Master of Technology fellows, in collaboration with the Indian Institute of Science, for developing leading business analysis solutions. This is a golden opportunity for business analysts to take their career to the next level.
The program is aimed at leveraging the power of business analysis, to introduce agility into the supply chain ecosystem by making it more responsive and mining out hidden insights that are useful.
Startups that are interested and are a good fit can apply soon as the invitation to apply is open, if you can commit for four months, are willing to adapt and customize and receptive to learning.

Healthcare’s Top 10 Challenges in Big Data Analytics

Posted on by Imarticus Learning

Healthcare’s Top 10 Challenges in Big Data Analytics

There are multiple perks to Big Data analytics. Specifically, in the domain of healthcare, Big Data analytics can result in lower care costs, increased transparency to performance, healthier patients, and consumer satisfaction among many other benefits. However, achieving these outcomes with meaningful analytics has already proven to be tough and challenging. What are the major issues slowing down the process and how are they being resolved? We will discuss the top 10 in this article. 
Top 10 Challenges of Big Data Analytics in Healthcare

  • Capturing Accurate Data

The data being captured for the analysis is ideally expected to be truly clean, well-informed, complete and accurate. But unfortunately, at times, data is often skewed and cannot be used in multiple systems. To solve this critical issue, the health care providers need to redesign their data capture routines, prioritise valuable data and train their clinicians to recognise the value of relevant information. 

  • Storage Bandwidth

Typically, conventional on-premises data centres fail to deliver as the volume of healthcare data once reaches certain limits. However, the advancement in cloud storage technology is offering a potential solution to this problem through its added capacities of information storage. 

  • Cleaning Processes

Currently, the industry relies on manual data cleaning processes which takes huge amounts of time to complete. However, recently introduced scrubbing tools for cleaning data have shown promise is resolving this issue. The progress in this sector is expected to result in automated low-cost data cleaning. 

  • Security Issues

The recurring incidents of hacking, high profile data breach and ransomware etc are posing credibility threats to Big Data solutions for organisations. The recommended solutions for this problem include updated antivirus software, encrypted data and multi-factor authentication to offer minimal risk and protect data.

  • Stewardship

Data in healthcare is expected to have a shelf life of at least 6 years. For this, there is a need an accurate and up-to-date metadata of details about when, by whom and for what purposes the data was created. The metadata is required for efficient utilisation of the data. A data steward should be assigned to create and maintain meaningful metadata.

  • Querying Accesses

Biggest challenges in querying the data are caused by data silos and interoperability problems. They prevent querying tools from accessing the whole repository of information. Nowadays, SQL is widely being used to explore larger datasets even though such systems require cleaner data to be fully effective.

  • Reporting

A report that is clear, concise and accessible to the target audience is required to be made after the querying process. The accuracy and reliability of the report depend on the quality and integrity of data.

  • Clear Data Visualization

For regular clinicians to interpret the information, a clean and engaging data visualization is needed. Organisations use data visualization techniques such as heat maps, scatter plots, pie charts, histogram and more to illustrate data, even without in-depth expertise in analytics.

  • Staying Up-to-Date

The dynamic nature of healthcare data demands regular updations to keep it relevant. The time interval between each update may vary from seconds to a couple of years for different datasets. It would be challenging to understand the volatility of big data one is handling unless a consistent monitoring process is in place.

  • Sharing Data

Since most patients do not receive all their care at the same location, sharing data with external partners is an important feature. The challenges of interoperability are being met with emerging strategies such as FHIR and public APIs. 
 Therefore, for an efficient and sustainable Big Data ecosystem in healthcare, there are significant challenges are to be solved, for which solutions are being consistently developed in the market. For organisations, it is imperative to stay updated on long-term trends in solving Big Data challenges. 

Why is Excel Such An Undervalued Tool For Data Analysis?

