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Big Data Analytics and its Impact on Manufacturing Sector

Posted on December 29, 2016July 18, 2018 by Dexlab

It is no new news that the Big Data and software analytics have had a huge impact on the modern industries. There are several industry disruptors that have surfaced in the past few years and totally changed the lives of all connected humans. Like Google, Tesla and Uber! These are the companies that have used the long list of benefits presented to us by Big Data to expand and infiltrate newer markets, they have helped themselves improve customer relations and enhance their supply chains in multiple segments of market.

As per the latest IDC Research projects the sale of Big Data and analytics services will rise to USD 187 billion in 2019. This will be a huge leap from the USD 122 billion which was recorded in 2015. A major industry that stands to benefit greatly from this expansion which is the manufacturing industry, with the industry revenue projections ranging to USD 39 billion by 2019.

The industry of manufacturing has come a long way from the age of craft industries. But back then, the manufacturing process involved a slow and tedious production processes which only yielded limited amounts of products.

The effects of Big Data Analytics on the Manufacturing sector:

Automated processes along with mechanization have resulted in a generation of large piles of data, which is, much more than what most manufacturing enterprises know what to do with them.

But such data can yield beneficial insights for the manufacturing units to improve their operations and increase their productivity. Here are a few notable ones:

Image Source: mckinsey.com

Savings in cost:

Big data analytics can really help transform the manufacturing process and revolutionize the way they are carried out. The obtained information can be used to reduce the cost of production and packaging during manufacturing. Moreover, companies which implement data analytics can also reduce the cost of transport, packaging along with warehousing. This is in turn can help inventory costs and return i huge savings.

Improvement in safety and quality:

A lot of manufacturing companies are now making use of computerised sensors during the production to sift through low quality products while on the assembly line. With the right software analytics enterprises can use the data generated from such sensors to improve the quality and safety of the products instead of simply throwing away the low quality products after the production.

Image Source: blogs-images.forbes.com

Tightening up the workforce efficiency:

They can also use this data to improve management and employee efficiency. Big data analytics can be used to study the error rates on the production floor and use that information to analyse specific regions where employees are good when they perform under pressure.

Moreover, data analytics may help to speed up the production process n the production floor. S will be especially useful for large firms, which work with large volumes of data.

Better collaboration:

A great advantage of having an IT based data collection and analysis infrastructure is improved information movement within the manufacturing organization. The synergy of flow of information within the management and engineering departments as well as in the quality control sector and between the machine operators and other departments of the company helps them work more efficiently.

The manufacturing industry is much more complex than any other industry, which have implemented the big data analytics. Companies must effectively time the implementation of this software so that there are no losses. And should also pay attention as to from where they can mine the data and the right analytics tools to use for producing feasible and actionable results.

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Governance is Planning To Drive Compliance With GDPR

Posted on December 27, 2016December 12, 2019 by Dexlab

The companies’ data governance program is usually linked to the implementation of the General Data Regulation Program. And throughout time several articles have been written about to link the two initiatives but till none was so clearly distinct as the recent one on LinkedIn by Dennis Slattery. He made an analogy of a wedding between Governance and Privacy, which is very fitting but also highlights the fact that, a long term marriage with optimum success is based on the foundations that strengthen it with mutual efforts.

We can also take a similar message from the famous quote by Henry Ford – coming together is just the beginning, keeping together is actual progress, and working together is success.

Data analytics should tie its hands with privacy policies for a successful approach towards good business.

So, how can we make this marriage successful?

The GDPR regulation is quite clear on what it states, about things that must be done in order to protect the Data Citizen’s Rights. However, the bigger question most companies are facing is how to comply with regulations and/or go beyond the bare minimum and let GDPR work for them.

Majority of such discussions around the topic of how to implement GDPR today are focussed on one of two approaches – either top down or bottoms up. But we would argue otherwise, as these two approaches are not mutually exclusive and that a successful implementation of the GDPR must be based on a combination of these complementary approaches.

For the top down approach, the team for GDPR will reach out to the businesses to get a clear understanding of all business (data) processes, which involve either one or another. And for each of these processes like for third party credit checks, data analytics, address verification, and much more there are several attributes which must be clarified. Like for instance:

Have they acquired consent for the particular process?
What is the business purpose for the collection?
Who is the controller?
Who is the processor?
Who is responsible as the Data protection officer?
What is the period for retention of data?
What type of data is collected?
Along with several other information

However, it must be noted that this is not a one-time effort, once all the processes related to the personal data have been identified and classified they will still be needed to be maintained as the organization grows and evolves with development in its infrastructure over time.

The bottom up approach is a little more technical in nature. The businesses that have already established metadata management tools can then use these technologies to identify personally the identifiable information (PII) and then try and classify these data elements and assign the relevant attributes for GDPR. This approach shall quickly hit a bottleneck as the same data can be utilized for several business purposes and thus, cannot be classified for GDPR.

