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Introduction to MongoDB

Posted on February 17, 2021February 17, 2021 by Dexlab

MongoDB is a document based database program which was developed by MongoDB Inc. and is licensed under server side public license (SSPL). It can be used across platforms and is a non-relational database also known as NoSQL, where NoSQL means that the data is not stored in the conventional tabular format and is used for unstructured data as compared to SQL and that is the major difference between NoSQL and SQL.
MongoDB stores document in JSON or BSON format. JSON also known as JavaScript Object notation is a format where data is stored in a key value pair or array format which is readable for a normal human being whereas BSON is nothing but the JSON file encoded in the binary format which is quite hard for a human being to understand.
Structure of MongoDB which uses a query language MQL(Mongodb query language):-
Databases:- Databases is a group of collections.
Collections:- Collection is a group fields.
Fields:- Fields are nothing but key value pairs
Just for an example look at the image given below:-

Here I am using MongoDB Compass a tool to connect to Atlas which is a cloud based platform which can help us write our queries and start performing all sort of data extraction and deployment techniques. You can download MongoDB Compass via the given link https://www.mongodb.com/try/download/compass

In the above image in the red box we have our databases and if we click on the “sample_training” database we will see a list of collections similar to the tables in sql.

Now lets write our first query and see what data in “companies” collection looks like but before that select the “companies” collection.

Now in our filter cell we can write the following query:-

In the above query “name” and “category_code” are the key values also known as fields and “Wetpaint” and “web” are the pair values on the basis of which we want to filter the data.
What is cluster and how to create it on Atlas?
MongoDB cluster also know as sharded cluster is created where each collection is divided into shards (small portions of the original data) which is a replica set of the original collection. In case you want to use Atlas there is an unpaid version available with approximately 512 mb space which is free to use. There is a pre-existing cluster in MongoDB named Sandbox , which currently I am using and you can use it too by following the given steps:-
1. Create a free account or sign in using your Google account on
https://www.mongodb.com/cloud/atlas/lp/try2-in?utm_source=google&utm_campaign=gs_apac_india_search_brand_atlas_desktop&utm_term=mongodb%20atlas&utm_medium=cpc_paid_search&utm_ad=e&utm_ad_campaign_id=6501677905&gclid=CjwKCAiAr6-ABhAfEiwADO4sfaMDS6YRyBKaciG97RoCgBimOEq9jU2E5N4Jc4ErkuJXYcVpPd47-xoCkL8QAvD_BwE
2. Click on “Create an Organization”.
3. Write the organization name “MDBU”.
4. Click on “Create Organization”.
5. Click on “New Project”.
6. Name your project M001 and click “Next”.
7. Click on “Build a Cluster”.
8. Click on “Create a Cluster” an option under which free is written.
9. Click on the region closest to you and at the bottom change the name of the cluster to “Sandbox”.
10. Now click on connect and click on “Allow access from anywhere”.
11. Create a Database User and then click on “Create Database User”.
username: m001-student
password: m001-mongodb-basics
12. Click on “Close” and now load your sample as given below :

Loading may take a while….
13. Click on collections once the sample is loaded and now you can start using the filter option in a similar way as in MongoDB Compass
In my next blog I’ll be sharing with you how to connect Atlas with MongoDB Compass and we will also learn few ways in which we can write query using MQL.

So, with that we come to the end of the discussion on the MongoDB. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab Analytics DexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.

ARIMA (Auto-Regressive Integrated Moving Average)

Posted on January 27, 2021February 13, 2021 by Dexlab

This is another blog added to the series of time series forecasting. In this particular blog I will be discussing about the basic concepts of ARIMA model.

So what is ARIMA?

ARIMA also known as Autoregressive Integrated Moving Average is a time series forecasting model that helps us predict the future values on the basis of the past values. This model predicts the future values on the basis of the data’s own lags and its lagged errors.

When a data does not reflect any seasonal changes and plus it does not have a pattern of random white noise or residual then an ARIMA model can be used for forecasting.

There are three parameters attributed to an ARIMA model p, q and d :-

p :- corresponds to the autoregressive part

q:- corresponds to the moving average part.

d:- corresponds to number of differencing required to make the data stationary.

