Data Quality Tools
Iwing, at el. (2014) defined data quality as the data which has fitness for use. Hence, the ZAMBiDM would deal with data quality which is fit for use in all walks-of-life. Actually, the ZAMBiDM needs to set up the principles of data quality tools these include, the institution’s vision, policy, strategies, data cleaning, prioritising data quality procedures, and dimension of data quality. The actual data quality tools are given by Andreescu at el., (2014) such as the Centrus Merge/Purge, ChoiceMaker, ETLQ, ETI*Data Cleanser, Entity Search Server, FirstLogic, Hummingbird ETL, Integrater, Information ETL, Identity Search Server, DataBlade, DataStage, DeDupe, dfPower, DoubleTake, MatchIT, Migration Archtect, NaDIS, Sanopsis, Sagent, SQL Server 2000 DTS, QickAddress Batch, WizeSame, WizSame, WizeWhy, but to name a few. However, under Data Quality, it is recommended that there is no need to re-envent the wheel, as such, ZAMBiDM would use the listed data quality tools.
Elevate Data to Executive Level
The collected heterogeneous data should be analytically transformed to executive consumption. This is where the data could be used to predict the performance of the output. The information provided could help the managers have an insight of production. In that way, they will be able to make tangible plans and decisions on the management of the success of their ZAMREN member institutions. The managers would heavily strategize on the accessing and utilising of huge, in terabits of relevant teaching and research data. As the saying goes that: “information is power”, this would avail the necessary data for institution’s development. Adequate and viable information is useful in a number of ways such as reduction of escalating cost of information gathering, measure the effectiveness of individual processes, raise enterprise visibility, and make the correct decision at right time.
Road Map Manage Big Data
The ZAMBiDM development team would need to reposition in setting up a number of strategic preparation that includes: human capacity development, adequate data storage, produce tools for effective data accessibility, and data definitions.