With successful implementation of the GDPR we will be able to marry both the approaches well.

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Why Getting a Big Data Certification Will Benefit Your Small Business

Posted on October 25, 2016December 19, 2017 by Dexlab

Do you know how much data is currently produced globally every year?

As per the reports published by IBM, the figures are 2.5 QB (Quintillion Bytes). The numeric representation of the same looks as: 2,500,000,000,000,000,000. And we thought that our mobile devices with 64GB memory space are capable of storing huge data.

Increasing reliance on Big Data

As technology is expanding at the speed next to light, more companies are planning to invest in Big Data platforms for getting the best out of it. Gartner Inc. had conducted a research recently among 437 global organisations across different industries and figured out that more than 75% of them are looking forward to the benefits they can derive from Big Data. The purpose for using Big Data varied to some instance across these organisations, however most of the companies were found to use data analytics for enhancing their customer service segments. Recently, security breach has hit the headline more often than global warming and that has been a factor of worry for many data driven companies. Thus, they are opting for Big Data tools in order to strengthen their online security. Continue reading “Why Getting a Big Data Certification Will Benefit Your Small Business”

High Demand for Data Scientist profiles in LinkedIn

Posted on September 28, 2016August 9, 2019 by Dexlab

Currently, Data Science experts are the most sought candidates in the world. According to a research report published by DJ Metrics, the number of ‘Data Scientist’ profiles in LinkedIn has nearly doubled over the last few years. At present, there are more than 11,400 data scientists on the professional networking website, out of which, 52% have added the particular job description (read Data Scientist) during the period between 2012 and 2015.

About the Research

DJ Metrics have taken into account 60,200 LinkedIn profiles of professional experts, while 27,700 records of Educational data and 254,000 records of skills sets were also used to conduct an analysis. Additionally, they have analysed the database of 6200 companies that have provided employment to the Data Scientists. The names of the Companies were collected by analysing the profiles of the Data professionals, since they have listed the names of their employers.

Great Career Opportunities

Researchers are forecasting that there will be a steady rise in the demand for trained Data Scientists, because of the increased adoption of Big Data and Business Intelligence by the leading global companies. High-end business organisations like Microsoft and Facebook are going through a continuous recruitment phase, as these companies had accelerated their hiring process by 151% and 39% respectively in 2014, as compared to what they had done in 2013.

According to the research report, about 65% of the total recruitments were carried out by the following industries:

Information Technology and Services, Internet and Computer Software Sector: 9%
Education: 3%
Banking and Finance: 2%
Marketing and Advertising: 2%

Big Data demands Bigger Skills

DJ Metrics has analysed the database of 254,000 skills in order to figure out the growth in the number of skilful Data Science professionals. The results are significant, as apart from the general ‘power’ skills; namely, Data Analysis, Analytics and Data Mining, the top skills found among the vast number of profiles included R, Python, Machine Learning, MATLAB, JAVA, Statistics and SQL. Surprisingly, the Chief Data Scientists are found to have the least technical skills, as only 27% of the profiles had listed Python, while 26% listed R as their technical skill sets. On the other hand, 52% and 53% Junior Data Scientists have listed Python and R, respectively.

Top Recruiters

If you see the chart above, you will see that Microsoft and Facebook are the top recruiters over the given period. Surprisingly, Google has not made it to the top 10, although it has recruited quite a number of Data Science professionals. The reason may be that the Data Scientists at Google are called ‘Quantitative Analysts’, which is probably used by their employees while listing their designation on LinkedIn. Since, LinkedIn has researched about the general Data Scientists; they may not have detected the alternate titles.

Countries with highest Data Scientist population

Almost 55% of the total Data Scientists in the world are currently located in the United States of America (USA), which makes the top of the list. The second country with maximum numbers of Data Science professionals is United Kingdom (UK), while the third position is occupied by India.

Are you interested in coveted data science online courses to upgrade your data science skill-set? Look no further than DexLab Analytics. They offer cutting edge Data Science training in Gurgaon for aspiring candidates.

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The Best Analytics Tools for Business And How to Make The Most of Them

Posted on August 10, 2016May 23, 2020 by Dexlab

All companies are awash with useable data about their customers, prospects and internal business operations as well as suppliers and partners. But most of them are also ill-equipped with the requisite understanding to leverage this streaming flood of data and cannot convert it to actionable insights to increase their revenue by growing their revenue thus, increasing their efficiency. Business intelligence tools are technology that allows businesses to transform their data into actions for generating better business.

The Business Intelligence and analytics industry has been around for decades now and is considered by most analytics personnel as a mature industry. But this BI market is never static with constant evolution and innovation to prepare for meeting the ever expanding needs of businesses of all sizes and from a diverse range of industries. So, it is imperative that people gather an understanding of the different Business Analytics tools for better operation of their companies.

