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Steps for Building a Data Analytics Strategy for AI



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The data centralized world is now giving many organizations to induce some sort of high- end analytics which can work on the principle of artificial intelligence. Artificial Intelligence is providing more customized user experiences to all the customers, at the time of data experts and Data analysts get some new information to build from data or even that can just pass without even getting detected with the help of using a traditional analytics method.

By using critical thinking to Artificial Intelligence

There is always a need of well- maintained and organized data strategy from the starting only so that the end result could be best. If an organization is easily capable to identify the issue that has to be solved and their decision is also supported by analytics and there is a point where they need to think at a very critical stage for data that is needed to solve that particular problem. So here we are discussing all the five steps to be followed for the successful Artificial analytics project.

1. Make a plan

One of the main issues is always there with many enterprises that they don’t have a uniform view for data silos and this is one the most challenging part, for the project of data analytic, so to overcome this challenge the enterprise should achieve an utmost clarity on their goals of analytic projects first of all. After this one should find out a major source of potential data inside the enterprise. To integrate this entire data there is a need for data lake and data capacity.

2. Bring all diversity of data

The data required for answering the strategic questions need to be qualitative in nature, so the qualitative data come from a very unknown source which includes social media posts, the outsider website content and images, and text documents or just from notes. It is very imperative for an organization to understand that how they can use such sort of data to give any of the more added value.

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3. Define the data architecture

Organizations that have diversity in their business and be a part of deal and mergers maintain many datasets which include different point of view for the same sort of data.

4. Establish data governance

One of the most important is data governance which helps in ensuring information from many different sources, and these are particular for organizations in a much more regulated industry. They also help in protecting, maintaining privacy by just giving visibility in the data supply chain, which plays a very important role.

5. Maintaining a very safe data pipeline

Providing different procedures and policies just to create a kind if the process that allows the flow of data in most constant for the analytic pipeline which will give permission to produce most of the Artificial Intelligence analytics. A very fruitful step so that they can build a privacy and security.

More: 3 Artificial Intelligence and Machine Learning Prediction for 2019

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