Posted on by Imarticus Learning

Microsoft Excel is one of the most popular data analytics tool available in the market. Initially released way back in 1987, its popularity increased manifold in 1993 after the launch of its Version 5. In its most basic version, Excel is a kind of spreadsheet where users can generate and store their data and interact with it to perform all sorts of operations and view them in the form of graphs, charts and other sorts of visualizations.

Widely regarded as one of the best tools in data analytics at one time, Excel has lost much of its reputation and prominence to other more advanced software and tools in recent time.

Although a worthy and powerful tool for data analytics, it does not feature in top 5 of most of the industry experts of today. In this article, we are trying to look at some reasons for the breaking up of the acclaimed connection between data analytics and excel in today’s world.

Unfair Comparison with Advanced Tools
With the arrival of more specialized tools for handling different aspects of data analytics, people find them better for their specific needs than a general workhorse like Excel and make the conclusion of Excel to be useless. It is not a fair comparison though. Some experts have pointed out that it is like comparing a Minivan to a large-scale cargo hauling such as Freightliner or comparing a minivan with a Formula 1 car and concluding that minivan is useless since it isn’t a Formula 1 car.

Excel works as a general tool for a wide range of data analytics work and is good for quickly building a wide variety of highly specialized timesaving workflow tools. The newer tools generally try to target and specialize one or two components of data handling and therefore are more advanced and capable in those aspects than Excel.

Relative Ease for Understanding and Operate
One can start working on MS Excel even with a basic knowledge of computer and networking. It does not require a high level of education or knowledge to be a master in Excel. Even kids of elementary schools can be taught to operate Excel with much ease. Excel is also easier to operate than the other advanced specialized tools in the market.

This relative easiness in understating and operating of the tool creates a misconception among many that it is not sophisticated enough to handle complex aspects of data analytics and visualization. It is however a wrong assumption. Even the best tools in data analytics require the knowledge and functioning of Excel in at least some part of their operation. Data analytics and excel have always went hand-in-hand and one is inseparable from the other.

Difficult to Find Errors
Even as one of the best tools in data analytics, Excel functions with the use of simple and complex analytical formulas. And since the formulas are only used for computation and calculation on the data, any errors which may reflect in the outcome becomes very difficult to be searched. Since there is no usage of coding in Excel, it is nearly impossible to automatically detect any error in data without having to go through the complete data manually.

Inability to handle Big Data
One of the major cons of using Excel is its inability to handle big data in data analytics. Since big data is emerging as a major component in today’s world in almost all major sectors, the simplicity of Excel makes it incapable of handling such larger data creating a perception of it being inferior to other such tools which can handle big data efficiently. Even after this disadvantage, it is impossible to overlook the sheer history of data analytics and Excel in any way.

Why is Data Science So Famous?

Posted on by Imarticus Learning

Data Science is clearly the way to the future and is revolutionizing a number of fields across industries. In just a few years, it has emerged as the most sought-after career route. In spite of all the hype, a lot of people end up asking ‘What is data science, exactly?’

Data analytics is basically the examination of data, and a data science course mainly equips young tech enthusiasts to sift through huge amounts of scrambled data to process them and extract information out of them.

From healthcare to politics to disaster management, data science is making way for breakthroughs all over. Celebrated computer scientist Jim Gray considered data science to be a fourth paradigm of science, and insisted that information technology is changing everything about science.

Decades after his prophecy, he stands corrected, and a career in data science is one of the most lucrative career aspirations you can have. But it is very important to know exactly what data analytics does, and how it is changing the world around us.

So, what is data science and why exactly is it the hottest career right now?

Did you know that according to the company review website Glassdoor, data science was the highest paid field in 2016? Glassdoor is basically a platform where employees can rate their workplace and its management. The 2016 report was actually based on reviews of the people in the data science field, and their income growth. The survey also took into account the possibilities for career growth of the people working in the field.

