We have some intriguing news, The Ultimate CMO Challenge from the Delhi School of Economics, University of Delhi is being held and we are sponsoring the event. Participate at the Ultimate CMO Challenge if you a PG student or are pursuing a course in MBA.
The first round will be an online round where participants would have to submit their solutions online. Problem case will be sent to the teams that have registered for the challenge, the solution to be submitted should be of 6 slides except the cover slides. One can mail their solutions to cmo.atharva@mibdu.org
When Moore’s Law meets with modern day Business Intelligence, what happens? Disruption and then wider adoption!
With costs of implementing BI tools lowering, more and more enterprises are keen on jumping on-board the homebrewed variety of custom BI solution to help drive their business. The result of these efforts is that these days several organizations are pursuing data driven intelligent decision-making, at a cost, which is almost fractional compared to yesteryear’s Business Intelligence budgets.
A proper Big Data certification allows individuals to make the best of available smart BI solutions available out there!
But the question remains, as to are all these companies actually making better decisions?
Surely, most enterprises are now reaping the benefits of having a larger range of BI solutions available to them. Nevertheless, there is still a bigger room for error in the picture, which many firms tend to ignore.
If done right, BI solutions can deliver an ROI of USD 10.66 for the cost of every dollar spent on implementing them. But, as per a survey conducted by Gartner, the results are not so glorious for most firms. More than 70 percent of all BI implementations do not stand up to meet the business goals that were anticipated of them.
Is there a secret formula to BI solution driven success? Well, starting with asking the right questions is always a good place to begin:
Here are six steps that can tip the balance in your favour:
Which data sources to use?
Do you know what the lifeblood is for BI? Why, data of course, data is what Business Intelligence strives upon. All firms do have a rudimentary strategy to collect and analyze data, however, they tend to overlook the data sources. The key here to note is – truly reliable data sources are the main difference between the success and failure of your Business Intelligence efforts.
These data sources do exist; all you have to do is choose right. In addition, the best thing about them is a lot of them are almost free of charge. Using the good ones will transform the way you look at your market, the business pipeline and the way you perceive your audience.
Are you warehousing your precious data right?
These are your firm’s single source data repositories. Warehouses store all the data you collect from various sources, and provide the same for when needed, on prompt for reporting and analysis. However, self-service BI tools can be a bit of hit-or-miss at times, where consistently handling data is a worry.
The key is to discover a data warehouse solution, which can efficiently store, curate and retrieve data for analysis on prompt.
Are your analytics solutions good enough?
Companies that are looking to use their own Business Intelligence infrastructures must identify the analytics architecture that best suits their necessities. However, unwieldy datasets in combination with a lack of processing maturity can dull the effort even before one decides to start!
How does your BI solution integrate with the existing platforms?
For incorporating enterprise-scale Business Intelligence solutions, it is necessary to have it work effortlessly with the different other information formats, processes and systems, which have already been established previously in the internal work pipeline.
So, the key here is to ask the question – will the necessary integration cost more in terms of resources and effort that you can afford?
Use reporting mechanisms that are both powerful as well as easy to understand:
The most persistent challenge in BI is to wrangle data, majority of users cannot understand any of it beyond a simplified visualization. Decision-makers may be fooled with the help of powerful visualization tools. However, the truth is that making it pretty alone will not get the job done right.
So, forget pretty, and ask the all important question of whether the reporting mechanism is useful in interpreting otherwise unintelligible data or not.
Has better compliance enabled through your Bi solutions?
If your BI solutions, directly impinges on relevant regulations (and so it will, when the time comes). Then the solutions should aid the compliance and not hinder it. A good BI solution should provide a means to trace and audit data and its sources wherever, needed.
In conclusion: the success of your efforts will ultimately depend on the data.
The field of data science is evolving in expertise. And even professionals involved in the field tend to vary in their capabilities and opinions about the same. So, the important thing is to consider the importance of data in your company, and that one has all the appropriate responses to the posed questions above.
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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
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:
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.
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.
This is a new venture and earlier such advanced tools were only employed on corporate tax assessments.
When you work in the SEO and PPC industry it is a giveaway that you will be handing a large amount of data. While there are several ways you can utilize this data and manage it with Excel functions, and several tutorials are available online to talk about them. But what if you do not have the functions on Excel to do what you have to do with the data. You can use the Visual Basic for Applications (VBA) feature in MS Excel and write your own functions to help Excel carry out the functions that you want it to.
