CU Employee Community Chair

 

              “Without big data, companies are blind and deaf, wandering out on the web, like a dear on the freeway.”                                                                                       

                                                                                                                              Geoffrey Moore

Credit unions require data for functions ranging from the routine to the most crucial.  When wide data is made available to the credit unions, they can make use of it to gain a better understanding of members, improve overall business functions, and receive more returns on investment. It can reduce cost while generating more results.  For credit unions, personalization of services is a key distinguishing factor from other financial institutions. They can bring this personal touch only when they have the right member and operational data to do so. Credit unions can use self-service data preparation technologies to be prepared with data to customize interactions and personalize information. These self-service data preparation technologies not only collect data but also, ensure that it is cleansed and integrated before it is presented for further use.

The five key benefits which self-service data preparation technologies can provide credit unions are listed below-

1. Accelerate Business Impact

The process of data preparation is democratized and as a result, it allows the organization to achieve outcomes faster thus resulting in faster cycle time and more efficient insights.

2. Instant Data

These technologies provide access to the data in a matter of few minutes without any significant manual effort. The data is relevant, structured, consistent and it is possible to have unconditional access to the same.

3. Deepen Member Relationships

Self-Service Data preparation technologies assist in gaining insights about member behavior. Through these insights, it is possible to effectively target customers, increase loyalty, predict member behaviors so that it is possible to adapt to sudden changes and transforming needs and work on other operational analytics.

4. Transforms Raw Data

The self-service data preparation technologies, not just present you with data collected from various sources but also ensures that the information which is collected is transformed (Joined, sorted, aggregated, or pre-calculated).

5. Cross-Platform Sourcing and Extraction

Self-Service Data preparation technologies can provide access to data across portfolios, departments, and channels.  They can retrieve data from even the most ad-hoc and unstructured sources.

 According to Datawatch, as quoted in Data preparation solutions lead credit unions out of the cold, into the heat -

Self-service data preparation technology prepares data for analysis in a fraction of the time it would take when using spreadsheets and other manually-intensive measures. Through automated and repeatable modeling, credit unions can eliminate errors inherent with manual calculation and input, allowing them to create, distribute and publish thousands of reports internally.

Amidst these benefits, there are some complexities with respect to the application of self-service data preparation technologies. Listed below are some stereotypes concerning data preparation and how these technologies can resolve them.

1. Difficulty in accessing Data Across System or Silos

With the assistance of self- service data preparation technologies, it is possible to stream the data across multiple platforms and systems with the help of features like data integration tools,  central storage, and governance. Sometimes, it is also possible to replicate the data for storage and quick access.

2. Irrelevant and Unsatisfactory Inbound Data

Self Service data preparation technologies support analysts by converting raw data into value generating data.  Through ETL tools and features like data virtualization and warehouse automation, data integration is relevant and connected. There is a growing number of sources for data, and a necessity to create agile and related data and this pace can be met only with the help of these technologies.

3. Poor Integration of Data with Analytical Tools.

Business intelligence and data management teams are often burdened with inefficient data. Self-service data preparation technologies provide solutions and practices that offer self-service data preparation and facilities to work with Hadoop data lakes.  These technologies are integrated with analytical tools, to save the time spent on manual inputs.

4. Low Speed  and Agility

Data analytics and service teams provide an agile and self-service model that enable greater access to data. Functions like data cleansing and integration quickly transform the data into actionable and consumable information. You can get fast insights through multiple data sets from any source.

5. Data Preparation Workflow is Complex

The Self service data preparation technologies remove the difficulty of manual inputs and lengthy procedures.  The data is collected, cleansed and integrated within some minutes and without any massive investment of time, money and labor.  They often provide visual workflows which accelerate the process of data preparation.

80% of the work in any data project is in cleaning the data and 80% of any time and resources spent on any data project is in data preparation.

                                                                                                                                                          DJ Patil, former US Chief Data Scientist

This is one of the primary reason why data preparation and cleansing process needs to be automated. Manual data preparation is time-consuming, at high risk of errors, and complex.  With the help of intelligent, automated and self-service data preparation technology, consistent and standardized data can be accessed. With self-service processes and technologies for data preparation, credit unions can devote more time to operations and investments. The technology takes care of the format, structure, and quality of the data before it is presented for further analytical processes.

 

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