You must understand that a data science course consists of a number of skills which the aspirants must marvel at, like programming, statistics, coding etc, and this makes their skill set a very coveted one in the field of analytics. Data science training is still mainly about about figuring out trends and patterns based on statistics and jumbled data. More and more companies are hiring data scientists for strengthening their analytics team, which is why the data science field is such a lucrative one.

In the field of artificial intelligence (AI), especially, data science training is an invaluable asset. You must have heard about the exponentially growing influence of AI in today’s industries. Every major company is seeking data scientists who specialize in AI. To put it simply, a successful AI assignment is not possible without the right data, which is extracted by a data scientist and processed to their advantage.

Let’s look at other simple examples. Take for instance, companies like Google. Their operations and service depends almost entirely on successful data analysis. Are you aware that the HR department at Google has completely changed the game for other corporate companies, when it comes to perfecting work culture?

They have moved to a form of data-based employee management, where they sift through data and process it to make the company a better place to work for their employees. As a result, research has shown that Google is the best corporate company to work in the world right now.

Some of the best and the most successful companies in the world are investing millions of dollars to amp up their data science branch, and this hardly comes as a surprise in the era of information technology. Even small businesses need to study data and the statistics of the market before they can launch their products.

Not to mention a data scientist earns substantially better than his counterparts in other sectors, and it can only get better if the pattern is followed. Research has shown that the median salary of the data scientist is something around $110,000. In a couple of years, a data scientist with only a few years of experience will not have to look around for long to get better opportunities, considering the boom in the field and the overriding necessity of data analytics.

Importance of Data Analysis in India

Posted on by Imarticus Learning

The importance of data in the world of today can not overstate. Though data has formed the backbone of all research for centuries, today, its use has spread to businesses – both online and offline, governments, think tanks which help in policy formulation, and professionals.
With the surge is collection and dissemination of data, the importance of data analysis has grown as well. While data collation is vital, it is just the first step in the process of using it. The ultimate use of data is to draw meaningful insights from which can then be put to use to practice. Data analysis helps in doing this by transforming raw data into a human or machine-usable format from which information is being drawn.
Also Read: What is Data Analysis and Who Are Data Analysts?
Data AnalyticsSome ways in which data analysis can be distinguished are as follows:

  • Organizing data: Raw data collected from single or multiple sources may be disorganized, or present in different formats. Data analysis helps in providing a form and structure to data and makes it useful so that other tools can be used to arrive at findings and interpret the results.

  • Breaking down a problem into segments: Working on data collection from an extensive survey or transaction and consumer behavior data can become very challenging due to the sheer volume of data involved. Data analysis techniques can help segment the data thereby reducing a massive, seemingly insurmountable problem, into smaller parts which can be relatively easily tackled.
  • Drawing insights and decision-making: This is the aspect which is most readily associated with data analysis. Tools and techniques from the field applied to pre-organized and segmented data assist in drawing meaningful insights which can either help in concluding a research project or support business in understanding consumer behavior towards their products better.

Further, through data analysis in itself is not a decision-making process, it certainly does help policymakers and businesses make decisions based on insights, information, and conclusions drawn while researching and analyzing data.

  • Presenting unbiased analysis: The use of data analysis techniques helps ensure that unwarranted biases – human or statistical – are reduced at least or eliminated at best. It helps ensure that top quality insights can be extracted from the data set which can help in taking effective policy actions or decisions.

Some people misconstrue data analysis to be just the presentation of numbers in a report based on which researchers support their thesis or managers take decisions. This is far from being true. More than merely data collection, data analysis helps in cleaning raw data, dissecting it, and analyzing it. It can also assist in presenting the insights drawn or information received from this exercise in a format which is compact and easy to understand.
In companies, there are data analysts and data scientists who are responsible for conducting data analysis. They can play a crucial role in harvesting information and insights from the data collection and study cause and effect relationships by understanding the meaning behind figures in light of business objectives. They are trained to process technical information and convert it into an easily understandable format for management.
Some data analysis methods that they use include:

  • Data mining: This studies patterns in large data sets – also known as big data – by applying statistical, machine learning, and artificial intelligence methods.
  • Text analytics: It processes unstructured information in text format and derives meaningful information from it. It also converts this information into the digital format for use by machine learning algorithms.
  • Business intelligence: This method draws insights from data and converts it into actionable information which is used by management for strategic business decisions.
  • Data visualization: This method uses data analysis tools to present trends and insights visually, thus making data more palatable.