Business Intelligence tools can be categorised in three different groups:

Guided analysis and reporting
Self-service Business Intelligence and Analysis
Advanced Analytics

The first category of guided analysis and reporting includes Business Intelligence tools of traditional styles that have long been used for years to perform recurrent data analyses of specified data groups. This system of data analysis was only used for predefined static reporting several years ago, but today it is possible for data analysts to select, compare, visualize and analyse data using various tools and features.

Tool styles in this category include the following:

Reports
Scorecards and dashboards
Spreadsheet integration
BI Search
Corporate Performance Management

The second category of BI tools which falls under the category of self-service BI and analysis includes the tools BI users utilize to make ad hoc analysis of data. Such analytical practices may be a one-time analysis or building of a recurring analytical system that may with shared by others.

Usually the users of such Bi tools have a dual role to play – consumer of information and producer of analytical systems. They usually share or publish their BI application which they build with the self-service BI tool. The users of such tools will always have the term analyst in their job title. Staff members of the management department may also make use of such tools when they need to perform similar tasks as that of a business analyst, for their peers even if their job title does not imply that.

The Business Intelligence tools include in this category includes the following:

Ad hoc analyses and reporting
OLAP cubes i.e. online analytical processing
Data visualization
Data discovery

The third category of advanced analytics includes the tools that a data scientist uses to build predictive and prescriptive models of analysis. These are tools for predictive modelling, statistical modelling and data mining along with rigorous use of big data analytics software. In these cases data analyst spend a huge chunk of their time performing tasks like data ingestion, cleansing and integration.

To understand the full spectrum of different Business Intelligence tool classes here is a visual explanation:

Who should invest in BI tools?

For a long time now investment and use of BI tools has been growing gradually regardless of the economic conditions. And it has especially accelerated in the recent times as companies crave for data for better growth and more organized operations. While data analytics tools were mainly associated with large enterprises due to their cost, complexity and demand of high skilled personnel, but those factors have now been grossly transformed as more and more SMBs (small and medium sized businesses) now being significant customers of BI tools and software.

Now that you have a good understanding of the different tool categories and how they should be deployed, the next step for you is to understand your company specific needs and make the best use of these tools that are optimized for so.

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Big Data Hacks: 5 Amazing Free Data Sources

Posted on May 17, 2016July 17, 2018 by Dexlab

With the data explosion revealing a continuum of numbers and facts and figures across the web and across businesses, it is of no doubt that data is omnipresent. But as the saying goes, sometimes it is hard to see the forest due to all the trees. A big myth among several companies is that they need to hire data analysts to look for their own data for analysis and to reap the benefits from Big Data analytics. But you must realize that this is far from the truth.

There are more than hundreds in fact even thousands of free data sets available for analysis and use for those who are smart enough to know where to look for them. Here is a list of 5 most popular free data set sources that are widely used globally. There are several more out there for those who are keen enough to look for them.

Data.gov: in compliance to the promise made by the US government last year all the government data is available for free on the internet in this site. The site is a useful source of information on everything starting from numbers in association to crime to climate change and much more.
Socrata: another great place to get scoop on the latest government related data along with some useful visualization tools that come built into the web portal.
org another place to access government data for free. One can get access to government data from the US, Canada, EU, CKAN and more.
World Health Organization data portal: a place to access all the statistics of hunger, health and disease of the world can be accessed here.
FaceBook Graph: FaceBook over the past few years has tightened their security and privacy settings. But there are still some amounts of data open to eyes without any privacy. And FaceBook provides information and access to all this data with their Graph API. While users may not be happy to share them with the world, they probably have not yet figured out how to hide them.

A bonus free data source that could also be fun to explore.

Face.com: get face recognition data with this fascinating tool and analyze possibilities like the creator.

These days a lot of forward thinking companies are trying to data driven, but they may not have ample resources to get their own data right away. So, it may be a good idea to begin with these publicly available free data sources. The best tip for data scientists is to learn to ask the right questions to get the right answers.

For more updates on big data hadoop training, follow DexLab Analytics. They are a premier big data training institution offering intensive career courses.

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The Worst Techniques To Build A Predictive Model

Posted on April 8, 2016May 23, 2020 by Dexlab

While some of these techniques may be a little out of date and most of them have evolved over time greatly, for the past 10 years rendering most of these tools completely different and much more efficient to use. But here are few bad techniques in predictive modelling that are still widely in use in the industry:

1. Using traditional decision trees: usually too large decision trees are usually really complex to handle and almost impossible to analyze for even the most knowledgeable data scientist. They are also prone to over-fitting which is why they are best avoided. Instead we recommend that you combine multiple small decision trees into one than using a single large decision tree to avoid unnecessary complexity.

