Category Archives: Institute

New Survey Finds Increasing Big Data Adoption

New-Survey-Finds-Increasing-Big-Data-Adoption-01

NewVantage Partners have over the course of the last four years which has found that Big Data has quickly become the part and parcel of the day to day activities of most of Fortune 1000 companies.

The opinion of the crucial decision makers within organizations was taken into account. Financial led the way in investing in Big Date infrastructure. Also of note is the fact in the field of life sciences too usage of big data is increasing by the day.

Other findings of the survey are:

  • In 2012 just a small group of 5% of firms had in place a system of Big Data analytics which in 2015 rose to 63%.
  • In 2012 only 24% of firms claimed to have expectations to invest $10 million or more in Big Data by the year 2017 which rose to 63% in the year 2015.
  • At present a majority of 54% of companies have in place a Chief Data Officer which rose from 12% in the year 2012.
  • In 2012 only 21% of firms reported that their firms held Big Data to be critically important which rose to 70% in the last year.

During the starting year of the survey the executives of organizations were struggling to come in terms to properly perceive the impact and opportunity that Big Data might potentially hold. But today it has become the standard norm for corporate organizations and the focus is rapidly shifting to results produces and the business capabilities enabled by the same.

There is also a need to develop the appropriate metrics. While indeed the majority of Fortune 1000 companies’ implemented capabilities in Big Data, few have demonstrated the business value derived from the investments over time. Organizations with responsible executives for data who report to a Chief Financial Officer are often more likely to have developed financial measurements that are precise.

Innovation remains a source of much promise in the field of Big Data and innovation opportunities need to be identified. There is a dearth of success stories in innovation in things that are enabled by Big Data. Till now its success has largely been limited to savings on cost of operations or being able to analyze data sets that are larger or more diverse. Innovation in Big Data led applications need to be funded more and its practitioners need to exemplify imagination as well as boldness.

Businesses and the analytics industry needs to prepare for both business and cultural change as a new generations take their place in the workplace who have grown up on tools Like R Programming and Hadoop. There is great need for organizations to realize that Big Data is more about cultural change rather than solely technical change.

Big Data Certification

If you too want a piece of the bigger pie of Big Data as a practitioner, then you should try to train yourself for a promising career through proper Big Data Courses in Delhi as conducted by a leading institute like DexLab Analytics.

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DexLab Analytics Bags Fourth Spot in MS Excel course Rankings

Rank postDexLab Analytics as outperformed many of its more illustrious peers in India as it bagged the fourth spot in the rankings recently released by Analytics Vidhya. This comes close on the heels of the success enjoyed by MS Excel Dashboards Bootcamp organized by DexLab Analytics on the 26th of January.

Analytics Vidhya

Analytics Vidhya is a blog dedicated to the Analytics run and overseen by Kunal Jain who happens to be an aerospace engineering alumnus from IIT Bombay with more than six years of experience as Business Analytics. It aims at creating a vibrant and passionate community dedicated to analytics study and has enjoyed a considerable amount of success too in meeting its stated goals.

Ranking Criteria

The rankings of these courses have had four factors as their basis. They happen to be the Quality Score, Value Course, Coverage Score in addition to Industry Recognition. All of the courses examined, including the ones that were not ranked, were evaluated intensely on the basis of these factors. To delve into the details of the ranking basis:

  • The Quality Score(0.4): This score indicates several aspects that are inclusive of how well is the training material presented in addition to the support provided by the respective institutes for the candidates.
  • Value Score (0.2): The value score indicates the value for money as provided by that particular score.
  • Coverage Score (0.2)- The comprehensiveness of the material covered the course is taken into account in this part of the score factor.
  • Industry Recognition (0.2): This is representative of the recall and recognition of the training course platform or institute amongst professionals and employers. Institutes that have high recognition in the industry ate better placed to get placements for their students.

To Conclude:

As the review of the MS Excel Course included in the rankings concluded- “This course is best suited for candidates aspiring for MIS analyst roles.”

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What 2022 Will Look Like for Business Analytics

Business-Analytics
The market for Business Analytics stood at $42.55 billion in 2014 and is all set to touch $70.11 billion by the year 2022, globally. The industry will witness a CAGR of a considerable 6.44% during the period of 2014 to 2022 for which the forecast is made. The factors that are fuelling the growth of the market include rising demand for analytics by organizations as more and more organizations embrace Big Data, changing the environment in which businesses operate and the choices made by customers with unprecedented swiftness. The things that stand as an obstacle to its growth are factors like the relatively high costs of execution and a general unwillingness to adopt Business Analytics. Other hindrances are severe shortages of skilled workers who have the technical ability to run applications related to Business Analytics.

