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Editor’s Question: How to make sense of data insights

Editor’s Question: How to make sense of data insights

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Rakesh Jayaprakash, Product Manager, ManageEngine, explains how ManageEngine helps organisations make sense of data insights in a sustainable and repeatable way.

In this decade, if there is one thing that we are not running low on, it’s data. Depending on how you look at it, having large volumes of data at your disposal is a gift or a curse. When you have abundant data, there is less likelihood that your interpretations are skewed because short-term seasonal anomalies are avoided. However, the downside of large data sets is maintaining them over a long period because organisations do not have an infinite amount of storage space to work with. Also, it may not be required for organisations to store and maintain historical data older than, say 12 months, because it might not help them make present-day decisions. 

At ManageEngine, we help our customers get the most out of their data by recommending them to set up a mechanism, which involves three key steps:

Defining goals

This is the foremost and the most important step for any data analysis project. Stakeholders must establish what they wish to accomplish and the KPIs to track in order to help them make strategic business decisions. Though this may sound complex, necessary KPIs can be easily established by listing questions the business wants answered. These can be questions such as, ‘What products should I spend my marketing budget on during the holiday season?’ and ‘What demographic do our products appeal to, the most?’

Establishing clear goals can help narrow down the type and source of data that would answer these questions directly. It can also help cut down the volume of data that needs to be stored and analysed by factor of about 40 to 60%. 

Building an automated data pipeline

Once the data analysis goals are established, organisations would have a fair idea of which data to use and which to discard. This learning should be fed into data pipeline and ETL (extract, transform, load) tools which, in addition to gathering data from a variety of sources and cleansing them, can also discard data that is irrelevant for analysis. Doing this will ensure that data noise is reduced at the source rather than having to discard unwanted data once it has reached data warehouses or analytics applications.

Archiving historical data

We are in an era where companies must evolve their market approach so fast that the data used for decision making six or 12 months ago would not be relevant for the present day. Businesses must rapidly adapt to changing conditions to ensure continuity and growth, so their decisions must be based on the most current and relevant data. Businesses must determine the relevance of historical data and establish a baseline that indicates how far back in time the historical data should go. This will help them achieve or discard old data that is no longer of any use.

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