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Drawing a Bigger Picture: FAQs about Data Analytics

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Drawing a Bigger Picture: FAQs about Data Analytics

When the whole world is going crazy about business analytics, you might be sitting in a corner and wondering what does it all mean? With so many explanations, notions run a gamut of options.

It’s TIME to be acquainted with all the imperceptible jargons of data science; let’s get things moving with these elementary FAQs.

What is data analytics?

Data analytics is all about understanding the data and implementing the derived knowledge to direct actions. It is a technical way to transform raw data into meaningful information, which makes integral decision-making easier and effective. To perform data analytics, a handful number of statistical tools and software is used and et voila, you are right on your way to success!

How will analytics help businesses grow?

The rippling effects of data analytics are evident, from the moment you introduce it in your business network. And stop rattling! The effects are largely on the positive side, letting your business unravel opportunities, which it ignored before owing to lack of accurate analytical lens. By parsing latest trends, conventions and relationships within data, analytics help predict the future tendencies of the market.

Moreover, it throws light on these following questions:

  • What is going on and what will happen next?
  • Why is it happening?
  • What strategy would be the best to implement?

Also read: Tigers will be safe in the hands of Big Data Analytics

How do analytics projects look like?

A conventional analytics strategy is segregated into the following 4 steps:

Research – Analysts need to identify and get through the heart of the matter to help business address issues that it is facing now or will encounter in the future.

Plan – What type of data is used? What are the sources from where the data is to be secured? How the data is prepared for implementation? What are the methods used to analyse data? Professional analysts will assess the above-mentioned questions and find relevant solutions.

Execute – This is an important step, where analysts explores and analyses data from different perspectives.

Evaluate – In this stage, analysts evaluate the strategies and execute them.

How predictive modelling is implemented through business domains?

In business analytics, there are chiefly two models, descriptive and predictive. Descriptive models explain what has already happened and what is happening now, while Predictive models decipher what would happen along with stating the underlying reason.

Also read: Data Analytics for the Big Screen

One can now solve issues related to marketing, finance, human resource, operations and any other business operations without a hitch with predictive analytics modelling. By integrating past with present data, this strategy aims to anticipate the future before it arrives.

When should I deploy analytics in business?

An Intrinsic Revelation – Analytics is not a one-time event; it is a continuous process once undertaken. No one can say when will be the right time to introduce data analytics in your business. However, most of the businesses resort to analytics in their not-up-par days, when they face problems and lags behind in devising any possible solution.

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So, now that you understand the data analytics sphere and the significance attached, take up business analytics training in Delhi. From a career perspective, the field of data science is burgeoning. DexLab Analytics is a premier data science training institute, headquartered in Gurgaon. Check out our services and get one for yourself!

 

Interested in a career in Data Analyst?

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To learn more about Data Analyst with SAS Course – Enrol Now.
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Attributes of Effective Data Monetization Strategy

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Attributes of Effective Data Monetization Strategy

The saying goes – ‘necessity is the mother of all inventions’ and with the advent of globalization we have witnessed this aphorism in its sincerest form. A new wave of competition and profit generation owing to the advent of the internet, within the labyrinth of our society has led to the creation of Data at a scale previously unthinkable. To capture the essence of this huge amount of data, a new term Big Data, was coined which meant extremely large data sets, which are to be analyzed to reveal patterns that lie within.

Today, technology has become the backbone of the society and data is its vertebrae. The technological boom began and became common around year 2000; this is when data monetization became apparent.

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To simplify, data monetization is the act of generating revenue by exchanging, processing and analysis of data. Processing and analyzing means extraction of value from a particular set of data; eventually this value is to be interpreted to make decisions.

The need for data analysis is apparent since the digital universe is expected to grow 50-fold in terms of data by 2020, yet today only about 1% of the data is analyzed.

To capitalize on data monetization, we can employ the following approaches:

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  • An improvement in internal business processes – To locate synergy between different results, one result may provide some information, but coupled with another piece of result obtained, the synergistic outcome may be far more valuable.
  • Wrapping information around core products and services – This can be accomplished through understanding the target customer (analysis of their online presence can yield valuable information), and many companies are already indulging in these practices.
  • Trade of information to existing markets – This can often lead to be the most profitable of the three approaches, depending on the information, which it possesses.
  • Developing a technological structure – A technological infrastructure, capable enough to churn a real time data and provide real time results would be a boon to any business.


