5 steps for building a data strategy for AI

5 steps for building a data strategy for AI 1

The data centralized world is now giving many organizations to induce some high- end analytics that can use artificial intelligence. Artificial Intelligence is providing more customized user experiences to all the customers, when data experts and analysts get some new information to build from data or even that can just pass without getting detected by using traditional analytics methods.

By using critical thinking to Artificial Intelligence

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

1.Make a plan

One problem 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 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 the source of potential data inside the enterprise. To integrate this entire data there is a need for data lake.

2.Bring all diversity of data                      

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

3.Define the data architecture

Organizations that have diversity in their business and been a part of acquisitions and mergers maintain many data set which include different point of view for same data.

4.Establish data governance

One more very important key is data governance which help in insuring information from many different source, and these are particular for organizations in regulated industries. They also help in protecting, maintaining privacy along with giving visibility in data supply chain is very impotant.

5.Maintaining a very safe data pipeline

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

Written by Sony T

Sony is a passionate bloggers writes on Futuristic technologies ...

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