The Duplicate Records in a System

At Dbperfection, we have witnessed a number of clients facing the problem of having duplicate records in their systems. However, the fact of the matter is that this problem is so mainstream and usual that even the databases which are effectively and efficiently managed also have some duplicate records. These duplicate records are  considered to be very difficult to avoid and there are a number of reasons for this:

  1. The members of a company or an organization use multiple emails. Consequently, new records can be created which are not detected by the system as duplicate ones.
  2. Multiple names are used by the members, which also include nicknames.
  3. The members have similar names and can move. For instance, when there are two James Smith in a company or from different companies.

Firstly, it is important to accept that these duplicate records can not be completely eliminated. However, there are a number of things that can be done which would minimize the number of duplicate records in a system.

  1. Corporations can ensure that their technology has the important duplicate-detention functionality which is both on the customer side and the staff side. The systems should be designed in a manner that they must be checking for email addresses which are duplicate, and even more than that.
  2. The various data integrity reports should be implemented, which are known for checking the potential duplicate records.
  3. The staff should be trained in a proper fashion so that they can enter accurate data, this will enable them to distinguish and clarify if an individual is already existing in a database or not.

Hence, we can say that duplicate records are considered as a fact of the database life. Although we cannot eliminate it completely, but be sure to have some measures, procedures and tools used to minimize these duplicate records.

The Critical Role of Document Processing

The process of data managing is considered to be critical in the field of Computer Science. For instance, clients from various different organizations have discussed with dbperfection on how much it is important to them to capture as much notes as they can  across the organization. It is critical for the staff to actually capture the conversations the departments have with the customers and the members, hence all of this needs to be stored in the database.

But in reality, it is an important fact that if data is not stored in a consistent basis, the data will mostly become useless. There must be clarity in the process, and the process needs to be carried out every time.

And obviously, the data needs to be documented as well.

Why should data be document?

The above given scenario is one of the many reasons why it is important to document the database procedures. Other than assisting the user in the absence of the owner, data documenting and process documenting can also assist in the following:

  • The Temporary as well as New staff can be trained. This will enable them to have documentation when they complete their training.
  • The identification of the wrong practices in business. As the data becomes more and more documented, one realizes that some things need to be altered and changed. For example, why is it that one keeps a copy of the form of membership renewal in the database, with accounting.
  • Time and Money is saved as once a procedure or process is documented, that documentation can be referred to again and again rather than having the irritation of calling software providers.
  • The process of once-an-year can be avoided. As it is said that the toughest procedures and processes are the ones who are run only once an year; including renewal dues annually.
  • and lastly, with good documentation of the processes, the user does not need to remember as its all written down.

What should you document?

It also make adequate sense to make sure that every process is documented when it works its way through to the database. The following should be documented:

  • when you process a newly added member;
  • when you enter information of prospective members;
  • when you run dues the renewal notices;
  • when you drop a member;
  • and lastly when you run a membership listing.

Of course, if the database is being used for the registration of meetings, exhibit sales, publication sales, or other such processes. These are the important areas that need to be documented and are very crucial as well.

 

 

 

Strategies for Database Management

It is witnessed that in this world of innovation and technologies, database management is continuously being reshaped by various new and improved practices that have altered the previous landscape of the domain. This is elucidated by the change in acquisition of data by techniques such as automation, Al, and cloud computing.

 

As a consequence of this, there are a plethora of new and innovative challenges as well as opportunities in the path of different database professionals around the world. In the contemporary era, the teams designed for database management are actually tasked with managing a larger databases with bigger and more variety, on both cloud and premises as well.

 

Simultaneously, the corporations in the world are now striving to achieve greater effectiveness and efficacy, as well as flexibility and scalability in order to support their new generation applications and to assist the derivation of the overall digital transformation.

 

Recently, there was a webinar that took place which was held with the senior product marketing manager of Couchbase, Tyler Mitchell. Other joining in were the director of product marketing at Delphix, Dhiraj Sehgal. The meeting became the center of attention because these personalities then discussed the different strategies that can be used today by various database professionals around the world.

 

Furthermore, across all the corporations in the world, different systems are actually fueling the interactions and becoming more interactive than ever before. Corporations are providing people with various services such as E- Commerce, Internet of Things, and Supply Chain.

 

However, with such facilities, there are also some mammoth challenges which are architectural in nature that include the scale fail, database sprawl, and the multi-cloud manageability as well.

