Data is the crucial ingredient for this recipe called AI, and hence, a great person once said:

“It is a capital mistake to theorize before one has data.” — Sherlock Holmes

Data is indeed everything in this world driven by technology.

Innovation now seems like an everyday affair, and the amount of data created in this modern world is inconceivable. What do we do with so much data? Well, the good news is every enterprise is coming in terms to understand the importance of Data and its capacity to build AI Model.

Many companies across the world have upped their investment wanting to be a part of the AI world.

Data Strategy – Customized products But one thing enterprises need to understand is, Data and AI will not solve the issues in business, products or services. It helps business leaders to make informed decisions, access the information faster, automate the business process and enable them to deliver results more quickly than a human.

By making use of Data information rightfully, brands have a fair chance to gain a competitive edge among their competitors.

Data Strategy And Its Importance
Data Strategy is a set of choices and decisions that, together, chart a high-level course of action to achieve high-level goals. This includes business plans to use the information to competitive advantage and support enterprise goals. Let us take a look at how having a strategy to utilize the data we have will bring in charm to our brand.

Set Goals
Understand the business goals and needs from the Data sourcing perspective. Once you have concluded, then define the data goals and its importance because it helps to form a strategy so that you can decide what type of data needs to be collected, and what kind of data collection can be avoided.

It is always advisable to step in with well-defined goals, so the efforts we put in to help us get the right data we need to enhance our brand, ever.

Source The Right Data
There are multiple ways to source the data you need. But we must find the right set of data which would give value to our business.

Data can be of two types, names, Structured and Unstructured.

As much as 80% of the time is being spent in data cleaning and processing to convert the unstructured to structured data.

Unstructured data, only if made structured, can give us precise results and insights for the problem. So, before collecting the data, try to source more structured as possible.

There is a principle in the data cleaning process called FAIR (Findable, Accessible, Interoperable, Reusable). If the data cleaned based on the FAIR principle then those data will be Asset of this Digital Era and this can be built once can – use many, according to the 2016 G20 Hangzhou Summit consensus. Not all the data give insights only a few.

Data Governance
Once the Data is collected and processed, the next important thing is to do Data Governance. It is a prime responsibility to protect the data against unauthorized users.

Data Governance is not just preparing private policy for the organization but also needs to adhere to other frameworks as well. Say, we collect the data across different stakeholders from different geographical boundaries – it is essential to comply with the existing policies from where data was collected.

For instance, if we are doing business to European Nations, then we need to adhere to GDPR (General Data Protection Regulation). If not done, there are chances to impose penalties on us because the data might be sensitive.

Also, we need to inform the stakeholders on why we are collecting the data and its purpose. Collecting the data without their knowledge is violating norms, and hence, highly to keep them the stakeholders informed.

Ensure the Data with us is secure and stable.

Right Proportion of Skill
Data and AI Journey require new roles and skills in an organization. It is not a one-person job and needs multiple resource persons across domains.