Data Investment: Strategies For Maximizing Your Data's Value

Data is an invaluable asset to any business, and with the amount of global data growing at a rapid rate, it is no surprise that it is now an actionable business element. Investing in data can offer numerous benefits, such as targeted marketing, streamlined processes, and enhanced customer experiences. By leveraging data analytics, businesses can gain a competitive edge, make better decisions, and identify market trends. However, it is important to note that data collection and analysis can be complex and time-consuming, requiring careful planning and strategies to ensure effective outcomes.

Data analytics

Build a Single Source of Truth

Data lakes, data warehouses, and data marts enable businesses to capture, organise, and analyse vast amounts of data. By extracting and loading raw data from source systems, businesses can create a conformed data set for business intelligence and analytics. This 360-degree view of the business helps eliminate data silos and provides a comprehensive understanding of customer and product lifecycles.

Clean Your Data for Confident Decision-Making

Establishing trust in the data source is crucial. By programming "data quality rules", businesses can automatically enforce critical business requirements. For example, setting alerts when commercial loans exceed a certain percentage of the loan portfolio. Data visualisation tools also help identify data quality defects, ensuring data accuracy and integrity.

Enable Self-Service Data Requests for Employees

Self-service business intelligence empowers employees to access trusted data sources and make informed decisions in their daily roles. With proper permissions, employees can access relevant data and information to drive measurable success. This also allows employees to analyse data and provide recommendations and insights to management and executives.

Automate Routine Reports

Automating routine or manual reports ensures the timely delivery of key performance indicators to decision-makers. It streamlines processes, frees up staff time, and eliminates errors associated with manual reporting.

Capture Historical Data for Predictive Analytics

Collecting and organising historical data is essential for predictive analytics and understanding trends. It helps identify seasonality, customer behaviour patterns, and sales velocity, enabling businesses to make data-driven decisions and predictions.

Leverage Pooled Data Analytics

Some business questions require a large volume of data for accurate insights. Data pooling allows businesses to collaborate and amass extensive data sets, enhancing statistical analysis capabilities and providing a more comprehensive understanding of the market and customers.

By investing in data analytics, businesses can improve their decision-making, optimise operations, enhance customer experiences, and ultimately, increase their profitability and competitiveness in the market.

Data security

Protecting Sensitive Information

The primary goal of data security is to safeguard sensitive information. This includes corporate and personal data, such as financial records, customer details, and intellectual property. Losing such information can have severe financial and legal consequences. The average cost of a data breach is approximately $4 billion, with costs associated with compliance fines, court fees, and identity theft prevention services.

Maintaining Business Operations

Compliance and Reputation

Many industries have strict data protection regulations, such as the General Data Protection Regulation (GDPR) in Europe. Compliance with these regulations is essential to avoid legal repercussions. Additionally, data breaches can lead to reputational damage, causing customers to lose trust in the company, resulting in a loss of business and long-term reputational harm.

Competitive Advantage

Investing in data security can provide a competitive advantage. Customers are more likely to do business with companies that prioritize data protection. By ensuring data security, businesses can attract and retain customers, gaining an edge over their competitors.

Cost-Effectiveness

While investing in cybersecurity may seem costly upfront, it is more cost-effective than dealing with the aftermath of a data breach. Preventative measures, such as security consulting, managed security services, and data classification software, can help mitigate the risk of breaches and reduce potential financial losses.

Risk Mitigation

In conclusion, investing in data security is crucial for any business aiming to protect its operations, customers, and reputation. With cyber threats on the rise, data security measures are essential to safeguard sensitive information and maintain a competitive advantage in the market.

Data quality

Good data quality is essential for businesses to make informed decisions. For example, in the context of COVID-19 vaccinations, reliable data must include a patient's date of birth, the date of each dose, the type of vaccine, the number of required doses, and the location of each dose. Poor data quality can prevent stakeholders from making informed decisions and may even cause harm, as in the case of public health outcomes.