So, here in this advanced Excel trainingblog post, we will discuss about how to write a simple custom Excel function and will also give you readers some general advice on how to get started with Excel VBA.
Getting started with the Excel VBA editor:
First in order to work with the VBA editor in Excel, you must open a new Excel workbook or document and then press the following keys on your keyboard – ALT + F11. This will open a new window on the screen which is the VBE (Visual Basic Editor). This is where you can write your own Excel functions to use with the spreadsheet you have opened in your Excel document. This will be highlighted on the top left corner of the window. The project explorer pane will have the icons for each sheet of the document and another one for the whole of the workbook itself.Then for the next step, right-click on the ‘ThisWorkbook’, and then go to ‘Insert’ and then ‘Module’ options. That will further add a code module along with a container for the code which we will learn to write here.
Now you are ready to write your first Excel function:
Data analysis will help you analyze the keywords:
Each element of the data gathered through the SEO and PPC will often have keywords and phrases and this can give birth to a large amount of data for people to work with. For a recent piece of analysis, our faculty member was asked to find a method for counting the number of words in a search term. In this way single keywords can be dealt with differently in comparison to phrases. Like for e.g. ‘dresses’ can be treated with a stark difference to the term ‘red party dresses’. But there are often 100s or even 1000s of keywords to work with and it will be too time consuming to manually count the number of words in each phrase. Also there are no in-built functions in MS Excel to do so for us. Hence, we must use VBA to write new functions for us.
Adding the code:
Function countWords(phrase as string) as Integer
This will be the first line of the function you are about to write, start by copying it into the module we just created. Copy it under the words ‘Option Explicit’ which should be anyway entered (if it is not then do not worry, just copy it at the top and we can come to this later). This sentence however, has a lot of important things to tell us about.
Function: this first word itself tells us about which code is going to follow. A function is simply a piece of code that takes one or more values, performs something with them and then returns a different value. For instance, there is a built-in function with Excel called SUM. This function may take some input values and add them together to return a different value which is the sum total of the inputs. Similarly the function we create will take the keywords or phrases as an input and then count the number of words in them, then return a value for that number.
CountWords: we have put this as the name of our function. The moment we wish to use it, we can simply input into the spreadsheet cell the words as ‘countWords’. Just like we would add ‘SUM’ to use the sum function.
Phrases as string: this is the input will be the one to be entered when we will need to search a keyword or phrase.
As integer: this is the type of information which will be returned by the function. We are only interested in the number of the whole words in the phrases and hence are aiming to return an integer value.
How to prepare the function:
The next thing to do is to prepare the function by declaring the variables. Here we will declare the variables in ‘countWords’ as integers because it is built to only take integers. This will allow Excel to warn us if anything unexpected happens. For example, if we want to use a function to count the words in ‘red party dresses’ and it only returns with party. This will mean that something has gone wrong for sure. So, with declaration of the variable we will be able to let Excel know that it is not an integer and hence it will return with an error warning.
The variables we will use in this function are going to be called as ‘I’ and ‘counter’, however, there is no hard and fast rule to name your variables this way, you can name it the way you like. But ‘I’ is usually used as an abbreviation for index and counter will just be used as counter. The next step will be to add this line into your code.
‘Counts number of occurrences of space character in a phraseDim i as integerDim counter as integer: counter = 1
Note that ‘dim’ here is short for dimension. This describes the data type of a variable. We have told Excel through our codes that the variable ‘counter’ will always be an integer. We have also given the initial value as 1. But currently ‘I’ has no value assigned to it. The first line should appear green in the code window, this is mostly because of the apostrophe that precedes it. This line in our code is merely a comment and does not do anything within the code. It only exists as a label to let us know what the use of the code is for. It is a good practice to comment your code as otherwise it often becomes very hard to understand it otherwise. Also feel free to add in your own comments throughout to help understand and all you have to do for it is to add an apostrophe before it.
How can you count the words?
You must understand that Excel has no preconceived notions about what a word is. So to count them the concept has to be broken down for it to understand in a few short steps. One of the key features of a word is that it has a space either after or before or even at times in between it, and often both.