The lion’s share of the market has been captured by financial services, insurance and banking sector. The ten top vendors of business analytics software together constituted for 70% of the market share all over the world as of 2013. In the year 2013, SAP, Oracle, IBM and Microsoft together sold more than 50% of all sales of software related to business analytics. Tableau earned the distinction as being the fastest growing software company in the category of business analytics in the same year, witnessing a growth of 80% in a single year.

The global market for business analytics is segmented based on application, deployment, end users, software as well as geography. If deployment is considered the market is further segregated to cloud and on-site deployment. If the market is viewed from the perspective of the end user it may be categorized medium and small businesses and large enterprises. From the point of view of application the market for business analytics may be segmented into IT and telecom, media and entertainment, retail, healthcare, manufacturing, energy and power, government, banking, education, insurance and financial services.

According to software the business analytics market globally may be segregated into search and alter, performance and management of big data, predictive analytics, discovery of data, software for visualization, business intelligence and analytics of content. According to geography the markets are North America, Asia Pacific, Rest of the world besides Europe.

The key players in this market are INFOR, IBM, Microsoft, Oracle, Microstrategy Incorporated, Inc., SAS Institute, Tableau, QLIK Technologies and Tibco Software.

Business Analyst Certification Training

If you are interested in this field and are contemplating a career here it is highly advisable that you sign up for a Business Analysis Training in Delhi.

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Data Analytics is revolutionizing trading, are you prepared?

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If you still are unaware of the power of data analytics even after the success of the movie Moneyball, then you might as well be living under a rock. Yes the book by Michael Lewis transformed the way sports administrators can identify low-performing players by using data analysis rather than the conventional techniques. This was the first time something as analytical and core-academic was used in the field of sports and that too for something as substantial as selecting players.

But today data analysis is used in almost every aspect of our lives than just the way we like our favorite teams in popular sports. The basic idea behind statistical analysis is to determine meaningful patterns within a given set of data. This discipline is slowly edging its way into every conceivable business avenue these days whether big or small. The main elements in this field are data (that are available easily these days) and highly powerful computer programs that are used to analyze it and draw valuable insights from it. This field has completely changed the way we perceive information and make decisions. Historically these analysis tools have helped companies take decisions on how they should position themselves in new market ventures as well as place their new products.

For instance, ecommerce start-ups that house some of the smartest minds in the business are using expert statisticians to analyze Big Data with state-of-the-art data analysis tools to generate useful deductions from otherwise dull numbers and decimal points. Whether it is the heavy shifters in the logistics market or simply consumer durables and other fast selling products, companies are using data analysis to predict trends, deduce pricing patterns and are leveraging themselves with a revolutionary use of data analysis tools. Also in the sports world data analysis is behind big decisions like who to draft in a team to what should be the in-game strategy to marketing budget. Movie studios also employ analysis tools to calculate box-office generations and to even predict which movies will be a hit and which ones will be a flop.

Therefore, it is highly unlikely that data analytics would not make its way into the field that generates the highest number of data i.e. finances. The worlds of finances are all about numbers and so data analysis is an integral tool that has been used as an operational instrument in this field for a long-time now. While data analysis has been used in the financial world for several years globally, it is still fairly new to the Indian markets. Until now Indian markets only used data analysis tools in brokerage firms for slicing orders and for minimizing the impact cost of sales and purchases. But data analysis can also be used for mitigating frauds in money laundering, risk analysis and management and for rogue trading.

But currently the stock market is also on the verge of boarding the Big Data analysis train which is known as ‘Quants’ amongst the industry insiders. The basis of decision making in the stock trading market holding the hands of Data Analysis is to choose on the basis of history repeats itself theory. So the analysis tools are used more for technical analysis. The trend of analyzing data on the verge of the result season is slowly transforming as realizing valuable insights and patterns in data has somewhat a knee-jerk effect for the analysts and experts associated with the field. Hence, it is understandable that data analysis is making its way into the core practices of how we trade finances. So, it is understandable that the new key player in the market is data analysis or in other words also known as power computing. As the market experts have realized that using data analysis tools with precision will only increase their trading capabilities while enriching their funds in the process, so it is highly recommended for market participants to join the coveted team of data analysts to harness the true potentials of data.

