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Data Management
We have a wide choice when it comes to data management technologies that can make a M&E data collection project manageable and efficient. My personal choice and also because I am better acquainted are open source technologies like MySql for relational data base, Tableau for data visualization and Python for data science. Since MS Excel is a commercial software that has become a household product it is important to know the basics of performing data cleaning with it, though Python has a host of libraries that enable you to explore data with more agility.
Data base systems are far advanced of late and we have NoSql for creating and managing non relational or unstructured data bases. We also have graph graph databases which are capable of processing both relational and unstructured types of data. Over and above there are countless tools which can also aneble one to query file directories like Apache Spark and Drill though these are big data applications I trust that they could come in handy when the data being studied is historical and was collected and stored in a fashion that was not well planned or so due to it's nature(records in documents and folders).
As a data analitics enthusiast I am of the opinion that data for any purpose and to answer societal and economic phenomena questions can be sourced through planned projects like M&E as well as through active and continuous collection from websites and other available applications. Web scraping is a also vital to get acquainted with and Python has easy tools for this style of collecting and processing of data. It is important to note that there are legal implications in sourcing data through webscraping and permission has to be sought from owners of the websites and applications, which may in turn affect the personal data policy agreements between platform owners and platform users.
In conclusion I think every M&E project team should have among their staff members a data specialist and elect to use data management tools for which they have good proficiency.
