Introduction

Today we live with an exponentially ever-growing sea of data. To handle it safely, we use analytics. Without analytics, we would simply drown into the sea of data, not aware what happened or what coming next, the size and source of the data are just expanding unexpectedly. One of the most important branches of analytics is predictive analytics, it has made some sense of storing data. 

Are you aware of predictive analytics technology? If not, get ready. It is the next big revolutionary thing that will propel the world beyond the imagination—and in many respects, it is already in between us. Predictive analytics is the use of statistics, machine learning, data mining, and modeling to analyze current and historical facts to make predictions about future events.

Important Values of Predictive Analytics

Establishment of Effective Customer Relationship- “now you know your customers”

Using the predictive analytics, you can meet your customer needs and target the most benefit cable customers, how you can do this? Here we answer the doubts which are coming into your mind, how predictive analytics helps you to establish a relationship with the customers and helps you to grow your business? Is such a thing even possible? Or Such things have any existence or not?

The predictive analytics (predictions) doesn’t have to be all that accurate to grow or improve your business or to target the profitable customers. “This all about numbers and it’s a number game as always” and predictive analytics used to play it as well and it translates it’s a high return and doesn’t depend on highly accurate predictions      

Let’s understand it in other ways, suppose if predictive analytics identifies 3 times more likely to defect than average customer segments, you can target those segments accordingly. If your overall defection rate is 5%, the segment we’ve discovered has a higher detection rate of 15%, so, in this case, we’re not actually confident in predicting whether any one individual customer will defect or what, the analysis identifying this kind of customer segment which is predicted in aggregate which behaves differently compared to your overall customer base. And now by knowing the different segments of your customers you can understand them more as compared to the past, you will come to know their expectations their needs and have the better relationship by offering them your evaluated services.

Now, the very next question is coming into your mind is how these valuable customer segments are discovered? To get the answer to this question I would suggest you uncover your computer and sooner or later you may get the answer, I must be joking! Yes, I am. This is the magic of “predictive analytics”, It works on past historical data with a huge number of statistical models which leads to delivers magical results and by that your business decision more accurate and healthy in prospects.

The table below lists predictive analytics business applications.

And there are many more applications of predictive analytics, including collections, supply chain optimization, human resource decision support for recruitment and human capital retention, and market research survey analysis.

Offering New Services and Products 

Somehow If we could predict the future, we would be able to read the minds of our customers as well and will come to know exactly what they wanted.  The closest we can get to this supernatural ability is utilizing predictive analytics to tap into the hidden information that resides into a big data. 

To understand this, we have an example of insurance sector which is widely using this technology and changing their offerings, launching policies almost every day, based on their analysis.  

     Risk Management- “we are here to care…. leave it on us”

A famous philosophy works in Risk Management is to “work smarter, not harder”, but How? By implementing the most efficient tools like as Predictive Analytics with your existing system. 

The predictive analytics playing the vital role in mitigating the risk in all sectors which are associated with our day to day life, let us take an example to understand it in depth.

Let’s have an example of the bank, as we know the banking sector is very risky and hard to handle or predict the risk based on your experience or intuition, the predictive analytics allows you to embed advanced fraud detection models directly into your database applications. By introducing predictive analytics into your database or transactional systems, a percentage of fraudulent transactions can be detected at the time they are being processed and before payment. The result is a significant reduction in costs associated with fraud both within the organization and to the customer.

Before the predictive analytics, we were dependent on our intuition and domain training, and that was not enough to stop the fraudulent activity after introducing the predictive analytics we are more powerful and efficient to deal with it in several ways.

Why do You need to Trust on Predictive Analytics to Improve Risk Management?

The “Predictive Analytics” plays a vital role in risk reduction in various fields such as insurance and healthcare industries. As per the report of “Predictive Solutions publication”, the predictive modeling has high accuracy levels between 80 percent and having accuracy about 97 percent in predicting future worksite injuries based on existing data. The use of predictive analytics introduces a very professional way of safety and prevention and it leads to risk reduction. 

