Data has transformed the way companies do business in the 21st century. The focus is to collect, clean, and analyze the data in the most effective way so that important business decisions can be made to drive sustainable growth. All of this is impossible if you don’t have a powerful data strategy in place.
As businesses these days rely a lot on their data for critical analysis, it becomes inevitable for them to take good care of this highly valuable asset to stay competitive. Effective data management with a good data and AI strategy can help organizations avoid costly mistakes as well as create new opportunities to improve revenue streams.
What is a Data Strategy?
Generally speaking, a data strategy clearly defines what data you’ll be collecting; what processes you’re going to follow to collect that data; how to store it in form for secure, compliant, and easy retrieval; and what will be the ultimate goal of that data. You need to have a thought-out long-term plan, and the right tools and technologies for data generation, processing, management, and analytics.
Artificial Intelligence (AI) in Data Analytics
Businesses worldwide are already embracing AI as it is helping them improve productivity, maintain operational efficiency, and ensure customer satisfaction. When applied to data, the technology allows you to process a large amount of complex information with accuracy and precision, avoid potential human errors, and ensure consistency.
With time, AI has become more powerful than ever, and it can now be used to manage and analyze Big data. It can identify various data types, find common patterns among large datasets, and recognize knowledge using natural language processing (NLP). AI also leverages technologies like machine learning (ML), deep learning, and cognitive computing that use algorithms and neural networks to learn and execute tasks without human intervention.
A Data & AI strategy can help businesses:
- Identify and capitalize on the latest industry and market trends
- Better understand consumer behavior and customize online marketing campaigns
- Prevent potential losses by using techniques like predictive and prescriptive analytics
- Streamline operations and avoid bottlenecks
- Stay compliant with corporate, industry, and government regulations
- Mitigate risks associated with data breaches
Find more about AI data analytics on how it can help you get the most out of your data.
The Intersection of AI, ML, and Data
Artificial Intelligence is a broad area of computer science that aims to mimic the human brain and create systems that can understand, think, solve, and learn like humans. “Intelligence” is the keyword here, which basically means the ability to make decisions with precision when given a set of inputs and a series of possible actions.
Machine Learning is a subset of AI that uses algorithms to learn and identify patterns from the data fed to the machines—without being explicitly programmed. It works like how a human brain gains knowledge from experience. AI-equipped computing systems are designed to think like humans and perform tasks independently whereas, in ML, the machines use mathematical models of data to learn on their own.
AI and ML play a crucial role in identifying the right data strategy for a business. Organizations can use these powerful technologies to gather and process a large amount of data with precision for predictive analysis and visualization. They can forecast consumer behavior to improve their product or service, or estimate sales to increase or reduce production.
Data Auditing – Why Businesses Need It?
As a business owner, you are collecting a lot of data, but the big question is — do you really trust it? Data without quality means data with little to no significance. And how do you ensure quality? By conducting data audits at regular intervals.
Data auditing refers to the process of assessing a company’s data to check its accuracy and efficacy for a specific purpose. With data auditing, discrepancies in data or data collection processes can be identified with their root cause for remediation. It also ensures compliance with various standards like GDPR, CCPA, and HIPPA.
Data auditing can help businesses find answers to the following questions:
- What and how is data being collected?
- What is the origin of the data?
- Is the data relevant and enough?
- Where is the data being stored?
- Is sensitive data protected against threats with appropriate systems?
Know how good your data is and whether it be leveraged for useful analytics with data audit services.
Data Defense & Data Offense
When it comes to building a data strategy, there are basically two approaches that companies follow: Data Defense and Data Offense.
The Data defense approach is more centralized and allows companies to have more control over their data. The architecture typically includes systems that prioritize compliance and regulations, fraud detection, and data prevention and integration.
The Data offense approach, on the other hand, focuses on adding more flexibility to data management systems. Different business units interpret the data differently as it allows for efficient data transformations. The Data offense approach can help in increasing profitability or enhancing customer experience.
Both approaches have their pros and cons. Generally, the best way to go is to have a mix of both and have real-time operations while also complying with the regulations.
Data Project Planning
A systematic approach and strong planning are essential to kick-start your data project initiative. Also, you need a well-defined workflow to build a data model for the project life cycle.
A data engineer would focus on identifying ways to safely collect data and build ETL pipelines for the smooth flow of data from one repository to another. Because the data is available in multiple formats, it’s important to first clean and standardize it for further processing.
The data sets must be checked for issues, such as missing information, duplicity, and errors that can impact analytics. Once the data is organized and available in a structured format, it must be put into a data model for testing purposes. A suitable data model can be identified by using techniques such as regression, classification, and clustering.
After you have evaluated a model, it must be deployed for critical analysis. The findings can be analyzed through interactive data visualization tools that use visual elements like charts, graphs, and maps to present data in the most effective way.
Because there are a lot of steps involved to stay efficient, you need to plan your resources and timelines for implementation beforehand.
Final Thoughts
Businesses are collecting a lot of data every minute, which needs to be aggregated and processed most efficiently. Because the data is diverse, it requires open platforms, flexible architectures, and strong AI capabilities and skillsets to generate data-driven business insights. Implementing the right data and AI strategy is important to ensure that you are getting the most out of your data. By deploying AI-based data analytics solutions at every point of interaction, you can improve the quality of your data and ensure consistency and accuracy.
Unleash the Power of Data & AI with Contata
At Contata, we can be your strategic partner to offer assistance in efficiently and cost-effectively developing analytical tools that will help your organization better achieve its data management goals.
We have a team of data experts that carries over 10000 hours of cumulative experience in data project planning and execution. We can help you develop plans and find the correct mix of resources for project delivery without delays and budget overruns.
Before suggesting any solutions, we first analyze your data volume and sources, current tools, and licenses so we can provide you with a clear picture of timelines and costs that would be required for building and deploying prioritized modules.
Get in touch with one of our experts today to find optimal solutions for your data and analytics needs.