In our previous blog we have already discussed in detail what is p and q but what we haven’t discussed is what is d and what is the meaning of differencing (a term missing in ARMA model).

Since AR is a linear regression model and works best when the independent variables are not correlated, differencing can be used to make the model stationary which is subtracting the previous value from the current value so that the prediction of any further values can be stabilized . In case the model is already stationary the value of d=0. Therefore “differencing is the minimum number of deductions required to make the model stationary”. The order of d depends on exactly when your model becomes stationary i.e. in case the autocorrelation is positive over 10 lags then we can do further differencing otherwise in case autocorrelation is very negative at the first lag then we have an over-differenced series.

The formula for the ARIMA model would be:-

To check if ARIMA model is suited for our dataset i.e. to check the stationary of the data we will apply Dickey Fuller test and depending on the results we will using differencing.

In my next blog I will be discussing about how to perform time series forecasting using ARIMA model manually and what is Dickey Fuller test and how to apply that, so just keep on following us for more.

So, with that we come to the end of the discussion on the ARIMA Model. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab Analytics DexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.

Autocorrelation- Time Series – Part 3

Posted on January 22, 2021February 13, 2021 by Dexlab

Autocorrelation is a special case of correlation. It refers to the relationship between successive values of the same variables .For example if an individual with a consumption pattern:-

spends too much in period 1 then he will try to compensate that in period 2 by spending less than usual. This would mean that Ut is correlated with Ut+1 . If it is plotted the graph will appear as follows :

Positive Autocorrelation : When the previous year’s error effects the current year’s error in such a way that when a graph is plotted the line moves in the upward direction or when the error of the time t-1 carries over into a positive error in the following period it is called a positive autocorrelation.
Negative Autocorrelation : When the previous year’s error effects the current year’s error in such a way that when a graph is plotted the line moves in the downward direction or when the error of the time t-1 carries over into a negative error in the following period it is called a negative autocorrelation.

Now there are two ways of detecting the presence of autocorrelation
By plotting a scatter plot of the estimated residual (ei) against one another i.e. present value of residuals are plotted against its own past value.

If most of the points fall in the 1st and the 3rd quadrants , autocorrelation will be positive since the products are positive.

If most of the points fall in the 2nd and 4th quadrant , the autocorrelation will be negative, because the products are negative.
By plotting ei against time : The successive values of ei are plotted against time would indicate the possible presence of autocorrelation .If e’s in successive time show a regular time pattern, then there is autocorrelation in the function. The autocorrelation is said to be negative if successive values of ei changes sign frequently.
First Order of Autocorrelation (AR-1)
When t-1 time period’s error affects the error of time period t (current time period), then it is called first order of autocorrelation.
AR-1 coefficient p takes values between +1 and -1
The size of this coefficient p determines the strength of autocorrelation.
A positive value of p indicates a positive autocorrelation.
A negative value of p indicates a negative autocorrelation
In case if p = 0, then this indicates there is no autocorrelation.
To explain the error term in any particular period t, we use the following formula:-

Where Vt= a random term which fulfills all the usual assumptions of OLS
How to find the value of p?

One can estimate the value of ρ by applying the following formula :-

Time Series Analysis & Modelling with Python (Part II) – Data Smoothing

Posted on January 20, 2021January 20, 2021 by Dexlab

Data Smoothing is done to better understand the hidden patterns in the data. In the non- stationary processes, it is very hard to forecast the data as the variance over a period of time changes, therefore data smoothing techniques are used to smooth out the irregular roughness to see a clearer signal.

In this segment we will be discussing two of the most important data smoothing techniques :-

Moving average smoothing
Exponential smoothing

Moving average smoothing

Moving average is a technique where subsets of original data are created and then average of each subset is taken to smooth out the data and find the value in between each subset which better helps to see the trend over a period of time.

Lets take an example to better understand the problem.

Suppose that we have a data of price observed over a period of time and it is a non-stationary data so that the tend is hard to recognize.

QTR (quarter)	Price
1	10
2	11
3	18
4	14
5	15
6	?

In the above data we don’t know the value of the 6^th quarter.

….fig (1)

The plot above shows that there is no trend the data is following so to better understand the pattern we calculate the moving average over three quarter at a time so that we get in between values as well as we get the missing value of the 6^th quarter.