Companies like Amazon and Google have made pioneering efforts in using data analysis by applying machine learning and artificial intelligence to create end-user experience better. Given that we are living in the information technology age, the use of data analysis is expected to increase manifold in the future and enhance its scope.
Also Read:

Top 3 Reasons to Include Data Analytics in a Financial Audit

Posted on by Imarticus Learning

Auditing basically refers to collection, analysis and examination of and verification of all the financial records of any company. This means that all those professionals who work under the team which is supposed to financial audit the company, essentially have to deal with a lot of data and information.
This is exactly where the concept of data analytics comes in. Just like any other activity, this activity is also supposed to work with a great amount of data that is generated by the whole company through invoices, receipts, ledgers and so on. Data Analytics has one main function, to collect and process the data in order to derive insights from the same.
Read Also : Data Analytics Market Growth and Scope Analysis in 2018
So in an economy which is full of uncertainties and high risks today, there is a great amount of pressure on audit teams worldwide to be on their toes at all times. There are major expectations from these, including the ability to provide companies with exact results with assurance of quality and continuity. In such a challenging scenario, one can find that the concepts and techniques of data analytics come to play a major role.
Mainly because of what their functions are, future of a Data Analyst professional shines very bright. Big Data has been gaining a lot of importance over time, as it data analytics and the integration of the same in the process of financial audits, would not only give accurate results but also ensure that the companies can maintain their remarkable acumen in business matters.
Here’s what few of the Big Cheeses of the business world have to say in favour of the inclusion of data analytics in the process of financial audit:
“With the exponential growth in data and availability of inexpensive new technologies to generate business insight and value, this is an opportune time for the internal audit profession to provide greater value to their organizations,” says Neil White, Deloitte Risk and Financial Advisory principal and Global Internal Audit Analytics leader at Deloitte & Touche LLP.
“It’s a massive leap to go from traditional audit approaches to one that fully integrates big data and analytics in a seamless manner,” says Roshan Ramulkan from EY.
Both of these professionals are talking about the massive possibilities that are in store especially when it comes to the integration of data analytics in the process of financial audit. Given below are three ways it would be beneficial for various companies:

  1. Data Analytics will ensure that every financial audit produces more reliable reports and insights, thus helping in the governance of the company.
  2. Analytics will also help the financial teams of the company understand the impact of changes in accounting can affect the financial statements and help them manage things efficiently.
  3. Analytics will help the company find out the best financial processes and focus on the best process controls in order to provide most efficient of insights.

Thus, such great additions to the financial audit will pave a way for a great career in Data Analytics in India for many.

Related Article : Career Opportunity in Data Analytics

Data Analytics Trends in 2018

Posted on by Imarticus Learning

For a decade, Data Science has now been a hot topic, but most of its concept was present theoretically. The practical application of Data Science became possible after the existence of large data sets to work upon, effective machine learning algorithms, and systems to operate these algorithms.
Data Analytics is a lifeline for the IT industry right now. Technologies and techniques like Big data, Data science, Machine learning, and Deep learning, which are used in analysing vast volumes of data are expanding rapidly. To refine data analytics strategy and to be a successful data scientist, gaining deep insights of customer behaviour, and system performance is a must. So be on the apex with knowledge of latest data analytics trends for 2018.

With the world revolving around gadgets and technology, consumers desire spontaneous scope of entertainment. They want multi-sensory experiences, beyond sight and sound. Also, they don’t want to be restricted by criteria like venue or time for their entertainment and crave experiences that say something unique about them, which they can share with their friends and followers.

Source: www.springpeople.com