Already 70% of the large institutions purchase external data and monetization of the information asset is still in its infant stage. According to a study performed by Gartner, Data Monetization will be performed by 30% of the companies or more and in a survey conducted by IBM, data monetization was found to be among top 5 priorities of an organization.

The above clearly implies the upward trajectory growth in the near future in this industry, and with the application of the above-mentioned approaches, an effective strategy can be implemented by any organization hoping to be a part of the Data Monetization phenomenon.

Get the best Big data certification with our specialists at DexLab Analytics.

 

Interested in a career in Data Analyst?

To learn more about Machine Learning Using Python and Spark – click here.
To learn more about Data Analyst with Advanced excel course – click here.
To learn more about Data Analyst with SAS Course – click here.
To learn more about Data Analyst with R Course – click here.
To learn more about Big Data Course – click here.

Top Databases of 2017 to Watch Out For

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Data processing is the most talked about topic of this year. From the figure below, you can comprehend that NoSQL and SQL databases are the ones most preferred by the respondents. 

 
Top Databases of 2017to Watch Out For
 

By putting together the percentage of respondents who found them fetching and who called them ‘extremely engaging’, we can conclude who the runner-up is. Here, NoSQL databases secure the second rank with 74.8%.

Continue reading “Top Databases of 2017 to Watch Out For”

Sherlock Holmes Has Always Been a Data Analyst. Here’s Why

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The job of a data analyst or scientist revolves around gathering a bunch of disorganized data, and then using them to build a case through deduction and logic. Finally, following that you will reach a conclusion after analysis.

Sherlock Holmes Has Always Been a Data Analyst. Here's Why

Below quote from Sherlock Holmes is relevant –

“When you have eliminated the impossible whatever remains, no matter how Improbable it is must be the truth.”​

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He always started each case by focusing on the problem.

The problem would sometimes arrive in the form of a letter, sometimes as an item in the newspaper, but most often, it would announce itself by a knock at the door. The client would then present the mystery to Holmes and he would probe the client for salient information. Holmes never relied on guesswork or on assumptions. For Holmes, each new case was unique, and what mattered were reliable and verifiable facts about the case. These gave the investigation an initial focus and direction.

Deduction, Reasoning & Analytics

“It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts.”

Similarly a data analyst is expected not to assume or formulate theories, which can make the reasoning biased. In his stories, Sherlock Holmes demonstrates his keen powers of observation and deduction from data in front of him. He can decipher how the light enters in Watson’s bathroom based on how his beard is shaved; he attests one person has lived in China from one of his tattoos; he discovers previous financial situation of a man who he had never seen before just looking to the hat the man had just used.

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A data scientist has powerful computational and statistics tools that help him finding patterns amid so much data.

 

In the end, a data analyst’s introduction can be similar to what Sherlock said:

“My name is Sherlock Holmes. It is my business to know what other people do not

know.”

–Team Cosmos

You can learn more about Data analysis by taking up Data analyst certification courses. DexLab Analytics also offers Business analyst training courses.

 

Interested in a career in Data Analyst?

To learn more about Machine Learning Using Python and Spark – click here.
To learn more about Data Analyst with Advanced excel course – click here.
To learn more about Data Analyst with SAS Course – click here.
To learn more about Data Analyst with R Course – click here.
To learn more about Big Data Course – click here.

Harnessing Big Data for Water Management

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World Water Day: Save Water with Big Data

Appalling forces are re-establishing the relationship between humans and water.

In the past, communities developed slowly, while the weather remained constant. Water sources never depleted at tumultuous rates as it has today. Water is no longer a dependable resource. That’s why many countries and cities are embracing smart technologies to manage water efficiently and preserve it for the coming generations.

As we observe the United Nations World Water Day on Wednesday, 22nd March, it is apt to assess the development being made in conserving this diminishing resource.

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 Today, the Internet of Things (IoT) – a blooming worldwide network of devices and appliances linked to the internet – has materialized as a propitious solution to save water and protect clean drinking water, especially in cities.  

To begin our discussion, Netherlands is on its way to develop a pioneering program to address the relevant problems of increasing sea levels, surging number of droughts and the effect of extreme weather changes on its trains, bus networks and roadways, and the efficiency with which the entire nation tackles situations like this. The ambitious project, Digital Delta draws in local and regional water jurisdictions, top-notch scientists and proliferating businesses to implement Big Data technology for upgrading the systems of its €7 billion water management, while restricting the costs of preserving water by 15%.

