Numerous businesses make use of artificial intelligence (AI) to gain new insights, improve existing products or services, and take their company to the next level. They use AI technologies to detect patterns from large sets of data and make predictions based on that information.
For any business to successfully execute an AI project, they need to obtain a certain amount of data and infrastructure readiness. But before organizing and processing their data, a business should identify the objectives of data analysis. What does your business hope to achieve with the data it collects?
Why Knowing Your Data is Vital
Knowing what data you are collecting and defining what data is lacking will enable you to make smarter business decisions. Ask yourself the following three questions to assess if your data is ready for AI.
What business problem are you trying to solve?
The key is to understand what your business wants to achieve with the data it is collecting, and why your business is investing in data readiness.
Maybe you want to provide repeat visitors with targeted and personalized offers to improve your conversion rate, or perhaps you want to optimize your predictive analysis to increase revenues and reduce costs. Different objectives will require different monitoring and data analysis techniques.
What data have you been collecting?
It is common for businesses to have data spread across multiple data sources. You may already have data that could help solve your business problem, it just is not organized and analyzed yet. It is key to understand the data you have been collecting and determine whether or not it is the right kind of data you need to move your business forward.
What additional data do you need?
After taking stock of your existing data, the next step is to know whether or not you need to expand your data collection to meet goals and objectives. If you need additional data, you need to know how and where you are going to collect this. Be prepared to invest in proven analytical software tools to compile and analyze data to reveal important insights. In addition, trained staff with technical know-how will be necessary to retract the correct data sets from analytics.
Having the answers to these three questions will ultimately help ensure that you’re not swimming in useless data.
Inspecting Data: 4 Steps To Better Decision Making
Once all relevant data has been collected, the next step is data inspection. Inspecting data is a multi-step process that includes data cleansing, data normalization, data transformation, and data enrichment.
1. Data Cleansing
Data cleaning is the process of preparing data for analysis by removing or modifying incorrect, incomplete, or improperly formatted data. This data may hinder the process or provide inaccurate results and is therefore considered unnecessary.
2. Data Normalization
Data normalization is the process of intercepting and storing incoming data to ensure it exists in one form only. During this process, redundant or repetitive data is eliminated in order to protect the data’s integrity.
3. Data Transformation
Data Transformation is the process of converting data from any source into a common format so that one set of data is compatible with another and allows for collaborative research.
4. Data Enrichment
Data Enrichment is the process of combining first party data from internal sources with third party data from external sources. Companies do this to enhance the data they already possess so they can make more informed decisions.
Inspecting and preparing data for analysis takes time and a sizable investment. However, this process cannot be skipped, fast forwarded, or neglected if you want to get valuable and accurate insights. Only when data is in its final format, can serious data analysis begin. From here, the much-anticipated insights will follow.
Learn more about how we can work together your Data Science and Analytics!
In this emerging data-driven and AI-powered economy, data is an important strategic asset for companies. However, not all data is always helpful, it needs to be fit for the purpose. Businesses frequently attempt to perform data analytics on data that is not yet ready for analysis. They end up with inaccurate findings, misleading results or an incomplete analysis.
In order to get valuable and deep insights from data for AI projects, businesses first need to determine the problem they are trying to solve, gain a better understanding of the data they already have, and gather any additional data needed that is not available to them yet. Once all required data has been collected, they need an orderly process to treat the data for proper analysis. This multi-step process is a serious time and monetary investment, not to be rushed through.
● To successfully execute an AI project, businesses need to obtain a certain amount of data and infrastructure readiness. The most important question companies should ask themselves is what their business hopes to achieve with the data they collect.
● Knowing your data will help you make smarter business decisions. You need to determine what business problem you are hoping to solve, what data you have been collecting so far, and what other data you might need.
● Once relevant data has been collected, data inspection can begin. Inspecting data for analysis is a multi-step process that includes data cleansing, data normalization, data transformation, and data enrichment.
● It is not just about collecting the right data and having the right analytical software tools, technical know-how is also vital to successfully execute an AI project.
Do you have the right data to make better business decisions? And does your company have the expertise to distill meaningful insights from this data? Encora can help you unlock the power of your data, creating data-driven insights and tools to drive your business’s growth strategy. Feel free to contact us to learn what we can do for your business.
Encora accelerates business outcomes for clients through leading-edge digital product innovation. We provide innovation services and software engineering solutions across a wide range of leading-edge technologies, including Big Data, analytics, machine learning, IoT, mobile, cloud, UI/UX, and test automation. Contact us to learn more about our services and how we can help your business.