Big Data is Now Helping Small Businesses to Grow Bigger & Better

In today’s digital era most of the businesses revolve around ‘Data’; be it a big organization or a start up or a small business. Data science helps companies to track their progress as well as pinpoints the places for improvement. Nowadays, it has emerged as a complex interdisciplinary field to grow your business with its statistical as well as productive analysis. It is basically the study of how to manage the huge pool of information of your organization and how to apply them to gain profit in business. In the competitive market, just mere strategy is not good enough especially for small businesses; you need to have foundational logic to make wise decisions to grow your business. 

One of the huge challenges for small businesses is where and how to start with? How to organize the data? Which data should be emphasized on? How to analyze the data to get market review? In most of the cases small business owners don’t possess the idea how to handle data. Here comes the need of professionals who are known as data scientists. The job of a data scientist is one of the most highly paid jobs of 21st Century. The job role includes analyzing data with the purpose of providing a competitive advantage in business or addressing a pressing business problem. A data scientist scrutinizes data from various angles, interprets it, and then suggests ways to apply that data. Small organizations also often look for data architects as the role involves using applied mathematics for creating database systems, constructing storage, and archival systems for information. Data architects also work on improving the data transfer between these systems.

Stand a chance of making a successful career as a data scientist by joining DexLab Analytics data analyst certification. 

Small businesses use data science as a tool to develop their marketing performance. Data science creates the scope for them to view the market from the customer’s point of view. Understanding customer needs and implementation of that in business strategy proves to be effective in the growth of business. Tracking campaign results is a process used by small organizations to monitor website traffic. Take, for example, Alexa or Google analytics: these tools analyze the website ranking as well as user behavior, like, how long a user stays in the company website or how he follows the updates. Companies can also track their social media traffic even on mobile devices.

Small businesses often run through budget constraints. So, various online free tools are available in the market to systemize your data; you can also opt for low price tools to manage your data efficiently. So nowadays, small businesses should certainly emphasize on data science because it will help them to gain useful insights on how to enhance their performance.

If you want to learn the ropes of data science, then you should enroll in the data analyst training institute of DexLab Analytics.

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Top 4 Data Scientists in India – 2016

View our presentation celebrating the lives of 4 Indian data scientists who have made it big in this field of analytics. 

This Video was  Posted By: DexLab Analytics

The 5 Golden Rules Behind The Success of Data Scientists

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Bridging The Gap Between Two Lost Brothers – Machine Learning

Machine learning is the subject that fills the gap and forms the missing link between the two separated brother subjects which when applied together can do wonders – statistics and computer science. Machine learning is the new avenue of technology which is totally transforming the way we take care of our daily errands. Learn more about machine learning and its broad spectrum of application at our latest video on DexLab Analytics.

Success factors for Business Intelligence program

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.

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.

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

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.

ALLOWANCES FOR LOANS AND LEASE LOSSES- Part 1

The Allowances for Loans and Lease Losses (ALLL) is a valuation reserve established and maintained by charges against the bank’s operating income. As a valuation estimate, it is an estimate of uncollectible amounts that is used to reduce the book value of loans and leases to the amount that is expected to be collected.  The ALLL forms a part of Tier-2 Capital; hence it is maintained to cover losses that are probable and estimable on the date of evaluation. It is not a cushion against all possible future losses; that protection is provided by the Tier 1 Capital. For establishing and maintaining an adequate allowance, a bank must:

• Understand the purpose of the allowance
• Be able to recognise its problems loans in a timely manner.
• Have a sound analytical process for estimating the amount of inherent loss in its loan portfolio.

To establish an adequate allowance, a bank must be able to recognise when the loans become a problem. An effective loan review system and control is essential to identify, monitor and manage asset quality system and problem in an accurate and timely manner. An effective loan review system must be able to identify the:

• Obvious indicators of a problem, such as delinquency.
• Subtle warning of the conditions that may affect the ability of the borrowers to repay on a timely basis, such as deterioration in a borrower’s financial statements or adverse market developments.

The condition and events that cause a loan to be classified by a bank’s loan review system, also indicates that an inherent loss exists in the loan. It is these inherent (but unconfirmed) losses that must be recognised and provided for in the bank’s allowances. Let us discuss these two types of loans: (1) Unconfirmed losses (2) Confirmed Losses.

• Unconfirmed losses: The allowances are general reserves for unconfirmed losses. Unconfirmed losses are those losses which does not have a surety of occurring. They may or may not occur. These losses arise from accounts which are still performing in the books of the banks, with a probability of default over the next twelve months. Any unconfirmed losses must be treated as a ‘general reserve’ or ‘pooled reserve’
• Confirmed losses: Such losses are to be charged off as soon as they are identified. Irrespective of the fact that whether the loan is unsecured or collateralised, banks must charge off as soon as they are identified.

It is important to understand the soundness of the bank’s allowance determination process. Here we discuss models and analytical frameworks relating to estimating inherent losses and an adequate level for the allowance for loans and lease losses. There are two analytical frameworks for determining reserves: (i) As per FAS 5 (General Reserve models) (ii) As per FAS114 (Specific Reserve models).

In the next few blogs we will discuss these frameworks in greater details and the pros and cons associated with each. We will focus on the statistical model development frameworks associated with each approach and the respective advantage and limitation of each process.