Different Areas Where We Can Have Predictive Analytics Risk Management:

• Sales and Marketing.
• Fraud: Fraud is one of the measuring areas where we must have predictive analytics to reduce risk and covers healthcare fraud, credit card fraud and much more.
• Insurance: It’s a sector where Predictive Analytics has its own importance.
• Healthcare Industries. 

      Inventory Optimization – “no more over or under stocking”

As we all know, Predictive analytics uses various techniques to analyze past historical data to make predictions about future. One of the major areas which we can deal by applying the technologies and analysis is supply chain. The biggest challenge that the companies facing is accurate demand forecasting or to manage their inventory. This can be solved up to a certain level if technology and analysis can help to determine customer’s usage and buying nature. 

Let’s understand it with a simple example, in any product based company there needs to manage their supply chain and it has a different unique level of a supply chain, that is shown in fig. 

Of course, the secret to good forecasting is to keep doing it over and over until you get it right or closer to your expectations.  Forecasts should be continuously updated and incorporate time frames that may be several years out (to anticipate the obsolescence issues), mid-term forecasts that drive our financial investments in plants and new products, and near-term forecasts that drive actual production and procurement.

How is the Predictive Analytics Helping Hand to Managing Supply Chain?

• Demand Analytics – How is my forecast leading with actual data.

• • • Detailed demand forecasting at the different point of sale.
    • Tune the forecast with integration of promotional events and holidays

• Inventory Optimization – What stock should I hold and where should I position it.

• • • Inventory budget optimization
    • Safety stock level recommendations

• Key Things Which We Need to Avoid Before Working with Predictive Analytics:

• Don’t confuse more data more insights:  The volume of data is not always proportional with the insights of the data and their values.
• Don’t overestimate the ability to interpret the data:  Sometimes even the best data may afford only limited insight that doesn’t mean data is inappropriate.

Scope of Predictive Analytics – “Beyond the Predictions”

What Have We?

Predictive Analytics have already earned so much popularity in almost every sector like sales & marketing, e-commerce, insurance, banking, healthcare, CRM and much more due to its high level of accuracy and advances in computing technology, techniques used for analysis, advanced mathematical and statistical modeling.

• Tomorrow’s Just a ‘Day’ away
• Trend to monetizing data becomes, even more, ubiquities
• A new area of data sources will come forward: Internet of Things, Wearables, Beacons, …
• No need to have the expertise level of knowledge to handle the things.
• Automatic Business Modellers will come in front.
• Predictions of Business Decisions.
• The Future of Prediction Analysis

The entire community of ANALYTICS is working every second of time to improve an accuracy of prediction and to improve future of prediction, sooner or later we will see the revolutionary change. Just Wait and Watch!!

Please feel free to reach us at sales@bistasolutions.com for any queries on odoo and its related modules.Also, you can write us through feedback@bistasolutions.com` and tell us how this information has helped you.

Introduction

Bista Solutions had always been ahead in terms of adopting new technology and ongoing trend especially in the BI industry. For projects having short deadlines and Customer who don’t prefer spending more on the ETL part, we at Bista solutions had opted for Data Blending option for such special customers. After the successful implementation for one of our esteemed Client, we would like to share our experiences in using Data Blending.

Before we start, let’s understand what exactly Data Blending refers to? Data Blending is basically a process of combining the data from multiple data sources into a proper useful data set which can be used efficiently for reporting and analytics.

Challenges faced:

So as per our Customer requirement, the core data was required to be fetched from Postgresql and since they wanted to capture daily Currency rate fluctuations so we had to also fetch data from their website and to record their historic data it was also required to connect to the Hive database. As a part of the visualization, all this data from different sources had to be analysed and used in a single report. So, under this scenario, we initially opted for ETL to combine all three sources of data and point to a single data set. But it was difficult to implement due to time constraint and budget cost.

Solution provided:

To provide a better solution we had to explore and implement the Data Blending option which not only helped us in saving time but also made us to deliver the product on time without compromising on the quality. Tools which really helped us in implementing the same in our multiple projects were

1. Tableau
2. Microsoft Power BI

As per our experience on this tools, they were helpful in implementing the Data Blending concept for our Clients. The flexibility of self-service BI along with data blending facility gives a completely new edge in implementing a powerful BI solution.

Let’s understand why we didn’t preferred joins over Data blending.

