Guideline To Prepare Dataset for Data Science Marketing Tools

By CIOReview | Thursday, February 7, 2019

Data is now at the core of all business strategies across all verticals. Businesses are now making a shift towards data science with the help of artificial intelligence (AI) and machine learning (ML) to optimize the available data. The hindrance, however, in transitioning to big data analytics is that it requires a robust plan for making a shift. Organizations luring to switch to data science need to develop a strategy based on certain measures discussed below.

Training and Onboarding Skill Set

Enterprises, to give a start must first assemble a team with the essential skill set to work with big data analytics. For this they can either onboard new talents fulfilling the requirements, else could train their existing marketing team ensuring proper training routines and guidelines. Both approaches to form a skilled team is advisory and depends on businesses which one to choose.

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Determining Clear End-Goals for Quality Data

AI and ML require enormous data for better output but it is completely based on the quality of data fed to them. To achieve the desired end goal organizations will have set a clear end-goal and streamline their data collection or harnessing methods accordingly. There are various use cases of data science in marketing but depends completely on enterprises which one to choose.

Maintaining Consistent Data Flow

Analytics tools require a constant inflow of data from various sources such as social media, email marketing tools, web analytics tools, and various other. It’s the responsibility of the business to ensure consistent availability of new data which could result in new insights. Structured and unstructured, both forms of data are crucial in analytics, especially unstructured as it withholds unique insights that cannot be gained through any other form.

Data Cleansing: The Next Step

For achieving quality data and data science algorithms to comb through easily, data cleansing is the next step in the journey. This process can be completed either in-house or can be outsourced. Duplicate and fuzzy will get eliminated, allowing data tools to provide quality insights for the objective.

Integration of Data Consolidation

Crispy-clean data along with data sources should now be redirected to a central data repository—data lake. Integrating data consolidation allows enterprises to reduce the cost of ownership with increased efficiency and productivity helping with simplified regulation compliance.

Lastly, organizations will require comprehending the right skill set for each step and achieving the targeted.