So we can start by simply telling Excel to count these spaces:
For i = 1 To Len(phrase)
If Mid(phrase, i, 1) = ” ” Thencounter =
counter + 1
End If
Next i
This is one of the key areas of this function, you must paste it or type it out in the code module. You can do so line-by-line as well. But here is an explanation of what is happening with each step:
For i = 1 To Len(phrase)
Here we have given ‘i’ a value, in fact not just one value but a range of values from 1 to Len (phrase). This is a built-in function with Excel that may return the number of characters (letters + spaces) in the phrase we pass it in. f Mid(phrase, i, 1) = ” ” Then
With this line of the code we are using the ‘Mid’ function in excel. This will ask excel to look into each character in the phrases in turn. This function takes 3 inputs which is – the phrases to be looked at, the character to begin comparison on, and the number of characters to compare with. We aim to compare every letter with one at a time approach. So, we would pass on ‘I’ and 1. And then finally the ‘If’ statement which says that if a character uses spaces, then excel should proceed to the next line of code. Or pass it over to the ‘End If’ statement.
counter = counter + 1
This line is only activated when a space is discovered. So, we increase our counter variables by 1 every time to count the number of spaces in the phrase.
End If Next i
With the above two lines, we are able to let Excel know where the If statement ends and to go back to the top and the start again for the next value of ‘i’. This is called as a ‘For Loop’ as we letting Excel know that it must repeat this task for a certain number of iterations.
There is also one last piece of code which we will make use of in order to handle a particular situation. When the phrase is passed in is blank. Then copy the following with what you already have:
If phrase = “” Then
countWords = 0
Else
countWords = counter
End If
Here is another statement that we have. If the phrase we input is blank, countWords takes the value 0, or else it will take the value of the ‘counter’ variable. After setting the ‘counter’ to 1 initially, we ensure the code will work for single words. However, it may also return 1 for blank phrases, and this prevents errors from occurring.
End Function
Finally with that we tell excel that we have finished defining our function. Here is the full code as mentioned below, check if yours looks the same or not:
Image Source: us.searchlaboratory.com
After you are done, you can close the VBE by clicking on the ‘X’ in the corner and then going back to the spreadsheet. Once done type in some words in a few cells and then type ‘countWords’ in another cell. And then click one of your cells containing the texts and then close the parenthesis. This cell can now contain the number of words in the cell that we have input. If it doesn’t, then we can set it to ‘automatic’ (Formulas > Calculations Options > Automatic in Excel 2010.
This simple function works best to save time as it can be dragged down over as many cells as you’d like, with hundreds of keywords and phrases. However, you must keep its limitations in mind. We are counting the spaces and not just words.
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While organizations are all words about having data driven decision making to drive their businesses, but maximum of business leaders seem to lack confidence in the information generated from data analytics. But in the rest of the world, demand for analytics training institute is on the rise with every passing day…
Data analysis is increasingly becoming central to decision-making in companies, especially in departments where people work towards increasing customer growth, improving productivity, and risk management. But although companies push to make their decision making process more data dependent, it seems business leaders are still more accustomed to taking serious business based on gut instincts and experiences. They still seem to have trouble trusting the insights shared from meticulous data analysis processes.Continue reading “Data-Analytics Driven Insights Still Distrusted By Executives!”
These days leveraging data be it big or small has become a powerful tool for all enterprises. IT firms are successfully transitioning to digital businesses and opportunities within the companies themselves are increasing to fulfil the growing demands.
So, if you want to join this megatrend in the job market, read on to find out the most in-demand data analytics job titles for today’s professionals:
Data scientist:
This job title has been getting a lot of attention since the past few years now. So much so, that even Glassdoor named it as the best career choice for optimum work/life balance. Their salaries are also comparatively higher.
But the field is still cloudy in terms of the job functions. So, let us understand what it actually means to be a data scientist.
According to Burch Works data scientists are people who “apply sophisticated quantitative measures and computer skills to both structure and analyze the massive amount of unstructured data sets or stream data continuously with an intention to derive information and prescribe action.
The executive recruiting firm says that the coding skills of these professionals are the main distinguishing factor that separates them from other predictive analytics professionals and allows them to exploit data regardless of its size, source and format.
These data professionals often have a master’s degree or a PhD in quantitative disciplines, such as applied math or statistics. They have expert skills and knowledge in statistical and machine learning methods and know tools like SAS, R etc. they are also proficient in other Big Data software like Hadoop and Spark.
Advanced analytics professional:
The professionals with this job role perform predictive analysis, prescriptive analysis, simulations, and all other forms of advanced analytics. Their role is however, significantly different from data scientists as they do not work with very large data sets and also not with unstructured data.