Investing in data quality can be challenging due to the difficulty of assessing the intangible asset of data and justifying the investment. However, it is a worthwhile journey, as it can lead to significant long-term benefits, such as cost savings, revenue growth, and improved profit analysis. Here are some ways to improve data quality:

• Sponsor beyond budgets: Articulate the importance of data quality, inspire cross-functional collaboration, prioritize progress, and hold managers accountable for long-term success.
• Invest in data quality and transparency tools: As the volume and variety of data increase, specialized software can automate quality management for profiling, cataloging, metadata, and lineage.
• Adopt DataOps automation practices: Integrate DataOps practices to promote collaboration, communication, and automation, bringing together business, applications, operations, and data teams to speed up feedback cycles and reduce delivery times.
• Embrace a stewardship culture: Incorporate stewardship into organizational norms and habits, ensuring shared responsibility for identifying and resolving quality issues and following governance practices.
• Build a data management team: Hire dedicated professionals, such as Chief Data Officers, data analysts, and data management consultants, to evaluate, design, and execute data quality solutions.

By implementing these strategies, organizations can improve their data quality, leading to better decision-making, increased efficiency, and a competitive advantage in the market.

Data pooling

The World Health Organization (WHO), for example, launched a data pooling platform in response to the COVID-19 pandemic. This allowed governments and businesses worldwide to share information about vaccines and health data. Data pooling can also be used to prevent and reduce fraud. Austrac, for instance, pools transaction data from banks to prevent a range of criminal activities, while the insurance industry pools claim data to identify fraud.

In the Internet of Things (IoT), data pooling allows manufacturers to have a data allowance for all their devices, rather than paying per device. This means they don't have to pay extra for devices that exceed the data limit. Data pooling can also be useful for phone contracts, where it can simplify billing and even out data consumption across different devices.

Data management

Build a Single Source of Truth

By leveraging data lakes, you can capture, organise, and analyse vast amounts of data. Extract and load raw data from source systems, maintaining tables, columns, and fields in their native format. Blend this data into a conformed data set to create a 360-degree view of your business and eliminate data silos.

Clean Your Data for Confident Decision-Making

Establish "data quality rules" to build trust in your data sources. Program these rules to run at a predetermined frequency to enforce critical business requirements. For example, set a rule that "commercial loans should not exceed 15% of the loan portfolio" and create alerts at various thresholds.

Enable Self-Service Data Requests for Employees

Implement self-service business intelligence tools to simplify and automate employees' duties. Provide employees with access to a trusted data source to make informed decisions in their daily roles, ultimately driving measurable success. Ensure timely delivery of information to key stakeholders and grant focused views of relevant data.

Automate Routine Reports

Automate the creation of routine or manual reports to streamline the delivery of key performance indicators to decision-makers. This frees up staff time and reduces errors that can arise from manual processes.

Capture Historical Data for Predictive Analytics

Collect data over time to conduct predictive and historical data analysis. Identify seasonality trends, customer behaviour patterns, and changes in sales velocity. Utilise this data to build descriptive, predictive, and prescriptive analytics capabilities.

Leverage Pooled Data Analytics

Join forces with others in your industry to amass a large volume of data through data pooling. Anonymise and enhance the quality of the pooled data to strengthen your statistical analysis capabilities.

Additional Tips:

• Implement strong file naming and cataloguing conventions to ensure data is easily findable and accessible.
• Consider metadata carefully, including information about the data's content, structure, and permissions for future discoverability.
• Invest in quality data management software to effectively manage and govern your data.
• Prioritise data experimentation and analytics at the leadership level to foster a data-driven culture.
• Ensure data security and privacy by establishing a secure environment with strong privacy standards.
• Define clear data management goals and create a data governance framework to outline roles, responsibilities, and processes.
• Ensure data quality assurance through ongoing monitoring and improvement to maintain data accuracy and reliability.
• Streamline data integrations to enable seamless data flow between systems and provide a unified view for analysis and reporting.
• Enforce proper data documentation and metadata management to facilitate understanding of the context and meaning of your data.
• Implement master data management to maintain consistent and accurate core data, such as customer information, product details, and employee records.
• Leverage data analytics and reporting to extract actionable insights and make data-driven decisions.
• Perform regular data audits and compliance checks to identify areas for improvement and ensure adherence to evolving data regulations.

Frequently asked questions