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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?

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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.

Concocting Data with GIS

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Concocting Data with GIS

In supreme and sophisticated geospatial realm, data have been predominant. Or, should I say it is the matured fosterling of Geographic Information Systems (GIS). Choose, whatever suits you; subject to whom you work for or what you need to work on. The meat and potatoes? To excel on location analytics, concentrate only on the best most current data.

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In today’s world, data is valuable. It is vital and veritable. It is indispensable in Geographic Information Systems (GIS).

To second that, today’s tech-efficient society is anchored on location-based data, than ever, especially with the rise in Twitter, Google, Facebook and other social media apps, which collects and stores data from their highly-valued users to sell them off to money-grubbing advertisers.  Though secretly. On the other hand, cell phones go a step ahead in broadcasting your current location data 24/7. Otherwise, how would your friends know that you are safe when a severe earthquake rattled your neighbouring city! (Thanks to location settings)

Feisty Predicaments

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However, the real challenge lies in data identification and consumption. Countless number of users gets baffled when it comes to finding data, and if found, how to consume it to set off their business determinations. To solve this, many imminent think tanks of tech industry came out with direct and decisive solutions. Some of them were loaded with an abundance of data, i.e. digestible and disintegrated. By disintegration, they meant that the data was categorized into: points of interest, roads, boundaries and demographics, for easy comprehensibility. Furthermore, industry data bundles concerning telecommunications, retail and insurance fields were added to make the coverage global and profitable. To top it off, quality content and sprawling file formats boosted the results and mechanisms, both.

Conflux of GIS and BI

Location technology – Does this ring a bell? Yes? Then you would be familiar with GIS but others, particularly new Business Intelligence users and consumers must have just started taking baby steps on basic mapping. For BI, maps are the backdrop against which business analysts project their business data, stats and analytical information. Analysing the data to understand the insights of consumers is crucial, directly affecting the business decisions and revenues thereby. For example, heat maps, used to see the concentration of installations, customers and IoT devices provides an unparalleled accurateness of spatial relationships, which is impossible to obtain from the spreadsheets.


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One of the integral location analytics issues is to help in identifying the high-risk zones at the time of natural disasters, like tornadoes, earthquakes, floods, hurricanes or mudslides. For example, in the US, the East Coast is vulnerable to a lot of hurricanes and floods, whereas earthquakes and mudslides snap the West Coast time to time. Assessment of these location problems is intrinsically important for mortgage underwriters, insurance agents and public safety departments. And best data along with effective geo-coding is the solution to all the inconveniences. 

Discover easy Data Science Courses Online by logging in to DexLab Analytics. To know more on Business Analytics Online Certification, contact us.

 

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.

CRACKING A WHIP ON BLACK MONEY HOARDERS WITH DATA ANAYTICS

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Tax officials are tightening up their ropes with improved Big Data analytics to crack a whip on hoarders of black money.

 

  • Under the bill for amending Section 115BBE of the Income Tax Act, transactions with unexplained deposits in banks will be taxed.
  • As per this amendment, tax officials can now tax people on such deposits at a rate of 60 percent (cess additional) as opposed to the previously determined 30 percent.
  • This new tax law is applicable from the 1st of April, starting this year!

 

Cracking a Whip on Black Money Hoarders With Data Anaytics

Cracking a Whip on Black Money Hoarders With Data Anaytics

How are the Income Tax officials leveraging Big Data Analytics to curb black money?

Here are the simple signals that showcase a rise of Big data analytics use and a more planned crack down on Black Money hoarding:

 

  1. The IT department is now increasingly becoming tech savvy, it is now making use of analytics tools to assess the personal bank deposits for an improved black money crack down action plan.
  2. The income tax officials are making use of Big Data analytics tools for the first time ever done in the history of the Indian economy, to further maintain a hawk’s eye affixed on the target of bringing down black money.
  3. This is a new venture and earlier such advanced tools were only employed on corporate tax assessments.

Continue reading “CRACKING A WHIP ON BLACK MONEY HOARDERS WITH DATA ANAYTICS”

5 Analytics Tools To Improve Your Business Decisions

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5 Analytics Tools To Improve Your Business Decisions

Big Data has proved to be inevitable for business organisations in the quest for stepping ahead of their competitors. Nevertheless, only having Big Data at hand does not solve problems. You also need the availability of efficient analytics software that can put your data to the best use.

A business analytics tool is responsible for analysing massive amounts of data in order to extract valuable information. Such information in turn, can be used for improving operational efficiency and for taking better decisions.

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So, let us here go through the top 10 data analytics tools available in the market.

  • Yellowfin BI

Yellowfin Business Intelligence (BI) is a reporting, dashboard and data analysis software. The software is able to conduct analysis of huge amounts of database, in order to figure out appropriate information. With Yellowfin, your dashboard can be easily accessible from everywhere including company intranet, mobile device or web page.