To find the missing value of 6^th quarter we will use previous three quarter’s data i.e.

MAS = = 15.7

QTR (quarter)	Price
1	10
2	11
3	18
4	14
5	15
6	15.7

MAS = = 13

MAS = = 14.33

QTR (quarter)	Price	MAS (Price)
1	10	10
2	11	11
3	18	18
4	14	13
5	15	14.33
6	15.7	15.7

….. fig (2)

In the above graph we can see that after 3^rd quarter there is an upward sloping trend in the data.

Exponential Data Smoothing

In this method a larger weight ( ) which lies between 0 & 1 is given to the most recent observations and as the observation grows more distant the weight decreases exponentially.

The weights are decided on the basis how the data is, in case the data has low movement then we will choose the value of closer to 0 and in case the data has a lot more randomness then in that case we would like to choose the value of closer to 1.

EMA= F_t= F_t-1 + (A_t-1 – F_t-1)

Now lets see a practical example.

For this example we will be taking = 0.5

Taking the same data……

QTR (quarter)	Price (A_t)	EMS Price(F_t)
1	10	10
2	11	?
3	18	?
4	14	?
5	15	?
6	?	?

To find the value of yellow cell we need to find out the value of all the blue cells and since we do not have the initial value of F₁ we will use the value of A_1.Now lets do the calculation:-

F₂=10+0.5(10 – 10) = 10

F₃=10+0.5(11 – 10) = 10.5

F₄=10.5+0.5(18 – 10.5) = 14.25

F₅=14.25+0.5(14 – 14.25) = 14.13

F₆=14.13+0.5(15 – 14.13)= 14.56

QTR (quarter)	Price (A_t)	EMS Price(F_t)
1	10	10
2	11	10
3	18	10.5
4	14	14.25
5	15	14.13
6	14.56	14.56

In the above graph we see that there is a trend now where the data is moving in the upward direction.

So, with that we come to the end of the discussion on the Data smoothing method. Hopefully it helped you understand the topic, for more information you can also watch the video tutorial attached down this blog. The blog is designed and prepared by Niharika Rai, Analytics Consultant, DexLab Analytics DexLab Analytics offers machine learning courses in Gurgaon. To keep on learning more, follow DexLab Analytics blog.

Time Series Analysis Part I

Posted on January 18, 2021January 18, 2021 by Dexlab

A time series is a sequence of numerical data in which each item is associated with a particular instant in time. Many sets of data appear as time series: a monthly sequence of the quantity of goods shipped from a factory, a weekly series of the number of road accidents, daily rainfall amounts, hourly observations made on the yield of a chemical process, and so on. Examples of time series abound in such fields as economics, business, engineering, the natural sciences (especially geophysics and meteorology), and the social sciences.

Univariate time series analysis- When we have a single sequence of data observed over time then it is called univariate time series analysis.
Multivariate time series analysis – When we have several sets of data for the same sequence of time periods to observe then it is called multivariate time series analysis.

The data used in time series analysis is a random variable (Yt) where t is denoted as time and such a collection of random variables ordered in time is called random or stochastic process.

Stationary: A time series is said to be stationary when all the moments of its probability distribution i.e. mean, variance , covariance etc. are invariant over time. It becomes quite easy forecast data in this kind of situation as the hidden patterns are recognizable which make predictions easy.

Non-stationary: A non-stationary time series will have a time varying mean or time varying variance or both, which makes it impossible to generalize the time series over other time periods.

Non stationary processes can further be explained with the help of a term called Random walk models. This term or theory usually is used in stock market which assumes that stock prices are independent of each other over time. Now there are two types of random walks:
Random walk with drift : When the observation that is to be predicted at a time ‘t’ is equal to last period’s value plus a constant or a drift (α) and the residual term (ε). It can be written as
Yt= α + Yt-1 + εt
The equation shows that Yt drifts upwards or downwards depending upon α being positive or negative and the mean and the variance also increases over time.
Random walk without drift: The random walk without a drift model observes that the values to be predicted at time ‘t’ is equal to last past period’s value plus a random shock.
Yt= Yt-1 + εt
Consider that the effect in one unit shock then the process started at some time 0 with a value of Y0
When t=1
Y1= Y0 + ε1
When t=2
Y2= Y1+ ε2= Y0 + ε1+ ε2
In general,
Yt= Y0+∑ εt
In this case as t increases the variance increases indefinitely whereas the mean value of Y is equal to its initial or starting value. Therefore the random walk model without drift is a non-stationary process.

