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Data readiness is a crucial part of any AI analysis project.

Tuesday 06 of October, 2020.
Reading Time: 4 minutes.
By Pixel506 (Avantica Authorized Partner)

Artificial Intelligence (AI) is a reliable means for processing data to help businesses gain insights that can take a company to the next level. AI relies on large volumes of data and mathematics to generate these insights. A business must, therefore, obtain a certain amount of data and infrastructure readiness before it can successfully execute an AI project. Before we get to how to process data that will give those crucial insights, it’s important to outline the objectives that an organization is hoping to achieve with the data insights. 

Get to Know Your Data

First off, ask: What business problem needs to be solved? Understand where your organization wants to go; and how data will help it get there. 

Next, get to know your existing data. Learn what kind of data you have been collecting to date. Consumer data differs from marketing data. Maybe you’ve been amassing client emails and surveys or perhaps your data collection is more sophisticated and includes analytics from Google and showing where your clients are, how long they visit your website for, and so on. From here you can see if you have the type of data that you’ll need to enhance your business going forward, or if you need to expand your data collection to meet your future goals and needs.

Your Why

After taking stock of your existing data, the next step is to know what your business wants to get from the data and why your business is investing in data readiness. How will using AI-driven insights benefit your business? Having the answers to questions such as these will ultimately help make sure that you’re not swimming in useless data. Maybe you want to be able to personalize your website for repeat visitors for a better conversion rate; or perhaps you want to use predictive analysis to increase revenues. The two are different objectives and the data sets required to achieve these goals very well may be different.


Once relevant data has been collected, it’s time to inspect it and later analyze. A business must be prepared to invest in the appropriate analytical software tools to cull and organize the right data from sources. This is a multiple-step process that requires staff with the technical know-how to retract the correct data sets from analytics. 

Data Cleansing

Another part of the data collection process is data cleansing. Data cleaning is the process of identifying and adjusting inaccurate records from a dataset. Ideally, data cleaning techniques are performed at the data source level via batch processing with data cleansing tools and again at multiple stages in the process. 

Data Normalization

Another data issue that must be tackled in the analysis process is Normalization. Data normalization is the process of efficiently organizing data in a database. The two main goals of normalization are to sort out repetitive or redundant data and making sure that the data dependencies are logical. 

Data Transformation

Data standardization is the process of putting data into a common format that allows for collaborative research, and analytics to occur. This is a stage where data is in a desired state for further analysis, sharing, and more. 

Enriched Data

Enriched data sets have implemented the above practices of data cleansing, normalization, and standardization and are deemed accurate and accessible. For AI to generate valuable insights, data should be enriched. 

Getting data to the enriched phase is a process in and of itself. And it should be acknowledged that it takes time and a sizable investment to organize the data for analysis. Too often, companies are overzealous about the benefits and promises of AI-provided insights and don’t do proper diligence with their data, sacrificing the entire project. Once the data is in its final format, the real analysis begins and from here, the much-anticipated insights follow. 

Final Thoughts

While AI promises a big ROI with dynamic insights, the due diligence must be done. Before any accurate insights can be obtained, adequate data must be filtered into a ready-to-analyze format; and this multi-step process is a serious time and monetary investment, not to be rushed through. Too often, businesses attempt to perform data analytics on data that isn’t ready which yields inaccurate results or an incomplete analysis. Every business should also know what it hopes to gain from the data and have an orderly process to treat the data for proper analysis. 






  1. Before processing data to obtain crucial insights, a company must outline the objectives it is hoping to achieve with these data insights.
  2. It’s key to get to know the data you have been collecting and determine if it is the right kind of data you need to move your business forward. It’s also important to define why your business is investing in data readiness.
  3. Once relevant data has been collected, data analysis can begin; this is a multi-step process that includes data cleansing, data normalization, data transformation, and enriched data.


About Avantica

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