Data has become the new oil powering the 21st century. Businesses today have access to a wealth of information that can be utilized to make informed and critical decisions. But traditional decision-making methods can be extremely time-consuming, hypothetical, and less accurate in predicting the future.
Companies that do not adopt a data-driven approach risk falling behind their competitors who are exploiting data to gain a competitive advantage. The right data insights strategies enable businesses to make smart decisions using empirical evidence rather than assumptions or guesswork. They can help identify new opportunities, mitigate risks, and optimize business processes for greater efficiency and cost-effectiveness.
Anish Purohit, Data Science Manager at Synoptek, has several insights to share on how businesses today can build a data-driven organization. Read to learn all about them!
5 Ways to Harness the Power of Data Analytics and Insights
A successful data-driven transformation requires a cultural shift within organizations. Leaders must prioritize data literacy and encourage employees at all levels to embrace data-driven decision-making. They must also drive efforts toward efficient data management and ensure that only clean, relevant, and updated data is used.
As a certified Data Science expert, I have been leading and managing AI/ML-backed projects for over 11 years now. I have collaborated with several cross-functional teams and helped them craft the right data strategies and embrace the right techniques. Here are five key tips I would give businesses looking to harness the power of data insights to fuel business growth:
1. Streamline Master Data Management (MDM)
Streamlining how data is collected, stored, managed, cleaned, and distributed across the organization’s core data entities is one of the most important steps in building a data culture. Businesses must implement effective MDM practices to ensure their data is accurate, consistent, and current.
Master Data Platform
Through effective MDM practices, businesses can
- Establish a single source of truth for their data and make better-informed decisions
- Meet legal obligations, lower the risk of data breaches, and enhance stakeholder satisfaction
- Promote better decision-making, lower operating expenses, and increase income
- Increase business agility and gain a competitive advantage in their respective industries
2. Establish a Robust Data Governance Framework
Establishing a strong data governance framework is a great way to have a set of policies, procedures, and guidelines for data management. The right framework can ensure that data is used appropriately, managed effectively, and protected against unauthorized access or misuse.
Data Governance Framework
Effective data governance can help businesses:
- Define how they manage, protect, and leverage their data assets
- Improve data quality, increase data transparency, and facilitate regulatory compliance
- Reduce the risk of data breaches and improve customer trust
- Better understand their data assets and enhance decision-making, workforce productivity, and competitiveness
3. Incorporate Explainable AI
As more and more organizations embrace Artificial Intelligence (AI) models, explainable AI (XAI) explains the decisions made by these models. It enables organizations to adopt a responsible approach to AI development. It also helps build trust and confidence when putting AI models into production.
Explainable AI
Using XAI, organizations can:
- Ensure that AI models are trained in high-quality and diverse data and that decisions made are accurate and trustworthy
- Better comprehend the results generated by machine learning algorithms and minimize potential biases
- Maintain accuracy, fairness, and transparency in AI-powered decision-making
- Continuously monitor and manage AI models to fine-tune their capabilities, improve performance, and optimize business outcomes
- Effectively manage regulatory compliance and risk while minimizing the overhead of manual inspection and costly errors
4. Invest in Cloud-based Technology
Investment in cloud computing is a crucial part of any data-driven business strategy. With data being generated from various sources and formats, the cloud offers a flexible, scalable, and economical way to handle this data. It also makes it easy for decision-makers to access data without hindrance and make informed decisions in real time.
Cloud Technology
Using cloud-based technology, organizations can:
- Enable regular backups and data encryption to protect sensitive information from unauthorized access
- Optimize their data storage and management processes, reducing the need for on-premises infrastructure
- Lower CapEx and OpEx, enjoy greater agility and efficiency and enable seamless scalability and growth of key data initiatives
- Minimize maintenance overhead while ensuring the data environment operates optimally
5. Enable Data Quality Assurance
Ensuring data is accurate, complete, consistent, and timely via effective data quality assurance is critical to data management. With poor data quality negatively impacting business decision-making, data quality assurance helps ensure that data is reliable and trustworthy.
Data Quality – Measuring | Knowing | Improving
By enabling effective data quality, businesses can:
- Improve confidence in the data they have and the decisions they make and eventually boost customer satisfaction levels
- Lower the risk of errors, discrepancies, and costs associated with bad-quality data
- Ensure data consistency across the organization’s processes and procedures
- Better deal with evolving business and market trends and adapt to changes quickly and efficiently
How to Build a Data-Driven Organization
- Establish a data-driven mindset: Cultivate a culture where data is embraced as a valuable asset and decisions are based on evidence rather than intuition.
- Define clear objectives: Identify specific business goals and metrics that align with your organization’s overall strategy.
- Collect and organize relevant data: Implement robust data collection processes, ensuring data quality, accuracy, and security. Organize data in a centralized system for easy access and analysis.
- Utilize advanced analytics: Leverage data analysis techniques like predictive modeling, machine learning, and data visualization to derive data insights for informed decision-making.
- Empower employees with data literacy: Provide training and resources to enhance data literacy skills across the organization, enabling employees to interpret and utilize data effectively.
- Foster cross-functional collaboration: Encourage collaboration between different teams to share data insights, promote data-driven decision-making, and identify opportunities for improvement.
- Continuously monitor and evaluate: Regularly assess data metrics and performance indicators to track progress toward goals. Use feedback to refine strategies and adapt to evolving business needs.
Cultivating a Data-Driven Culture
As the business landscape evolves, companies must adapt to emerging BI trends to remain competitive. Streamlining Master Data Management and establishing a data governance model is extremely important. In addition, incorporating explainable AI, investing in cloud-based technologies, and enabling data quality assurance can help cultivate a data-driven culture.
But given how complex data has become and how quickly critical decisions must be made, opting for Managed Analytics Services is a great way to drive value. A Data Analytics Services Partner can help conduct comprehensive market analysis and curate the right data insights strategy. A partner can also enable seamless data integration, implement the right AI and ML tools, and empower users with the right business intelligence and data insights solutions.
Discover the benefits of business analytics as a service today to harness the power of data to drive growth, outperform competitors, and meet customers’ evolving demands.
About the Author
Anish Purohit is a certified Azure Data Science Associate with over 11 years of industry experience. With a strong analytical mindset, Anish excels in architecting, designing, and deploying data products using a combination of statistics and technologies. He is proficient in AL/ML/DL and has extensive knowledge of automation, having built and deployed solutions on Microsoft Azure and AWS and led and mentored teams of data engineers and scientists.