Data has often been called the new oil and for good reasons. Just like oil was once a highly valuable resource that fueled all industries, data has become a critical raw material that powers our modern economy, which is digitally driven. It was in 2006 when renowned data scientist Clive Humby gave a talk titled Data is the New Oil. The analogy of data being the new oil has now existed for several years. Like oil, if unrefined, it cannot be used. Data must be broken down and analyzed to have value.
Businesses, these days, increasingly rely on data for effective decision making and competitive advantage. Many companies are getting value for data as they use it as an essential strategic asset to gain valuable insight into customer behavior, operations, and markets. Customer data can help businesses identify new opportunities, optimize marketing strategies, and improve their products and services. Enterprise data is also crucial as it can help to improve operational efficiency by providing insights into workforce productivity and delivery management. The value of any data lies in its ability to assist businesses in the decision-making process. For example, a Harvard Business Review (HBR) study for Google Cloud found that organizations that made decisions based on data during the COVID-19 pandemic were positioned well to navigate challenges.
However, not all companies are equally adept at deriving value from data. While some companies have invested heavily in data management and analytics, others may lack the necessary resources and skills to collect, manage, and analyze data effectively. Additionally, regulatory or ethical concerns may limit the use of specific data types.
During the COVID-19 peak, companies had to make a difficult decision about surviving or thriving in a rapidly-changing marketplace. They were forced to swiftly embrace digital models in their operations. As a result, the demand for high quality, organized data went through the roof. The companies then started realizing the need to get the data collected, analyzed, and used for decision making. The treatment of data as oil is beginning to make sense now.
Data Management is just as important as Data
Data management is necessary to ensure that data is reliable, usable, and protected, since it is the fuel that powers the decision-making process. Like oil, it is a critical resource and must be managed and maintained well to achieve the best results. Organizations that integrate data into their strategy outperform their peers in their business growth. With the growing importance of data, the need for effective data management has only grown. No wonder, the global data management market size was valued at USD 84.55 billion in 2022 and is projected to grow to USD 170.46 billion by 2029. However, with growth come challenges. Most companies are facing challenges in managing their enterprise data.
Challenges in Data Management
While data, in general, can be a valuable resource, unstructured or poor quality data may have very little value for businesses. Which is why, data management is essential. Data management challenges may include handling voluminous data and addressing quality, security, and privacy concerns.
- Volume: Managing and analyzing large volumes of unstructured data can be challenging. This is especially true for unstructured data, which can be difficult to search and organize.
- Quality: Poor data quality can lead to inaccurate insights and decisions, resulting in financial losses. For example, if a company’s customer data is incomplete or inaccurate, it may miss valuable opportunities to upsell or cross-sell to customers.
- Security: With the increasing amount of data companies collects, they must protect it from cyber threats and other security breaches. Also, organizations must comply with data privacy regulations, such as General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), which can be complex and time-consuming.
Best Practices in Data Management
Integrated high-quality data can positively impact business strategy and outcomes and improve enterprise operations. It is excellent for digital business models that rely heavily on analytics. Organizations must implement data management best practices to manage their data resources effectively.
Data governance and policies are imperative to successful data management. Organizations must have a proper framework to manage the availability, usability, integrity, and safety of the data used. The framework must include data governance policies, processes, and tools to ensure data is collected, stored, and used consistently and securely.
One good case study in data governance and security is the Equifax data breach in 2017. Equifax is a credit reporting agency that suffered a massive data breach affecting over 143 million consumers, including sensitive information such as social security numbers, birth dates, and addresses. The breach was caused by a vulnerability in the company’s web application framework, allowing hackers to access sensitive information. This breach highlighted the importance of data governance, explicitly ensuring data security and privacy.
The companies must train their staff on data security protocols for access controls, data collection, storage, and disposal. Keeping up-to-date with the latest data regulations of the country/region and adhering to them promptly is also incredibly vital.
Data quality management is also essential to enhance the efficiency of data management. Organizations must establish processes and procedures to ensure data is accurate, complete, and consistent. Data profiling, cleansing, and validation can also help to improve data quality.
Organizations must also invest in data analytics and business intelligence tools like AWS, Microsoft Azure, Google Cloud, Tableau, Microsoft SQL Server, and IBM Infosphere Master Data Management to manage and analyze their data. These tools can help organizations identify trends, patterns, and relationships in their data and provide insights into their operations and customers.
The world’s largest retailer, Walmart, is a brilliant example of efficient data management and analytics. The retailer has implemented a comprehensive data strategy to track shopping patterns, anticipate customer needs, and optimize operations. As a result, it has enabled Walmart to maximize customer satisfaction, drive sales, and become a leader in the retail industry. Machine learning, artificial intelligence, and predictive analytics are some of the tools Walmart has used to streamline processes and increase efficiency.
The Future of Data Management
The future will be shaped by emerging technologies, such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). These digital technologies transform how organizations collect, analyze, and interpret data. For example, AI and ML can help organizations make more accurate predictions and identify patterns in data that may not be immediately apparent. In addition, IoT can generate vast amounts of data to be analyzed to improve operations and customer experiences.
As the importance of data in decision-making continues to grow, data management will become a core competency for organizations, and those effectively managing data will have a significant business advantage in the digital era.
Organizations, big or small, must continue to invest in data management while focusing on technology and infrastructure and using skills and expertise to create a culture that values data-driven decision-making. By doing so, they can ensure being well-positioned to harness the power of data to drive innovation, improve operations, and create value for their stakeholders.