• Data needs cleaning.

On the off chance that your tables don’t correspond with each other effectively after a join, set up information hotspots for every table, make any vital customizations (that is, rename segments, change segment information sorts, make bunches, utilize figurines, and so on.), and afterward utilize information mixing to consolidate the information.

• Joins cause copy information.

Duplicate information after a join is a side effect of information at various levels of detail. On the off chance that you see duplicate information, rather than making a join, utilize information mixing to mix on a typical measurement.

• You have loads of information.

Commonly joins are prescribed for consolidating information from a similar database. Joins are taken care of by the database, which permits joins to influence a portion of the database’s local capacities. In any case, in case you’re working with extensive arrangements of information, joins can put a strain on the database and essentially influence execution. For this situation, information mixing may offer assistance. Since Tableau handles joining the information after the information is collected, there is less information to consolidate. At the point when there is less information to consolidate, by and large, execution moves forward.

Note: When you mix on a field with an abnormal state of granularity, for instance, date rather than a year, questions can be moderate.

Also during implementation, we came across some Prerequisite like,

Your data must meet the following requirements for you to use data blending.

Primary and secondary data sources

Data Blending requires an essential information source and no less than one optional information source. When you assign an essential information source, it works as the primary table or principle information source. Any consequent information sources that you use on the sheet are dealt with as an auxiliary information source. Just segments from the optional information source that have relating matches in the essential information source show up in the view.

Utilizing a similar case from above, you assign the value-based information as the essential information source and the quantity information as the auxiliary information source.

Note: Cube (multidimensional) information sources must be utilized as the essential information source. 3D shape information sources can’t be utilized as an auxiliary information source.

Characterized relationship between the essential and optional information sources

In the wake of assigning essential and optional information sources, you should characterize the basic measurement or measurements between the two information sources. This normal measurement is known as the connecting field.

Proceeding with the case from above, when you mix value-based and amount information, the date field may be the connecting field between the essential and auxiliary information sources.

• If the date field in the essential and optional information sources have a similar name, Tableau makes the relationship between the two fields and demonstrates a connection symbol ( ) beside the date field in the auxiliary information source when the field is in the view.
• If the two measurements don’t have a similar name, you can characterize a relationship that makes the right mapping between the date fields in the essential and auxiliary information sources.

Benefits:

With the help of Data Blending our BI solution offered flexibility to connect to multiple data sources and visualize it under the same report which not only made it more attractive but also helped in a productive way for our Customer which was missing before in their existing system. So now our customer could visualize their historic data and the value of the same under certain Currency exchange daily. Last but not the least it helped us in budgeting project cost up to 20% which was allocated for the ETL process. With this implementation, we strongly recommend our Clients for Data Blending and well confident to implement and bring value to your business data as well. If you are looking for any such type of Implementation or require any further details on the same, you can reach out to us on sales@bistasolutions.com , Also if you have any feedback or suggestion then mail us at feedback@bistasolutions.com    

Most real-world datasets include a certain amount of anomalous values, generally termed as ‘outliers’. These observations substantially deviate from the general trend therefore, it is important to isolate these outliers for improving the quality of original data and reducing the adverse impact they have in the process of analyzing datasets. Practically, nearly all experimental data samples are likely to be contamination by outliers which reduce the efficiency, and reliability of statistical methods. Outliers are analyzed to see if their unusual behavior can be explained. Sometimes outliers have “bad” values occurring as a result of unusual but explainable events. The cause of outliers are not always random or chance. Therefore a study needs to be made before an outlier is discarded.

Detection of Statistical Outliers

Statistical outliers are more common in distributions that do not follow the normal distribution. For example, in a distribution with a long tail, the presence of statistical outliers is more common than in the case of a normal distribution.

The simplest method of identifying whether an extreme value is an outlier is by using the interquartile range. The IQR tells us how spread out the middle half of our data set is.

The interquartile range, or IQR, is determined by subtracting the first quartile from the third quartile.

We start with the IQR and multiply it by 1.5. Then subtract this number from the first quartile and add this number to the third quartile. These two numbers from our inner fence. For the outer fences, we start with the IQR and multiply it by 3. We then subtract this number from the first quartile and add it to the third quartile. These two numbers are our outer fences.