Continue reading “The Worst Techniques To Build A Predictive Model”

How Can Big Data Impact the Lives of Students?

Posted on March 1, 2016May 11, 2019 by Dexlab

According to figures released by IBM opine that no less than 2.5 quintillion bytes created on a daily basis. Also it is worthwhile to note that a whopping 90% of the total data in the world have been created only in last two years.

In simple terms data is just pieces of information. The highly prominent concept of our times owes its origins to large data amounts which are derived from all sorts of computing devices. This data is then stored, collated and combined with the sophisticated tools for analytics available today.

Big Data is helpful to a broad spectrum of people from marketers to researchers. It helps them to understand the world around them and take optimized action through insights. Students too stand to benefit from Big Data a great deal and in this post we look at two ways through which Big Data may affect the lives of students.

It Helps To Be More Effective

Teachers have always been an informed lot, using data in order to optimize the practices and methods, Big Data facilitates the creation of far more powerful ways through which teachers and students may connect. As the focus shifts towards personalized learning, teachers are in a position to utilize more data than ever before.

This may be achieved through monitoring of study materials and how they are used by students in order to deliver more targeted instruction. With Big Data teachers will be able to better understand the needs of students and adapt lessons effectively and swiftly and in the end make decisions about enhanced learning for students, driven on the basis of data.

There is a Huge Demand for Data Scientists

Data Science was dubbed as the sexiest job of this century by Harvard Business Review and with good reason. People are just beginning to explore the possibilities enabled by Big Data and the need of skilled people in the field will only continue to increase in the years to come. Data Scientists have the ability to mine through data to the benefit of their employers including but not restricted to governments, businesses and of course, the academia.

McKinsey Global Institute reported that by 2018 there will be a shortage of no less than 190,000 persons with skills in deep analytics in the United States of America alone. There is no shortage for opportunities in this field and there are numerous programs all over the world that smooth out the career transition to Big Data. Work arrangements that display flexibility, more than decent compensation packages and the opportunity to make a significant impact are the added bonuses that go along being a data scientist.

We may conclude by saying that though Big Data is still emerging it held by most experts to be the undeniable future not only for those pursuing studies in data science and making careers in the field but to all the people whose lives are changed for the better through Big Data.

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How Data Scientists Take Their Coffee Every Morning

Posted on January 25, 2016July 10, 2018 by Dexlab

To a data scientist we are all sources of data, from the very moment we wake up in the morning to visit our local Starbucks (or any other local café) to get our morning coffee and swipe the screen of our tablets/iPads or smart phones to go through the big headlines for the day. With these few apparently simple regular exercises we are actually giving the data scientists more data which in-turn allows them to offer tailor-made news articles about things that interest us, and also prepares our favorite coffee blend ready for us to pick up every morning at the café.

The world of data science came to exist due to the growing need of drawing valuable information from data that is being collected every other day around the world. But is data science? Why is it necessary? A certified data scientist can be best described as a breed of experts who have in-depth knowledge in statistics, mathematics and computer science and use these skills to gather valuable insights form data. They often require innovative new solutions to address the various data problems.

Data Science: Is It the Right Answer? – @Dexlabanalytics.

As per estimates from the various job portals it is expected that around 3 million job positions are needed to be fulfilled by 2018 with individuals who have in-depth knowledge and expertise in the field of data analytics and can handle big data. Those who have already boarded the data analytics train are finding exciting new career prospects in this field with fast-paced growth opportunities. So, more and more individuals are looking to enhance their employability by acquiring a data science certification from a reputable institution. Age old programs are now being fast replaced by new comers in the field of data mining with software like R, SAS etc. Although SAS has been around in the world of data science for almost 40 years now, but it took time for it to really make a big splash in the industry. However, it is slowly emerging to be one the most in-demand programming languages these days.What a data science certification covers?

Tracing Success in the New Age of Data Science – @Dexlabanalytics.

This course covers the topics that enable students to implement advanced analytics to big data. Usually a student after completion of this course acquires an understanding of model deployment, machine language, automation and analytical modeling. Moreover, a well-equipped course in data science helps students to fine-tune their communication skills as well.

Keep Pace with Automation: Emerging Data Science Jobs in India – @Dexlabanalytics.

Things a data scientist must know:

All data scientists must have good mathematical skills in topics like: linear algebra, multivariable calculus, Python and linear algebra. For those with strong backgrounds in linear algebra and multivariable calculus it will be easy to understand all probability, machine learning and statistics in no time, which is a requisite for the job.

More and more data-hungry professionals are seeking excellent Data Science training in Delhi. If you are one of them, kindly drop by DexLab Analytics: we are a pioneering Data Science training institute. Peruse through our course details for better future.

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