In India the leading data analyst training institute in Delhi, NCR is DexLab Analytics which offers industry-oriented training courses in data analysis, Big Data Hadoop and other statistical analysis tools used in the market currently.

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The most asked career questions in Business Analytics

Business-Analytics-01We at DexLab Analytics conducted a survey, where we asked some of the tech savviest professionals associated with the ITES/Telecom/Marketing/BFSI fields, if they look to change their line of work in the near future and if they do, what will it be? Most of their response was to get into – Business Analytics.

But we have also noticed that although many are ready to take up Business Analytics for their career, when the time comes to take the big leap they are frazzled with a million questions that leave them confused. So, to help give such professionals some direction while taking such important decisions, here we have answered some of the most commonly arising question in the minds of students.

Firstly let us start with the very basics of the subject. What essentially is Business Analytics? Although this question has been answered by us numerous times; to put it simply it is the logical way we make decisions or the way we use logic.

For better understanding imagine a scenario, you are at the mall for shopping for a new dress. Some stores at the mal are offering a sale while some aren’t. And like all malls this one also has diverse range of retailers from budget-grade to superior high-quality ones. Now how do you decide on a dress?

You and almost everyone else take a decision based on the available information or data. How do you find data? You gather them by perusing around the mall visiting different shops and looking at the available options. But what if all the data is available, are there any risks associated with available data then? Is decision making completely risk-free? In such a scenario, even if information or data is available to us, they are usually not very conclusive or not present in their entirety. So, it might help to analyze the data to reduce the risk of taking wrong decisions.

While this might seem like an overly complex process and unnecessary burden to take care of when just shopping for a plain old dress, but imagine making high-stake decisions in similar or even more complex settings at business. Sounds daunting don’t it? That is the main use of Business Analytics. And as long as the mountainous heap of data keeps growing in the industry, the work of a Business Analyst will not dry-out.

The important tools in business analytics today are:

  1. SAS
  2. R programming
  3. Advanced MS Excel (using macros and VBA)
  4. Tableau/Spotfire/Qlikview etc.

Where can you learn Business Analytics?

The analytics training institutes in Delhi are the leading organizations in this sector much like B-schools. And with the real estate boom in the NCR regions of Gurgaon many reputed organizations are expanding their chains to such locations. DexLab Analytics is a premium analytics training institute that caters industry-specific training to data driven minds with the best in class faculties.

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R programming language 101: introductions and definitions

R-programming-language

Many programmers suggest that while they have developed software professionally in a plethora of programming languages, but the hardest language they have come across was R. While this statement may be debatable dividing the software developers’ community in the middle, as many others say that language is fairly easy to cope with. While the language may seem somewhat unconventional to learn initially, it is due to these factors that one with experience in languages like Java, Perl and C++ etc find it easier to handle. It has been developed keeping their abilities in mind.

What truly makes R programming stand-out from every other language is the fact that it is not just a programming language but also an environment for carrying out statistical analysis. Many experts suggest that they like to think that R is more of an environment consisting of a programming language component within that it being a programming language.

Most job sites these days are teeming with vacancies for R programmers, so it is highly recommendable to aspiring professionals to board the R train with a well-recognized R programming certification course.

When speaking about R programming it is safe to say, that is more like a scripting language for the R environment on similar lines as VBA is for MS Excel. This way some of the unconventional aspects of R can be explained when viewed in this perspective.

Understanding ‘sequences’ in R programming:

The reason behind using the expression seq(a, b, n) is used is to create a closed interval that starts from ‘a’ ends at ‘b’ and runs with step sizes of ‘n’. Taking a more realistic example, if we implement seq(1, 10, 3) returns with the following vectors – 1, 4, 7 and 10.

This command is somewhat similar to the range(a, b, n) in Python, except in Python only half-open intervals are used so, the vector 10 would not be included which was returned in case of the R example. The default step size augments in case of both R and Python is 1.

Boolean operators used in R:

The Boolean operators used in R are T or True for true values and F or False for false values.

As for the operators & and |, they are applied on the vectors element-wise. Conditional elements use && and || and they use lazy evaluations like in C. in such cases the operators do not use the second augment if the first augment works to determine the return value.