So, with that we come to the end of the discussion on the Time Series. Hopefully it helped you understand time Series, for more information you can also watch the video tutorial attached down this blog. DexLab Analytics offers machine learning courses in delhi. To keep on learning more, follow DexLab Analytics blog.

Classical Inferential Statistics: Theory of Sampling (Part -1)

Posted on June 12, 2020June 12, 2020 by Dexlab

Contents:

Introduction
Basic Building Blocks of Classical Sampling Theory
Types of Sampling
Sampling Distribution: Overview
Conclusion

1. Introduction:

Predictive models are developed over a specific time period and on a certain set of records. However, implementation happens on a mutually exclusive time period (Out of Time Sample). Therefore, the models developed need to be trained and validated on different datasets: 1. Model Development Data (training data) 2. In sample validation data 3. Out of time validation data. A predictive model is considered to be robust, if their performance remains more or less stable in the out of time samples. An important observation from the description above is the following: The entire data (Population) is never accessible for model development and hence, is unknown. Models are developed on subsets (Samples) which are representative of the entire data. Representativeness of the samples are important to ensure the robustness in the model performance. This blog explores the key concepts related to creating representative samples from the population. Section 2 describes the basic components of the classical sampling theory, Section 3 describes the key types of sampling, Section 4 introduces the concept of Sampling Distribution and Section 5 concludes with the key summary of findings.

2. Basic Building Blocks of Classical Sampling Theory:

Introduction To Population and Sample

The two basic blocks of Classical Sampling Theory are: 1. Population 2. Samples. Populationis defined as the base of all the observations which are eligible to be studied to address key questions relating to a statistical investigation or a business problem, irrespective of whether it can be accessed or not. In real time the entire population is always unknown since there is a part of the population which cannot be accessed due to different reasons such as: Data Archiving Problems, Data permissions, Data Accessibility etc. A representative subset of the population is called a sample. The distribution of the variables in the sample is used to form an idea about the respective distribution of the variables in the population.

In a real time, any predictive modelling exercise uses the samples, since they cannot practically use the population. The population is not accessible because of the following reasons:

Observation Exclusions used in models: Observation Exclusions are used in predictive models to remove unnecessary observations, which are redundant for analysis. For example, when developing a credit risk model, observations which are bankrupts or frauds are removed from the analysis, since frauds and bankrupts are a part of operational risk.
Variable Exclusions used in the models: Variable Exclusions are used in predictive models to remove unnecessary variables which are redundant for analysis. For example, when developing a credit risk model, variables which are market-oriented variables or operational variables are excluded.
Robustness Check of the developed models: The developed models are validated on multiple samples such as In-sample Validation data, Out of Time Validation samples Therefore, only a fraction of the dataset is available for model development. Hence, the population is always unknown, irrespective of the datasets, and hence the key statistical distributions of the population are anonymous

Mathematical Framework To Describe The Sampling Theory Framework:

Let X be a N x k vector (where N = Total number of rows that the matrix has (observation) and k = Total number of columns (variables)) which is normally distributed with mean μ and variance The population mean μ and variance both are unknown numerical features of the population distribution. These are called the Parameters: A functional form of all the population observations.

The key objective of the Classical Sampling Theory is to provide the appropriate guidelines for analysing the Population parameters based on the statistical moments of the sample. The statistical moments of the samples are called Estimators. The Estimators are a functional form of all the sample observations. For example, let us assume a subset of size ‘n’ is extracted from X such that the sample S is a n x k vector which is normally distributed.are the sample means and the sample variance respectively. The descriptive moments are called statistics. A Statistic is an estimator with a sampling distribution. (Detailed Discussion: Section 4). The key objective of the classical sampling theory is to estimate the population parameters using the sample statistics, such that any difference between the two measures are statistically insignificant and considered to be an outcome of sampling fluctuations.