 

Different solutions have been recommended by corporations. For instance, the Couchbase database recommends a solution that has the potential to offer strategic availability, containerized databases, and integrate the workloads.

 

The corporations who are considered to be successful actually have a very rapid speed of innovation and the previous techniques to focus on data actually contained a melange of manual processes and systems. The recent and innovative technique is to create a comprehensive DataOps platform which is used to streamline the various operations and unlock the overall cloud value.

 

 

According to the product manager, corporations such as Delphix transform hybrid applications through:

  • Offering non-disruptive, efficient data migration
  • Provision of data environments conducive to space efficiency.
  • Allowing the hybrid cloud dev/test with the on-demand data
  • Masking cloud data
  • Offering very convenient portability across the clouds

 

Impact of Big Data, Data Warehouse, and Internet of Things on Insurance Companies.

It is witnessed that over the past few years, the Internet of Things (IoT), have increasingly changed the models of business in a plethora of different ways, affecting many sectors and industries in the world. Currently, it is witnessed that the insurance companies are also largely getting affected as the rate-making procedures and business models largely driven by IoT have induced a new paradigm shift in the sector. For instance, now the primary drivers are real-time scoring and telematic approaches such as:

  • Auto insurance Telematics: the telemetry data can be utilized for claim reduction incentives, optimized tariffs, and measuring the insurance premiums for the risk of damage to the person or individual. The ‘Pay-how-you-drive’ tariffs is a great example.
  • Health insurance Telematics: Life-style and data related to health is utilized for the measuring of plans related to health care.

It is important to note that storing the raw data produced with the contract data and structured party is not always a useful thing to do. It is more useful and effective to consider Hadoop which is a more appropriate option available. Furthermore, many consider this to be a cost-effective avenue as well. On the contrary, it is also witnessed that these large data structures are not still accustomed to handling and processing the intricate and complex relational data structures with efficacy. Hence, we can say that it is more likely that large data structures such as Hadoop and others such as Oracle which is a relational data store, would be used simultaneously in parallel by the IT sector of the Insurance Corporations.

However, it is important that we first built a strong connection between these data systems. Firstly, the data can be a combination taken from the data warehouse at the analytical data marts level, which can include the linking of streaming data to a contract number or a customer from the data warehouse. But, assuming that the IT department of an insurance corporation processes the IoT data, we can say that a scenario can be structured in which the analytics results of the big data are generated or handed over by an external service provider to the insurance corporation. In any situation, the overall problem will be to connect the analytical results with the information in the data warehouse.

Hence, we are now witnessing that many insurance corporations are starting to develop an innovative and new system of automotive tariffs. In simple words, the corporations are now rewarding the defensive driving by giving off favorable premiums. All of this is done through IoT, which is through the analyzation from a telematics box which can be installed in the vehicle and sends anonymous info on the behavior of driving by the driver.

Learn the Difference Between Data Mining and Big Data

Data mining and Big Data are considered to be two different things but both are crucially important to understand in the realm of data analytics. Although both of these terms relate to the handling of large magnitudes of data for different recipients, but they are actually used in different context and for two different elements for this type of operations.

 

The term “Big Data” refers to the large sets of data that outgrow the databases which are simple in nature, and which were used in the times when technological advancement was a thing of the future and people used a less feasible and more expensive methodology or data handling architecture. For instance, the term “Big Data” can be used to address the large magnitude of data which is not easily handled in Microsoft Excel spreadsheet. Hence, it will be referred to as Big Data.

 

On the other hand, Data Mining refers to the process of analyzing and thoroughly looking through sets of “Big Data” in order to search for pertinent or important information. Putting the entire operation in simple words, we can say that the operation is similar to the phrase of “looking for a needle in the haystack”. The notion behind this is that the decision-makers in large corporations require access to more specific and smaller sets of data which have to extracted from the homogeneous large sets of “Big Data”. Therefore, “Data Mining” is used as a technique to elucidate the information which can assist businesses in chartering direction for their business.

 

Furthermore, different software packages including analytic tools can also be used in Data Mining, but generally the process of Data Mining include operations with intricate search operations which return results which are specific in nature. For instance, a tool used for Data Mining would look through years of accounting data in order to locate and provide a particular column of accounts needed by the user. Thus, we can simply say that Big Data is the primary asset, while Data Mining can be considered as the handler of this asset.