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  • What Is the Challenge and Strategies for Data Migration Conversion?

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    Data conversion and migration are intertwined. The experienced players of document conversion and outsourcing units often come with many pieces of advices for the best conversion strategy seamlessly from start to finish. They know what best practices can reap outstanding output.

     

    Standards to Convert Data:

    1.       Select and convert the data into appropriate format that suits the destination database.

    2.       Data transmission should be correctly done.

    3.       Check if data work seamlessly in the new destination database.

    4.       Maintain the quality of data.

    5.       Consistency of data should not be interfered with across all systems.

     

    Challenges:

    A few of the following challenges may interrupt the data conversion migration strategy:

    ·         Data Size: Data migration from legacy systems to the new system may not be a failsafe process, as the size, breadth and complexity of the project can hamper it.

    ·         Unstructured Data: The conversion is compulsory to get off anomalies. This is why uniformity is achieved by converting structured and unstructured data at first.

    ·         Wrong Timeline: Sometimes, the turnaround time may exceed its limit due to wrong migration strategy and predictions.  Always compute it by multiplying the task as per employee’s efficiency per hour. This computation will let you estimate the time being taken to complete a conversion process. You can keep some hours in bulk for meeting any unexpected challenge in the meantime.

    ·         Oddities & Inaccessibility: Defining quality and accessibility of data is a can of worms. The conversion may adversely impact them. So, make sure that you have double checked the quality and put data on authoritative hands.

     

    Key Strategies for Data Migration Conversion:

    1.       Planning: Before you pull up your sleeves for data conversion, put a little bit of strain on brain for planning over limitations, format, kind of data, quality maintenance, conversion process and privacy policy. These questions should run in the back of your mind while planning:

    ·         What kind of data do you have and which format is suitable for conversion?

    ·         What quality standards and networks do you prefer for?

    ·         What and what should you not move to a new database?

    ·         What formats are required for data conversion with compatibility?

    ·         Define the extent of digitization.

    ·         What standard should you adopt for a successful completion of data conversion tasks?

    ·         What could be the guidelines for this procedure?

    ·         What can be the tentative time-constraint of the pan project?

    ·         What is the frequency of data conversion?

    ·         What is the realistically estimated budget? 

     

    2.       Business engagement:

    Data conversion and migration are not the matter of pushing a button to import and export information. Rather, it sticks around mapping the journey to transform and shift data. First, catch the insight of these processes. Then, decide whether to load converted data in a go through big bank method or in batches every week. These all should be done in the presence of the business people because he could truly give the idea of what to throw away or what to keep.

    Subsequently, line up the data accordingly to shift from legacy database. Do remember that it’s a time consuming method, which sometimes goes sluggishly due to the sensitiveness and plenty of data sets transmission.

     

    3.       Data standard implementation:  

    Like data mining, the consistency in any database wins the match. The shifting may misfit your old data into new formats, thereby, damaging their consistency. This is where a quality check can save efforts worth millions.  Constantly go through its quality once uploaded in the warehouse.

     

    4.       Data profiling and cleansing:

    For cleansing, data profiling is a key. It lets the existing information filtered through pertaining statistics and available summaries. Upon understanding their profile, the conversion takes place with high quality.

     

    5.       Data management & governance:

    Duplicate entries may corrupt master data. The conversion process cannot radically remove duplicity. Rather, it may create oddities. Therefore, eliminate duplicate data entry so that the prospective clean reports and transactions could yield value in the next business.

     

    Don’t underestimate privacy policy. The conversion process should satisfy all requisites of data management and their governance.

     

    The aforementioned strategies define the way to move legacy data into a new system. The project may differ. The data may vary. But, the aforementioned key strategies can help you to span across any pertaining challenges without any hiccup.

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