Prophecies about Urban Centres
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Plummeting freshwater resources: a serious challenge faced by the global population is now at its apex. An overwhelming 89 percent of the world population thrives on enhanced water supply systems, which results in a loss of more than 32 billion cubic meters of fresh water, through physical leakage. Thereby, more than 50 percent of world population will be vulnerable in water-stressed regions by 2025. And by 2040, the figures will further push the energy demand by 56%, making US the second highest energy consumer across the globe.

Saving Water Globally

In the meantime, most of the world cities should re-invent and re-structure their assets to pull together advanced functions encompassing different complex systems and to associate with new powerful allies. Urbanization comes with its own costs. Day by day, these networks are growing more complicated and even more expensive. By delving deeper into the interconnections of systems, the societies will be in a better position to grasp how to run them more efficiently.

Water has never grabbed eyeballs, as it has today. Many countries are not at all prepared to manage such burgeoning complexities of water management. Besides, water management authorities are constantly under pressure to harness their power for flood protection and drinking water standards.

Reality Check: Water demand is set to rise by 30% by 2030. Ever increasing population and swelling urbanization are the reasons behind such calamitous figures.

Smart City Technology – The Key to Urban Sustainability

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New Jersey Institute of Technology (NJIT) revealed that by 2025 smart city technologies would multiply to an industry estimating $27.5 billion. Moreover, nearly 88 smart cities will develop by the end of 2025. Smart cities whirl around the concept of using improved, interconnecting technologies to make environment safe, lives easier and urban living cost-effective and more efficient.

Societies are enduring new weather extremes. It is the high time to use big data and analytical science to cure the growing complexities in managing our water systems. Smart technology is the only viable option that can take future generations towards a sustainable future.

Seeking data science courses online? Visit us at DexLab Analytics. We offer a wide array of highly interactive online courses in data science.

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

Knock! Knock! It’s Time to Change Your Bad Data Habits

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Knock! Knock! It’s Time to Change Your Bad Data Habits

Do you follow your instincts instead of data and insights?

Do you prefer storing data in different databases, in separate formats with varying values?

Habits are subject to change. Though it may take some time, but eventually it evolves. Good and bad habits make a person. Good habits don’t demand attention, but bad habits often need to be looked into.

If you suffer from bad data habits, then you must make sure you deal with it. It has to be a thing from your past rather than a dominating present. After all, data is incredibly important for business organizations to proliferate and generate decent revenues.

 

As per Experian’s Data Quality Report, 83% of companies consider their revenue suffers from inaccurate and insufficient customer data. It happens because of time and money wastage on insubstantial resources, which leads to a humungous loss of productivity and profit.

Bad Data Habits: The Ugly Truth

Data is the essence of business. From email delivery to customer feedback to profit generation, the impact of data trickles from strata to strata.

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Sadly, many companies fail to fathom the significance of data and continue storing data on multiple systems, instead of a single location, in various formats without actually knowing ways to handle it. This eventually results into huge data pile-ups, where the entire data silo becomes difficult to manage.

However, if you have the right tools and a zeal to ensure data quality, you can confidently manage your data, eradicate duplications and fix errors before they inflict damage to your fundamentals. Besides, prudent strategies, time-to-time reviews and absolute determination are necessary; read this article to gain more insights about how to work on your bad data habits.

Let awareness do the work

Detailed information about customers is crucial for better assistance and quicker efficiency. So, you should always tell your customer support team to derive more information about their customers in order to serve better.

Understand your data needs

What data is important for your business? Once you know that, you will be able to apprehend your customer’s needs and expectations more effectively. Moreover, be sure that the data is accessible to all those who really needs it, otherwise it won’t be fruitful.

Introduce Standardised Data Quality Policies

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For high quality data, make sure you introduce standard data policies and procedures. Also, ensure that the people working in your organization are acquainted with the ways of recording and storing it.

Initiate Regular Reviews

Data degradation is common. Human beings commit mistakes. Hence, it is important to regularly review and cleanse data in order to avoid future discrepancies.

Integration and Installation of the Right Tools

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Integrate your network to ensure the data is stored on one server, but accessible from multiple locations. This will help you get an entire picture of your company’s business performance over varied mediums. Install any of the improved Data Cleaning Software to make sure your data is free of duplicates and perfectly formatted right from the start.

 

To brush up your analytics skills, get enrolled in a Data analyst course. Visit DexLab Analytics.

 

Interested in a career in Data Analyst?