Outliers can now be detected by determining where the observation lies in reference to the inner and outer fences. If a single observation is more extreme than either of our outer fences, then it is an outlier, and more particularly referred to as a strong outlier. If our data value is between corresponding inner and outer fences, then this value is a suspected outlier or a weak outlier.

Example

Suppose that we have calculated the first and third quartile of our data, and have found these values to be 40 and 50, respectively. The interquartile range IQR = 50 – 40 = 10. Next, we see that 1.5 x IQR = 15. This means that the inner fences are at 40 – 15 = 25 and 50 + 15 = 65. This is 1.5 x IQR less that the first quartile, and more than the third quartile.

We now calculate 3 x IQR, that is, 3 x 10 = 30. The outer fences are 3 x IQR more extreme that the first and third quartiles. This means that the outer fences are 40 – 30 = 10 and 50 + 30 = 80.

Any data values that are less than 10 or greater than 80, are considered outliers. Any data values that are between 10 and 25 or between 65 and 80 are suspected outliers.

Reasons for Identifying Outliers

The presence of outliers indicates errors in measurement or the occurrence of an unexpected and previously unknown phenomenon. It is extremely important to check for outliers in every statistical analysis as they have an impact on all the descriptive statistics, as they are sensitive to them. The mean, standard deviation and correlation coefficient in paired data are just a few of these types of statistics. This could mislead analysts into making incorrect insights as all these statistics get distorted.

NOTE:

Certain statistical estimators are able to deal with statistical outliers and are robust, while others cannot deal with them. A typical example is the case of a median. It is the most resistant statistic with a breakdown point of 50%. Which means that as long as no more than half the data are contaminated or missing, the median will not deviate by an arbitrarily large or small amount.

In practice, an outlier could cause severe damage to data-driven businesses. For example, outliers in transactional data of retailers or distributors could lead to the incorrect calculation of demand forecasts. Leading to a mismatch of demand and supply as the business either ends up understocking and overstocking its inventory. Other adverse outcomes could also include; inaccurate budget planning, non-optimum resource deployment, poor vendor selection, loss-making pricing model et cetera.

Even engineering firms or manufacturers can be adversely affected by outliers. Errors in measurement taken from sensors (eg. thermometers, barometers) during quality checks of the products produced, could result in unexpected failure of products, incorrect measurement of warranty periods, initiate re-designing of products et cetera.

The adverse effects of outliers could even influence the life of citizens when data collected by the government contains outliers. Biased samples in government surveys, containing observations which would’ve been considered outliers when compared to the entire population, could justify the formulation of policies that could damage society. Thus, it is imperative to devise methods of dealing with outliers in statistical analysis.

Treatment of Outliers

The treatment of outlier values can be achieved by the following categories of actions that can be taken:

1. Transformation of Data: Transformation data is one way to soften the impact of outliers since the most commonly used expressions, square root and logarithms, affect larger numbers to a much greater extent than they do the smaller ones. Transformations may not fit into the theory of the model all the time as they may affect its interpretation. Transforming a variable does more than make a distribution less skewed; it changes the relationship between the variables in the model.

2. Deletion of Values: When there are legitimate errors and cannot be corrected, or lie so far outside the range of the data that they distort statistical inferences the outliers should be deleted. When in doubt, we can report model results both with and without outliers to see how much they change. Data transformation and deletion are important tools, but they should not be viewed as an all-out for distributional problems associated with outliers. Transformations and/or outlier elimination should be an informed choice, not a routine task. In some cases, the removal of an outlier value can also induce incorrect inferences made about the data. In such cases, replacing the observation with a measure of central tendency (Mean, Median or Mode), depending on the situation.

3. Accommodation of Values: One very effective plan is to use methods that are robust in the presence of outliers. Nonparametric statistical methods fit into this category and should be more widely applied to continuous or interval data. When outliers are not a problem, simulation studies have indicated their ability to detect significant differences is only slightly smaller than corresponding parametric methods. There are also various forms of robust regression models and computer-intensive approaches that deserve further consideration.

If you’d like to implement software involving forecasting for your business, you can reach out to us using our contact form or at sales@bistasolutions.com.