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New eBook: The Enterprise Guide to Kicking Off the AI Adoption Process

By Advanced Data & Analytics Team | Posted on January 28, 2020 | Posted in Artificial Intelligence (AI), Business Transformation, Modern Datacenter, Operations, Advanced Analytics

From automation and data analysis, to internal communications and customer service, there’s a lot of power in Artificial Intelligence (AI).

Unfortunately, many enterprise AI projects fail to reach lift-off due to hurdles like a failure to identify use cases for AI, data cleanliness and quality, or lack of technical expertise.

Our new eBook, The Enterprise Guide to Kicking Off the AI Adoption Process, is aimed at helping you get past these hurdles. In its pages, you will find step-by-step solutions for getting up and running with AI.

Here’s an excerpt about the importance of cleaning and managing your data:

AI is only as powerful as the data it has access to.

A disorganized data pool limits the effectiveness of AI by obscuring insights or leading to untrustworthy results. For example, a chatbot that has trouble finding information a customer is looking for in a mess of data.

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To successfully put AI to work, you need to ensure all your data is cleaned and organized. While different enterprises will have different data needs, in general, cleansing data can be broken down into five steps:

1. Make a plan

Identify the data sets you want to use, along with all the elements needed within those sets. For example, if you want a complete data set of your customers’ locations and industries, make sure to include specific validation rules to capture them.

2. Analyze

Review your existing data to identify what information is vital and what can be thrown out. This is also where you want to recognize any gaps in your data sets.

3. Cleanse

Set up scripts or workflows to standardize and clean the flow of incoming data, either in real-time or batches, depending on the amount of data you’re going through. These scripts and workflows can also be used to retroactively cleanse existing data.

4. Find missing data

Work with a third party or reach out directly to contacts and customers in order to fill in any missing data revealed during cleansing. For example, if you have a set of data with customer locations but not the industries they work in, you can append that data to bring all your information in line.

5. Monitor

Keep revisiting the previous steps regularly since the flow of data is nonstop and new variables may need to be included as your business evolves.

Download your free copy of The Enterprise Guide to Kicking Off the AI Adoption Process and get started with AI today.

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The Enterprise Guide to Kicking Off the AI Adoption Process

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