Classical Sampling Methods:

3. Types of Sampling

Broadly, there are two types of sampling methods discussed under the Classical Sampling theory: (i) Random Sampling (ii) Purposive Sampling. The different types of sampling and a brief description of each is provided in the figure below:

Applications Of Sampling Methods:

In the real time predictive modelling exercise, Stratified Random Sampling is considered to be of a wider appeal, than the Simple Random Sampling. Business datasets contain different categorical variables like: Product Type, Branch Size category, Gender, Income Groups etc. While splitting the total data into development data and Validation data, it is important to ensure that representation of the key categorical variables is made in the samples. This is important to ensure representativeness of the sample and robustness of the model. In this case a stratified random sampling is more preferred than the Simple Random Sampling. The use of Simple Random Sampling is limited to the cases where the data is symmetric and not much of heterogeneity is observed among the distribution of the values of the variables. The following examples discuss the applications of the Classical Sampling methods:

Example01: Splitting the Model Development Data into Training and Validation dataset

Models, when developed needs to be validated. The standard practice is to divide the data into 70% – 30% proportion. The models are trained on 70% of the observations and validated using the remaining 30%. To ensure the robustness of the model the distribution of the target variable should be similar in both the development and validation datasets. Therefore, the target variable is used as the Strata variable.

Example02: Boot Strapping Analysis

Boot Strapping Exercises exhaustively use Simple Random Sampling with Replacement. It is a nonparametric resampling method used to assign measures of accuracy to sample estimates.

4. Sampling Distribution: Overview

Sampling distribution of a statistic may be defined as the probability law which the statistic follows, if repeated random samples of a fixed size are drawn from specified population. A number of samples, each of size n, are taken from the same population and if for each sample the values of the statistic is calculated, a series of values of the statistic will be obtained. If the number of samples is large, these may be arranged into a frequency table. The frequency distribution of the statistic that would be obtained if the number of samples, each of the same size (say n), were infinite is called the Sampling distribution of the statistic. The table below shows a Sample Distribution and its associated frequency distribution:

5. Conclusion:

The blog, brought to you by DexLab Analytics, a premier institute conducting statistical analytics courses in Gurgaon and business analysis training in Delhi, introduces the basic concepts of Classical Sampling Framework. The objective here has been to explore the broad tenets of sampling theory, such as the different methods of sampling, their usages and their respective advantages and disadvantages. The Stratification Random Sampling is a more versatile sampling method compared to Simple Random Sampling methods. The concept of Sampling Distribution has been introduced but not discussed in details. This is to be the subject matter of the next blog: Sampling Distributions and its importance in Sampling theory.

Basic of Statistical Inference Part-I from Dexlab Analytics

Akash Dasgupta
Research Associate, DexLab Analytics

Business Intelligence Software in the Key for an Organization to Gain Competitive Advantage

Posted on April 24, 2018April 24, 2018 by Dexlab

Business Intelligence, or BI, is crucial for organizations as strategic planning is heavily dependent on BI. BI tools are multi-purpose and used for indicating progress towards business goals, quantitatively analyzing data, distribution of data and developing customer insights.

Advanced computer technologies are applied in Business Intelligence to discover relevant business data and then analyze it. It not only spots current trends in data, but is also able to develop historical views and future predictions. This helps decision-makers to comprehend business information properly and develop strategies that will steer their organization forward.

BI tools transform raw business information into valuable data that increase revenue for organizations. The global business economy is completely data driven. Companies without BI software will be jeopardizing their success. It is time to shed the belief that BI software is superfluous. Rather, it is a necessity.

Here is a list of 10 important things that BI solutions can help your organization achieve. After reading these you will be convinced that BI is vital in taking your business forward.

Provide speedy and competent information for your business

Nowadays, there isn’t much time to ponder over data sheets and then come to a conclusion. Decisions have to be taken on the spot. Valuable information doesn’t include business data alone, but also what the data implies for your business. BI gives you a competitive lead as it provides valuable information with the push of a button.

Provide KPIs that boost the performance of your business.

Business Intelligence software provides KPIs (Key Performance Indicators), which are metrics aligned with your business strategies. Thus, businesses can make decisions based on solid facts instead of intuition. This makes business proceedings more efficient.

