by Angela Guess

Bharath Hemachandran recently wrote in Datanami, “Despite the abundance of big data technologies available in the market today, enterprises struggle to take advantage of big data, because they fail to fulfill the following requirements: Implementing mechanisms to efficiently consolidate data from a large number and variety of sources; Effectively industrializing the entire data life-cycle; Consolidating technology stacks to successfully facilitate effective aggregation, ingestion, analysis and consumption of data to provide value and ROI from big data implementations; Enterprises must jump over quite a few hurdles in order to implement productive and efficient big data strategies.”

Hemachandran goes on, “In order to tap into the humongous potential that big data has to offer, enterprises should make sure to take the following steps: (1) Define: Codifying a precise problem that can be solved using data. (2) Identify: Experts within the enterprise need to agree upon what type of data should be collected, and what sources to collect data from, and the way it should be collected. (3) Model:Creating the right data model is extremely important – it forms the core of the implementation by processing the collected data. Patience is also key when creating data models. Enterprises often move forward and increase the data sample size without taking the time to verify whether a model is correct or not. Once a data model has been tested and is successful, enterprises still need to be careful, though. The data sample size should be increased gradually. A strong assurance strategy that filters out bad data and ensures data quality needs also needs to be setup during this phase.”

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