The post depression bedlam is clearing for developed nations. Nevertheless, the household debts are shooting up, making credit risk managers face growing default rates. As per the reports of International Finance, household debts have risen by USD 7.7 trillion since the year 2007 till 2015. At present, the debts stand at a whopping amount of USD 44 trillion – the figures can give anyone a nightmare!
In such a topsy-turvy situation, credit risk managers should look for ingenious methods to lower default rates and keep accuracy in check. Application of data analytics infused with Big Data can come to their rescue.
The term Big Data is really very big! Big Data can help draw crucial insights that would help financial institutions in analyzing their customer base and how their purchase decision patterns vary. It can also be used to enhance business results, especially in regard to credit risk management.
If you follow the current business news, in the coming three years, banks would be facing two major risks – Credit and Liquidity. However, if credit risk managers follow the below-mentioned ways, they can turn this complication into an opportunity:
- Data Analytics determines a person’s behavior and how his circumstances have changed. This is verified by his social media activity, which further affirms how his financial position has changed with time. Hence, the chances of fraud and non-repayment are put in check.
- With proper analysis of mobile and social media data, credit risk managers may be able to gather insights and broaden their market horizon, enhancing the market base.
- Data science can establish contact with low risk customers.
Parsing data with Python should always be discussed after getting a good grip on the nuances of machine learning because both the intricate concepts are interlaced with each other. Click on the link first pythonprogramming.net/downloads/intraQuarter.zip and then go forward with parsing the data.
The data set given in the above link resembles the data set we caught hold of when we first visited the webpages before. The point of interest here is that we don’t need to visit the page even. We just need to have the full HTML source code, that’s it! This system is quite similar to parsing the website without disturbing bandwidth use.
Here, we will discuss about how to incorporate Oracle BI server and make use of the existing Subject Areas built on RPD in Tableau desktop workbook as Data Source.
NOTE: This is applicable for 8.2, 8.3, 9.0 and later versions.
Firstly, to launch an ODBC connection with the Oracle BI Server to access RPD Online, you have to install a copy of Oracle Business Intelligence Developer Client Tools (available from the Oracle website). Following, you can use the same DSN to connect to Tableau through ODBC connections.
As soon as you are done with installing the client, carry out the following steps to create a database source name (DSN):
- Follow the steps mentioned below to add a new system DSN for Oracle BI Server ODBC in the ODBC Administrator tool..
- Go to the System DSN tab and click Add
- Choose the Oracle BI Server DSN, among other available drivers
- Give correct connection details and connect to BI Server
3. Save the System DSN
In total, there are 3 levels in RPD:
- Physical Layer (right pane) – This is the layer where a connection is established between each data source and the raw tables are disclosed. Joins across tables are performed here.
- Business Layer (middle pane) – This is where logical relations, data modelling and hierarchy development are implemented.
- Presentation Layer (left pane) – This is the layer exposed to the business through “subject areas”. The subject areas are clearly modelled to display data in the most easy-to-understand format.
Under Tableau, tables in the presentation layer are adjoined as data sources, only.
Locate the Key Fields in Dimensions and Fact Table in Physical Layer of RPD and disclose the same to respective Presentation Tables in Presentation Layer of Sample Sales Lite Subject Area.
Create a “.tdc” file
Create a Tableau data source customization (.tdc) file to connect Tableau Desktop to an Oracle BI server.
- Open a text editor; copy and paste the below code onto it:
<connection-customization class=’genericodbc’ enabled=’true’ version=’9.3′><vendor name=’Oracle Business Intelligence’ />
<driver name=’Oracle BI Server 1′ />
<customization name=’CAP_SET_ISOLATION_LEVEL_VIA_ODBC_API’ value=’no’ />
<customization name=’CAP_SUPPRESS_DISCOVERY_QUERIES’ value=’no’ />
<customization name=’SQL_CATALOG_USAGE’ value=’0′ />
<customization name=’SQL_SCHEMA_USAGE’ value=’0′ />
<customization name=’CAP_FAST_METADATA’ value=’yes’ />
Nota bene: Version is crucial; adjust the version with that of your Tableau desktop version. In my case, it is 9.3.
Recently, credit risk analysis course in Noida is attracting a lot of attention. If you are looking for an exhaustive credit risk analysis course in Delhi, log into DexLab Analytics. You will surely get what you need!
- Give correct connection details and connect to BI Server
For several years now, I have been associated with a company that enables individuals and organizations, implement Data Governance in their systems. An interesting thing that I have come across is that most people presume data governance to be a technical and analytical field. But the truth is, majority of data governance job roles are actually undertaken by business users. I have found out that most of the successful data governance schemes are run as change management initiatives, which are led and supported by the individuals who possess impeccable soft skills.
At the cost of penning clichéd words, things like passion, enthusiasm and the ability to persuade or motivate others to reap achievements for a goal, be it in Data Governance or any other major programme, will definitely work to stack the odds in your favour. For most of you reading this article, the subject of Data Governance may seem too dry and mundane to be passionate about, something that seems too mechanical and uninspiring. But have you ever thought of this, in this way:
If you do not buy, so won’t they!
And that is not a good news for any of the parties involved.
Having strong communication abilities is always a great asset to have. One may need to convince strangers and that too masses of them, influence them on an individual level, when making proposals of new approaches to governing data. History has it, that there is always resistance to change and having soft skills that can soften this blow of transformation, will be a great tool to manage that resistance and transform it into a positive direction.
So, for those who are working on a Data Governance initiative or aspire to work on some form of Data Analytics or Management field, and have a feeling that communication is not your forte. Then i recommend that you let a helpful hand learn it through proper coaching and practice. After all peering at large data sets does not help with social skills! That is definitely understandable. But you must have a strong orator hidden behind those ‘nerd’ glasses who can translate the complexities of data governance and analytics to the language of the commoners.
I strongly recommend that you use a pragmatic approach when implementing Data Governance and decide to apply the same for your soft skills training.
At whatever stage you are currently in your Data Governance and analytics journey do not forget that spending some time to focus on your soft skills will definitely have a significant contribution towards the success of your initiative.
You can learn more about Data Analytics and governance along with soft skill training at DexLab Analytics.