For more information on R programming we suggest that you take up a simple R programming course in Delhi, where the best professional courses are stationed at.

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Big Data and Analytics trends of 2015

The best way to begin speaking about how interesting the past year was for Big Data is to say that – there will be more.

Big-Data-and-Analytics-trends-of-2015

By more we mean that there will be more data, thus, there will be more data scientists, data analysts, more cloud analytics, more mobile analytics and data discovery. So, it is evident that Big Data will only get Bigger this New Year on 2016 with more data to visualize.

But if you are new in the world of Big Data Hadoop courses and are only beginning to acquaint yourself with Big Data, here are a few special trends of 2015 that you must keep a close eye on, this year.

You can make magic:

The technologies we use today are nothing short of sheer magic that, we could only imagine in our wildest dreams even a few years ago. But in the field of Big Data Hadoop and other data analytics tools, nothing such amazing has been noticed yet. We only know of tools and features that get the job done with some difficulty, but nothing that gives us data magic on our very fingertips. Thus, the world of data science still requires shepherds who would oversee and micromanage each and every step of data analysis. We still need people to manage aspects like – where to find data, how it should be stored, how it should be stored and most important how to analyze it and what conclusions to draw from the analysis.

Experts suggest that all this would soon change with advanced technologies being incorporated into the world of analysis to further automate the process. Things like machine learning with other advanced analytics tools are being applied to data science to further upgrade the process.

Insiders now want advanced tools that help them take care of the trivialities fast and get to analysis sooner with facilities like – simply pointing them at the data and allowing the algorithms to figure out things like how to join the data, propose complementary data, and cleanse it and to optimize it to determine it should be stored. Thus, 2016 will see more developments in this aspect of advanced automation.

Make way for multi-polar analytics:

Nowadays the layer cake approach of model analytics is slowly becoming obsolete and a practice that was thought of as the best – with external data feeding data marts and warehousing data with Bi features, as the final touch is fast being replaced with a multilayered or multi-polar approach. So, this New Year we will gain a better idea of how we can get better results from realistic yet complex data analysis centres.

Data privacy laws:

Currently there is a huge room for development in the world of data science contrary to the rate of development in the technologies. Already there have been reports of serious abuses with important data and we expect that 2k16 will see some strict actions in encryption practices in data.

In conclusion, 2016 will be a wonderful year for data analytics with increased adaptability.

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Things to judge in SAS training centres

If you are new to the glittering world of the corporate community, you may have come across the words like data analysis coaching, SAS training centres and may even know a few of your colleagues or batch-mates who have already enrolled with such institutes to make their resume look more impressive and smoothen the path to that long awaited promotion they have been working hard for.

SAS-training-centres

But why is SAS training necessary? And how would it impact your business?

The fact that knowledge in the field of data analysis or SAS could be important for the development of your company is nothing short of an understatement. It is a no-brainer that no commercial firm can hope to survive in the market without having their data analyzed. If you still find yourself ambitious in giving it a shot, we suggest you imagine the following two scenarios:

  • Imagine you have a pharmaceutical company, and are working on a new drug that can potentially cure cancer. And you are currently running drug trials on say, more than 500 patients. This massive bunch of patients will generate a huge bulk of data.
  • There may be a fruit juice company with plans to expand their flavor options by launching a new flavor of fruit juice. In order to test the markets, and to gauge the procurability of this flavor they are running a survey to determine the profitability linked to this flavor. This would generate a huge list of data to keep track of at all times.
  • The sales director of your company is aware of discrepancies with a certain popular product; it is a serious situation with the company reputation and brand value at stake. But he has not initiated a market research analysis program to enable him to draw valuable conclusions. What will happen?

The above sample scenarios are strong enough testimonies to the fact that market research and data analysis form the backbone of all companies. Hence, it is apparent that skills in data analysis tools such as SAS knowledge is an invaluable skill in any intelligent professional.

What a standard SAS teaching institute should provide in their curriculum?

  • How to automate work using MS Excel
  • How to create visually pleasing dashboards to make your webpage more appealing to potential leads
  • Understanding Logistic regression and Ace Learning
  • Perfecting SAS software understanding and R skills

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Is Predictive Analytics the Future of Big Data?

Is Predictive Analytics the Future of Big Data?The analysis of big data has for long been the cornerstone that technologists and futurists swore by in the recent past.