  • Business Intelligence & Reporting Tools (BIRT)

BIRT is open source software programmed for JAVA and JAVA EE platforms. It consists of a runtime component and a visual report designer, which can be used for creating reports, visual data, and charts and so on. Information gathered from this software can be used for tracking historical data and analysing it and as well as for monitoring ongoing developments in various fields. BIRT can also be used for real-time decision-making purposes.

  • Clear Analytics

Clear Analytics is quite easy to manage as the software is based on Excel spreadsheets. While the software allows you to continue managing data using Excel, it also adds some extra features like reports scheduling, administrative capabilities, version control, governance etc. for better decision making. In short, Clear Analytics can be your choice in case you want high-end performance in exchange of minimal effort.

  • Tableau

Tableau is BI software that provides insight into the data that a business organisation requires for connecting the dots, in order to make clear and effective decisions. Data visualisation in Tableau is much dynamic and elaborative as compared to the other programmes available. Besides, it also provides easier access to data given its extended mobile device support. Additionally, the costs of implementing this program as well as its upgrade are relatively low.

  • GoodData

GoodData is a service BI platform. It takes into account both internal and external datasets (cloud) of an organisation to analyse and provide better governance. The platform is programmed for managing data security and governance thereby, consequently providing the user with the desired results. The most important feature of this platform is that it can analyse datasets of any size, thus making it effective for its users. Recently, the company rebranded their software as an Open Analytics platform.

These are some of the major analytics tools used by organisations irrespective of their scale in order to enhance their business intelligence. Whether you are looking to enhance your career or take better business decisions, a Data analyst certification course can help you to achieve such objectives. Data Analysis helps you to track the competitive landscape and figure out the essentials that needs to be done, in order to get ahead of your competitors. If you are a manager, you can take precise decisions based on quantitative data. Since big data is potential of driving your success, it is your job to master the science and use it for your advantage.

 

Interested in a career in Data Analyst?

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Understanding the Difference Between Factor and Cluster Analysis

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Understanding the Difference Between Factor and Cluster Analysis

Cluster analysis and factor analysis are two different statistical methods in data analytics which are used heavily in analytical methods of subjects like natural sciences and behavioural sciences. The names of these analytical methods are so because both these methods allow the users to divide the data into either clusters or into factors.

Most newly established data analysts have this common confusion that both these methods are almost similar. But while these two methods may look similar on the surface but they differ in several ways including their applications and objectives.

Difference in objectives between cluster analysis and factor analysis:

One key difference between cluster analysis and factor analysis is the fact that they have distinguished objectives. For factor analysis the usual objective is to explain the correlation with a data set and understand how the variables relate to each other. But on the other hand the objective of cluster analysis is to address the heterogeneity in the individual data sets.

Put in simpler words the spirit of cluster analysis is to help in categorization but that of factor analysis are a form of simplification.

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Difference is solutions:

This is not an easy section for drawing a line of separation in between cluster and factor analysis. That is because the results or solutions obtainable from both these analysis is subjective to their application. But still one could say that with factor analysis provides in a way the ‘best’ solutions to the researcher. This best solution is in the sense that the researcher can optimize a certain aspect of the solution this is known as orthogonality which offers ease of interpretation for the analysts.

But in case of cluster analysis this is not the case. The reasons behind that being all algorithms which can yield the best solutions for cluster analysis are usually computationally incompetent. Thus, researchers cannot trust this method of cluster analysis as it does not guarantee an optimal solution.

Difference in applications:

Cluster analysis and factor analysis differ in how they are applied to data, especially when it comes to applying them to real data. This is because factor analysis can reduce the unwieldy variables sets and boil them down to a smaller set of factors. This makes it suitable for simplifying otherwise complex models of analysis. Moreover, factor analysis also comes with a sort of confirmatory use researchers can use this method to develop a set of hypotheses based on how the variables in the data set are related.  After that the researcher can run a factor analysis to further confirm these hypotheses.

But cluster analysis on the other hand is suitable only for categorizing objects as per certain predetermined criteria. In cluster analysis a researcher can measure selected aspects of say a group of newly discovered plants and then place these plants into categories of species grouped by employing cluster analysis.

Here is an infographic to better explain the difference between cluster analysis and factor analysis: 

 

Interested in a career in Data Analyst?

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Aspiring Data Analysts Must Know the Answer to These Interview Questions

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Aspiring data analysts must know the answer to these interview questions

You have recently completed Data analyst certification and are hunting vigorously for a job as a data scientist. But the prospect of sitting for such an important job role at a corporate firm in front of a room full of C-suite interviewers is an intimidating prospect. But fear not as we at DexLab Analytics have got you covered both inside the class room as well out.