Data analyst:
A gamut of responsibilities fall under the job listings of a data analyst. They include ensuring data quality and governance, building different systems that enable businesses to gain user insights, performing actual data analysis and much more. However, the skill sets are similar and typically these professionals fit into the same category as advanced analytics professionals and data scientists, because they all can analyze data. But despite such similarities data analysts may be considered as more junior-level employees who are still in a way generalists and can fit into several different job roles within the organization.
Data engineers:
These are the wizards who work behind the scenes to make the jobs of data analysts and data scientists easier. They are technical professionals who have a deep understanding of Hadoop and other Big Data technologies like MapReduce, Hive, SQL and Pig, NoSQL technologies and other data warehousing systems.
Their primary job role is to construct the plumbing, build the data pipelines that clean, collect and aggregate data, organize it from different sources and then load them in data warehouses and databases.
Note that data engineers do not analyze data, but in other words keep the data flowing for processing so that other professionals can analyze them.
Business Analyst:
Business analysts can perform all the tasks that are almost the same for those who perform data analysis. However, business analysts generally have specialized knowledge of their specific business domain and then they apply that knowledge and analysis specifically for the business operations. For example, they may use their analytical skills to recommend improvement suggestions for the business.
Database Administrator:
These professionals are responsible for all things relevant to the operations, monitoring, and maintenance of the databases, often SQL or other relational database management systems also form their jurisdiction. Their tasks include installation, configuration, schemas definition, user training, and maintaining documents.
The database vendors like IBM, Oracle, Microsoft and others often offer certifications specific to their own proprietary technologies for such pros.
Business Intelligence professional:
BI professionals are responsible for adapting themselves with OLAP tools, reports and other data dashboards for looking at historical trends within data sets. Business Intelligence can have data visualization, and also include popular business intelligence platforms like Qlik, Tableau and Microsoft Power BI.
These were the most in-demand job titles in the data analysis industry, to help turn your career into the right direction take a look at our Big Data courses and have a job that you would thoroughly enjoy.
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In the IT world change is always in the air, but especially in the realm of data analytics, profound change is coming up as open source tools are making a huge impact. Well you may already be familiar with most of the stars in the open source space like Hadoop and Spark. But with the growing demand for new analytical tools which will help to round up the data holistically within the analytical ecosystem. A noteworthy point about these tools is the fact that they can be customized to process streaming data.
With the emergence of the IoT (Internet of things) that is giving rise to numerous devices and sensors which will add to this stream of data production, this forms one of the key trends why we need more advanced data analytics tools. The use of streaming data analysis is used for enhanced drug discovery, and institutes like SETI and NASA are also collaborating with each other to analyze terabytes of data, that are highly complex and stream deep in space radio signals.
The Apache Hadoop Spark software has made several headlines in the realm of data analytics that allowed billions of development funds to be showered at it by IBM along with other companies. But along with the big players several small open source projects are also on the rise. Here are the latest few that grabbed our attention:
Apache Drill:
This open source analytics tool has had quite good impact on the analytics realm, so much so that companies like MapR have even included it into their Hadoop distribution systems. This project is a top-level one at Apache and is being leveraged along with the star Apache Spark in many streaming data analytics scenarios.
Like at the New York Apache Drill meeting in January this year, the engineers at MapR system showed how Apache Spark and Drill could be used in tandem in a use cases that involve packet capture and almost real-time search and query.
But Drill is not ideal for streaming data application because it is a distributed schema free SQL engine. People like IT personnel and developers can use Drill to interactively explore data in Hadoop and NoSQL databases for things such as HBase and MongoDB. There is no need to explicitly describe the schemas or maintain them because the Drill has the ability to automatically leverage the structure which is embedded in the data. It is capable of streaming the data in memory between operators and minimizes the use of disks unless you need to complete a query.
Grappa:
Both big and small organizations are constantly working on new ways to cull actionable insights from their data streaming in constantly. Most of them are working with data that are generated in clusters and are relying on commodity hardware. This puts a premium label on affordable data centric work processes. This will do wonders to enhance the functionality and performance of tools such as MapReduce and even Spark. With the open source project Grappa that helps to scale the data intensive applications on commodity clusters and will provide a new type of abstraction which will trump the existing distributed shared memory (DSM) systems.
Grappa is available for free on the GitHub under a BSD license. And to use Grappa one can refer to its quick start guide that is available readily on the README file to build and execute it on a cluster.
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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.
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.
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