Employees have data-power

BI solutions help employees to make informed decisions backed by relevant data. Access to information across all levels ensures company-wide integration of data. This helps employees nurture their skills. A competitive workforce will help a company gain global recognition.

Determine the factors that generate revenue for your business

Business intelligence is able to determine where and how potential customers consume data, how to convert them to paying customers, and chalk out an appropriate plan that will help increase revenue for your business.

Avoid blockages in markets

There are many BI applications that can be incorporated with accounting software. Business intelligence provides information about the real health of an organization, which cannot be determined from a profit and loss sheet. BI includes predictive features that help avoid blockages in markets and determine the right time for important decisions, like hiring new employees. Easy-to understand dashboards enable decision-makers to stay informed.

Create an efficient business model

As explained by Jeremy Levi, Director of Marketing, MarsWellness.com, ‘’ Why is BI more important than ever? In one word: oversaturation. The internet and the continued growth of e-commerce have saturated every market…For business owners, this means making smart decisions and trying to know where to put your marketing dollars and where to invest in infrastructure. Business intelligence lets you do that, and without it, you’re simply fumbling around for the light switch in the dark.”

Improved customer insights

In the absence of BI tools, one can spend hours trying to make sense out of previous reports without coming to a satisfactory conclusion. It is crucial for businesses to meet customer demands. BI tools help map patterns in customer behavior so businesses can prioritize loyal customers and improve customer satisfaction.

Helps save money

BI tools help spot areas in your business where costs can be minimized. For example, there is unnecessary spending occurring in the supply chain. BI can identify whether it is inefficient acquisition or maintenance that is translating to increased costs. Thus, it enables businesses to take the necessary actions to cut costs.

Improve efficiency of workers

Business intelligence solutions can monitor the output of members and functioning of teams. These help improve efficiency of the workers and streamline the business processes.

Protects businesses from cyber threats

Cyber crimes like data breaches and malware attacks are very common. Cyber security has become the need of the hour. Businesses should invest in BI solutions equipped with security tools that help protect their valuable data from hackers and other cyber attacks.

Businesses will progress rapidly through the use of smart BI solutions. Organizations small or big, can use BI tools in a variety of areas, starting from budgets to building relationship with customers.

If you want to empower your business through BI then enroll yourself for the Tableau BI certification course at DexLab Analytics, Delhi. DexLab is a premium institute providing business analysis training in Delhi.

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.

How Credit Unions Can Capitalize on Data through Enterprise Integration of Data Analytics

Posted on April 20, 2018May 23, 2020 by Dexlab

To get valuable insights from the enormous quantity of data generated, credit unions need to move towards enterprise integration of data. This is a company-wide data democratization process that helps all departments within the credit union to manage and analyze their data. It allows each team member easy-access and proper utilization of relevant data.

However, awareness about the advantages of enterprise-wide data analytics isn’t sufficient for credit unions to deploy this system. Here is a three step guide to help credit unions get smarter in data handling.

Improve the quality of data

A robust and functional customer data set is of foremost importance. Unorganized data will hinder forming correct opinions about customer behavior. The following steps will ensure that relevant data enters the business analytics tools.

Integration of various analytics activity- Instead of operating separate analytics software for digital marketing, credit risk analytics, fraud detection and other financial activities, it is better to have a centralized system which integrates these activities. It is helpful for gathering cross-operational cognizance.
Experienced analytics vendors should be chosen- Vendors with experience can access a wide range of data. Hence, they can deliver information that is more valuable. They also provide pre-existing integrations.
Consider unconventional sources of data- Unstructured data from unconventional sources like social media and third-parties should be valued as it will prove useful in the future.
Continuous data cleansing that evolves with time- Clean data is essential for providing correct data. The data should be organized, error-free and formatted.

Data structure customized for credit unions

The business analytics tools for credit unions should perform the following analyses:

Analyzing the growth and fall in customers depending on their age, location, branch, products used, etc.
Measure the profit through the count of balances
Analyze the Performances of the staffs and members in a particular department or branch
Sales ratios reporting
Age distribution of account holders in a particular geographic location.
Perform trend analysis as and when required
Analyze satisfaction levels of members
Keep track of the transactions performed by members
Track the inquires made at call centers and online banking portals
Analyze the behavior of self-serve vs. non-self serve users based on different demographics
Determine the different types of accounts being opened and figure out the source responsible for the highest transactions.