Enterprises look forward to this analysis as a source of valuable insights the operations related to their business as well as finding correlations within revenue and the activity conducted in the field of sales and marketing.

Frameworks like Hadoop are not only open source but also make data storage more effective both in terms of analytics tools as well as costs associated with the storage of data.

Situation Set To Change

But the prevailing situation is in for some serious change as large data set analysis undergoes a paradigm shift from what happened or is happening to what will happen. Machine learning, the concept of cloud computing as well as technologies that work in-memory all are contributing to this shift.

This shift is termed predictive analysis and promises to be the next big thing in the exciting world of Big Data. With it, Enterprises shall no more be confined to insights gained through analysis of data and react according to it but will be able make pretty accurate forecast of the state of their business a month, week or hours down the line through an effective combination of historical, third party and real time data.

This in turn will allow prompt action on potential problems like failure of machinery or depletion of stock and also to cash in on euphoric or depressed moods common after sporting events. However the focus should be on action than mere forecasts.

The Impact on CRM

CRM or customer relationship management software is a key area where predictive analytics can prove to be of invaluable help. Here predictive analysis will help marketers and sales people to be aware of the impact that their activity will potentially have and also to be able to provide content and pitches that are more personalized. This option is far better than simply to rely on the historical data about interactions made previously.

Inside Sales is a IT specialist in analytics and machine-learning and is one of the companies that is making use of this concept by not limiting itself to analytics but extending to the sphere of predictive analytics with a service based in the cloud.

Their system takes into account data from CRM platforms like Microsoft Dynamics and Salesforce and then proceeds to analyse the same against sales interactions that are suitably anonymized and are in tune of over 100 billion in number from the breadth of its worldwide customer base.

Why It Is Superior

This results in the predictive analytics that is both more effectively enhanced and expanded. This is due to the fact that the data possessed by the company is compared to an aggregated data mass.

When put into practice a company is able to figure out its performance vis-à-vis global and regional trends in sales in this particular sector and to find out key influences like economic factors, current affairs and maybe even the weather on its sales.

You have to know clients as people if you need to win them and then you’ll make more intelligent choices about their needs and practices.

The Workforce

Big Data training is essential for creating a workforce that can suitably match the demands of Big Data and its associated technologies and processes.

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How to Use Big Data for Advanced Customer Experience

How to Use Big Data for Advanced Customer Experience

With computerized information development anticipated to increment by 4,300% all over the world by 2020 and viable weights rising, organizations should now like never before convene the growing requests of their customers.

This digital upheaval is additionally giving remarkable chances to enhance the general client experience by means of big data analytics, as per a study conducted by Big Data Hadoop certification in Gurgaon. This is the procedure of gathering and deciphering these boundless amounts of information to separate the important, savvy, and helpful information that gives worth to a customer.

The following are 3 tips to utilize Big Data to improve general customer experience.

Actualize proactive bill shock administration

Bill shock is client agony from unforeseen allegations and is normally the consequence of broadband clients’ powerlessness to evaluate their huge information utilization, particularly while roaming. These disappointed clients can adversely affect the correspondence administration supplier’s repute and at last prompt income misfortune. Broadband organizations can stay away from this by giving continuous authorization activities and choices, through content warnings or email, permit free limited skimming, and divert clients to exchange information arrangements to dodge upcoming concerns.

Make more intelligent customized shopping encounters

Opt-in versatile showcasing correspondences of focused items and administrations can then be offered through customized messages particular to every phase of the purchaser cycle – mindfulness, engagement, thought, change and steadfastness. Suppose somebody selects to get promoting messages from a retailer who has an outlet in the neighborhood shopping center. GPS-incorporated tracking recognizes that the client is close to the store and sends the client an instant message alarming them to a unique one-day offer. With the client’s advantage provoked, she heads into the store and buys utilizing the coupon code as a part of the instant message.

Diminish holding up time in the line

A service organization, for instance, can deal with this perpetual agony of getting, as to orchestrate a home repair visit by getting the purchaser’s favored channel of correspondence, affirming the evening before in a mechanized way by means of that favored channel, and illuminating the client that the administration tech will call at 8:00 a.m. to tell the purchaser where he remains in the everyday line. This joys the client and disposes of the expense of up to three inbound telephone calls.

You have to know clients as people if you need to win them and then you’ll make more intelligent choices about their needs and practices.

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