To learn more about Machine Learning Using Python and Spark – click here.
To learn more about Data Analyst with Advanced excel course – click here.
To learn more about Data Analyst with SAS Course – click here.
To learn more about Data Analyst with R Course – click here.
To learn more about Big Data Course – click here.

Take Small Steps With Big Feet of Business Analytics

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Take Small Steps With Big Feet of Business Analytics

Do these following questions clog your mind?

I aspire to become a business analytics professional, but I don’t know what skills to possess?

I am sceptical; which training should I opt for in order to establish my career in the sphere of business analytics?

I am looking forward to switch my career into data analytics, but I don’t know which skills to imbibe for better prospects?

Answer: Yes, they do.

Continue reading “Take Small Steps With Big Feet of Business Analytics”

Understanding The Core Components of Data Management

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Understanding The Core Components of Data Management

Ever wondered why many organizations often find it hard to implement Big Data? The reason often is poor or non-existent data management strategies which works counterproductive.

Data cannot be delivered or analysed without proper technology systems and procedural flows data can never be analysed or delivered. And without an expert team to manage and maintain the setup, errors, and backlogs will be frequent.

Before we make a plan of the data management strategies we must consider what systems and technologies one may need to add and what improvements can be made to an existing processes; and what do these roles bring about in terms of effects with changes.

However, a much as is possible any type of changes should be done by making sure a strategy is going to be integrated with the existing business process.

And it is also important to take a holistic point of view, for data management. After all, a strategy that does not work for its users will never function effectively for any organization.

With all these things in mind, in this article we will examine each of the three most important non-data components for a successful data management strategy – this should include the process, the technology and the people.

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Recognizing the right data systems:

There is a lot of technology implemented into the Big Data industry, and a lot of it is in the form of a highly specific tool system. Almost all of the enterprises do need the following types of tech:

Data mining:

This will isolate specific information from a large data sets and transform it into usable metrics. Some o the familiar data mining tools are SAS, R and KXEN.

Automated ETL:

The process of ETL is used to extract, transform, and also will load data so that it can be used. ETL tools also automate this process so that human users will not have to request data manually. Moreover, the automated process is way more consistent.

Enterprise data warehouse:

A centralised data warehouse will be able to store all of an organization’s data and also integrate a related data from other sources, this is an indispensible part of any data management plan. It also keeps data accessible, and associates a lot of kinds of customer data for a complete view.

Enterprise monitoring:

These are tools, which provide a layer of security and quality assurance by monitoring some critical environments, with problem diagnosing, whenever they arise, and also to quickly notify the team behind analytics.

Business intelligence and reporting, Analytics:

These are tools that turn processed data into insights, that are tailored to extract roles along with users. Data must go to the right people and in the right format for it to be useful.

Analytics:

And in analytics highly specific metrics are combined like customer acquisition data, product life cycle, and tracking details, with intuitive user friendly interfaces. They often integrate with some non-analytics tools to ensure the best possible user experience.

So, it is important to not think of the above technologies as simply isolated elements but instead consider them as a part of a team. Which must work together as an organized unit.

For business analyst training courses in Gurgaon and other developmental updates about the Big data industry, follow our regular uploads from DexLab Analytics.

 

 

Interested in a career in Data Analyst?

To learn more about Data Analyst with Advanced excel course – Enrol Now.
To learn more about Data Analyst with R Course – Enrol Now.
To learn more about Big Data Course – Enrol Now.

To learn more about Machine Learning Using Python and Spark – Enrol Now.
To learn more about Data Analyst with SAS Course – Enrol Now.
To learn more about Data Analyst with Apache Spark Course – Enrol Now.
To learn more about Data Analyst with Market Risk Analytics and Modelling Course – Enrol Now.

We are Proud to Host Corporate Training for WHO Reps!

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We are happy to announce our month-long corporate training session for the representatives of WHO, who will be joining us to discuss data analytics all the way from Bhutan. The team of delegates who have come to seek training from our expert in-house trainers are for the Central of Disease Control, Ministry of Health Royal Government of Bhutan.

 
We are Proud to Host Corporate Training for WHO Reps!
 

The training is on the concepts of R Programming, Data Science using R and Statistical Modelling using R, and will go on from the 8th of February 2017 to the 8th of March 2017. We are hosting this training session at our headquarters in Gurgaon, Delhi NCR. It is a matter of great pride and honour for the team of seasoned industry expert trainers at DexLab Analytics to be hosting the representatives from WHO.

Continue reading “We are Proud to Host Corporate Training for WHO Reps!”

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