User-friendly interfaces for manipulating data

Important decisions like growing revenue, mitigating risks and improving customer experience should be based on insights drawn using analytics tools. Hence, accessing the data should be a simple process. These following user-interface features will help make data user-friendly.

Dashboards- Dashboards makes data comprehensible even for non-techies as it makes data visually-pleasing. It provides at-a glance view of the key metrics, like lead generation rates and profitability sliced using demographics. Different datasets can be viewed in one place.

Scorecards- A scorecard is a type of report that compares a person’s performance against his goals. It measures success based on Key Performance Indicators (KPIs) and aids in keeping members accountable.

Automated reports- Primary stakeholders should be provided automated reports via mails on a daily basis so that they have access to all the relevant information.

Data analytics should encompass all departments of a credit union. This will help drawing better insights and improve KPI tracking. Thus, the overall performance of the credit union will become better and more efficient with time.

Technologies that help organizations draw valuable insights from their data are becoming very popular. To know more about these technologies follow Dexlab Analytics- a premier institute providing business analyst training courses in Gurgaon and do take a look at their credit risk modeling training course.

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.

Success factors for Business Intelligence program

Posted on September 28, 2015May 23, 2020 by Dexlab

To implement a successful Business Intelligence program, one needs to understand the dimensions that are critical to success of the BI program. Here we will discuss six critical success factors for the BI program.

Critical Dimension 1 – Strong Executive Support

If there is one dimension or critical attribute that has major influence on successfully implementing the BI program would be strong executive support. If there is any lack of enthusiasm at the top will filter downwards. A key component of obtaining strong executive support is a convincing and detailed business case for BI.

Critical Dimension 2 – Key Stakeholder Identification

Early identification and prioritisation of the key stakeholders are crucial. If we do not know who will benefit from a BI solution, it is unlikely that we can persuade anyone that is in their best interest to support the BI initiative.

Critical Dimension 3 – Creation of Business Intelligence Competency Center (BICC) Many organizations have created a separate BICC to manage the lifecycle of analytics processes. Organizations must keep in mind while creating a BICC is that the need of Business Intelligence. They should ask all the strategic and tactical question before creating a BICC. Some of the key objective of BICC shall be

Maximise the efficiency, deployment and quality of BI across all lines of business.
Deliver more value at less cost and in less time through more successful BI deployments.

Also read: Business Intelligence: Now Every Person Can Use Data to Make Better Decisions

Critical Dimension 4 – Clear Outcome Identification

This dimension determines what outcomes the organization desires, and whether they are tactical or strategic.

Knowledge – What knowledge is needed for desired outcomes and where is it?
Information – What information structures can be identified from knowledge gathering and how can these same structures be beneficial.
Data – What sources of raw data are needed to populate the information structures?

Pursuing the answers to these questions requires both logic and creativity. We also need specific information at various steps in the BI process.

Also read: Trends to Watch Out – Global Self-service Business Intelligence (BI) Market 2017

Critical Dimension 5 – Integrating CSFs (Critical Success Factors) and KPIs to Business Drivers

Many business initiatives aim to obtain benefits – greater efficiency, quicker access to information that are hard to quantify. We can easily accept that greater efficiency is a good thing, but trying to quantify its precise cash value to the organization can be a challenge. These benefits are essentially intangible but need to be measured.

Therefore, when identifying these key values, they can be classified as “driving” strategy, organization or operations.

Strategic Drivers Influence:

Market attractiveness
Competitive strengths
Market share

Organisational Drivers Influence:

Culture
Training and development

Operational Drivers Influence:

Customer satisfaction
Product Excellence

Also read: Role of R In Business Intelligence

Critical Dimension 6 – Analytics Awareness

Organizations have a tendency to measure what is easy to measure – internal transactional data. Extending the sensitivity of the organization to external and internal data presents a fuller picture to decision makers of the organization and the competitive environment. If measures are appropriate, the organisation can start to improve the processes.

When the above mentioned six critical dimension of BI solution are place. Organizations can benefit the value from the BI solutions are exponential in manner.

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