This megatrend on Big Data analysts started first in 2013, when the leading universities of the world began to realize the gap in between the demand and supply of Big Data professionals. And soon several , Data analyst training institutes cropped up here and there and rooms transformed into classrooms with several students being keen to learn about the steps to handle Big Data  and to join the ranks of data scientists which is a highly sought after profession of these days. Continue reading “Aspiring Data Analysts Must Know the Answer to These Interview Questions”

A few easy steps to be a SUCCESSFUL Data Scientist

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Data science has soared high for the past few years now; sending the job market into turbo pace where organizations are opening up their C-suite positions for unicorns to take their mountainous heap of data and make sense of it all to generate the big bucks. And professionals from a variety of fields are now eyeing the attractive position of data analyst as a possible profitable career move.

We went about questioning the faculty at our premiere data science and excel dashboard training institute to know how one can emerge as a successful data scientist, in this fast expanding field. We wanted to take an objective position from a recruiter’s point of view and create a list of technical and non-technical skills which are essential to be deemed an asset employee in the field of data science.

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A noteworthy point to be mentioned here is that every other organization will evaluate skills and knowledge in different tools with varying perspectives. Thus, this list in no way is an exhaustive one. But if a candidate has these songs then he/she will make a strong case in their favor as a potential data scientist.

The technical aspects:

Academia:

Most data scientists are highly educated professionals with more than 88 percent of them having a Master’s degree and 46 percent of them have a PhD degree. There are exceptions to these generalized figures but a strong educational background is necessary for aspiring data scientists to understand the complex subject of data science in depth. The field of data science can be seen in the middle of a Venn diagram with intersecting circles of subjects like Mathematics and Statistics 32%, Engineering 16% and Computer Science and Programming 19%.

Knowledge in applications like SAS and/or R Programming:

In depth knowledge in any one of the above tools is absolutely necessary for aspiring data scientists as these form the foundation of data analysis and predictive modeling. Different companies give preference to different analysis tools from R and SAS, a relatively new open source program that is also slowly being incorporated into companies is Hadoop.

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For those from a computer science background:

  • Coding skills in Python – the most common coding language currently in use in Python. But some companies may also demand their data scientists to know Perl, C++, Java or C.
  • Understanding of Hadoop environment – not always an absolute necessity but can prove to be advantageous in most cases. Another strong selling point may be experience in Pig or Hive. Acquaintance with cloud based tools like Amazon S3 may also be advantageous.
  • Must have the ability to work with unstructured data with knowledge in NoSQL and must be proficient in executing complex queries in SQL.

Non-technical skills:

  • Impeccable communicational skills so that data personnel can translate their technical findings into non-technical inputs comprehensible by the non-techies like sales and marketing.
  • A strong understanding of the business or the industry the company operates in. leverage the company’s data to achieve its business objectives with strong business acumen.
  • Must have profound intellectual curiosity to filter out the problem areas and find solutions against the same.

 

Interested in a career in Data Analyst?

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Trending Data Job Role: Chief Data Officer

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Trending data job role: Chief Data Officer

Financial firms are going berserk in order to employ the best Chief Data Officers from around the world. This is the new hype in the C-suite world who wants to manage risks associated with data and also grasp its opportunities for conducting better business.

These days all financial firms are sincerely focused on maintaining their data and governing them to comply with the latest rules and regulations. They want to comply with customer demands to maintain their competitive edge and stay on top of the game. And in order to maintain this, the financial services teams are on a hyper drive in hiring the C-suite role of a Chief Data Officer i.e. CDO.

Recent developments in the regulatory mandates of Volcker Rule of the Dodd-Frank Act in relation to capital planning have made it difficult for financial organizations to aggregate and manage their data. In a recent stress test a large number of major US corporate banks and other financial institutions have failed as the quality of their data was not up to scratch.

But expert data analyst and scientists state that only regulatory compliance is not the main issue at hand. Effective risk management goes hand-in-hand with efficient data management. And firms are lacking that front as they do not manage their data effectively and are simply gambling with chances of a hug penalty at the risk of losing customers and acquiring a bad name in the business.

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The opportunities in this position of Chief Data Officer:

While the aspects of regulatory compliance and risk management are becoming more and more complex every day, but that is not the only reason to move up information management positions and invite them into the boardroom. That is why as most financial organizations know that good governance requires strong data management skills with good understanding of architecture and analytics. Companies have come to realize that this kind of information can prove to be effective and provide them with competitive advantage in terms of reaching out to customers and protecting them with the offering of innovative products and services.

According to latest research, experts predicted that 25 percent of every financial organization will have employed a Chief Data Officer by the end of 2015. The job responsibility of this role is still clouded and most organizations are trying to refine and boil it down, but as of now three main roles have been identified – data governance, data analysis and data architecture and technology. While according to this survey 77 percent of the CDOs will remain focused in governance focused but their responsibilities are likely to grow into other areas as well. The main objective behind data architecture is to oversee how data is sourced, integrated and then consumed in the global organizations. The way to lead efficiencies in this respect is to consider this aspect in depth. Thus, it can be concluded that data analytics has the most potential.

For more details on Online Certificate in Business Analytics, visit DexLab Analytics. Their online courses in data science are up to the mark as per industry standards. Check out the course module today.

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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.

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