Article
With Big Data Comes Big Responsibility for Organizations
Big data has become integral to the modern world, revolutionizing how we collect, manage, and analyze information. It has opened up new possibilities and opportunities across various industries, from healthcare to finance to marketing.
Big data provides a more comprehensive understanding of trends and patterns by integrating diverse data sets to form a complete picture. This fusion facilitates retrospective analysis and enhances predictive capabilities, allowing for more accurate forecasts and strategic decision-making. Additionally, when combined with AI, big data transcends traditional analytics, empowering organizations to unlock innovative solutions and drive transformational outcomes.
According to Mckinsey Global Institute, data-driven organizations are not only 23 times more likely to acquire customers, but they are also 6 times as likely to retain customers and 19 times more likely to be profitable!
Big data brings big responsibility
Data-driven organizations have demonstrated a distinct competitive advantage in recent years. However, with big data comes big responsibility. Companies need to ensure their information is being used responsibly and ethically. Due to the superpowered nature of their work, wherein data is collected in fractions of milliseconds, proper data management is now more crucial than ever.
First, big data is …. actually big. Although new technologies have been developed to facilitate data storage, data volumes are doubling yearly. Organizations that struggle with data management and seek effective storage solutions are unlikely to achieve success. Moreover, it’s not enough to store data affordably and accessibly. Aside from storage, big data must be appropriately processed, cleaned, and formatted to make it worthwhile for analysis. This requires considerable time and effort due to big data’s size, multiple data sources, and structured, unstructured, and semi-structured data combinations.
Secondly, we are all aware of cybersecurity threats and how hackers are getting smarter daily. Hence, the old way of safeguarding information is no longer working. Organizations must find better solutions to – protect their systems and promote their immunity to ransomware and other data-focused attacks.
That’s not all! Data storage is directly linked to high energy consumption and carbon footprint. From a data management point of view, ensuring energy efficiency, reducing waste, and meeting sustainability targets while maintaining high performance and availability presents ongoing complexities for operators, – as it requires investments in greener technologies and operational practices.
Guidelines for building a successful big data foundation
To help leaders on their big data journey, we have put together some key best practices for managing data. Here are some guidelines for building a successful big data foundation.
Align big data with specific business goals
To unlock the genuine value from data, leaders must first set clear goals that connect day-to-day data usage with their organization’s broader strategic objectives.
Whether enhancing operational efficiency, optimizing marketing campaigns, or improving customer satisfaction, data management goals should directly support broader business objectives.
When data goals are weak, they lead to loss of time, and budget revisiting or reworking solutions, creating frustration at both operational and executive levels. This shortfall rings a death knell for a data value, resulting in missed data opportunities and inefficient operations, leaving many data initiatives forever incomplete. It drains people’s enthusiasm and weaves a narrative that ‘data does not fulfill its potential.
Resource management
Once the data management goals are articulated, allocating the necessary resources—budget, staff, and technology—becomes imperative. Vague or undefined goals may lead to resource misallocation and investment in areas that don’t align with the business’s needs.
Implement a flexible data governance framework
Leaders should implement a practical data governance framework. It involves setting clear rules and guidelines for how a company should use and manage data. It helps ensure data is consistent, high-quality, and secure across different departments and verticals. This includes determining who owns the data, who can access it, and how to meet compliance requirements.
Data governance and management ensure that everyone in the organization knows their responsibilities regarding data handling and keeping everything organized and reliable.
Prioritize data quality
‘Garbage in, garbage out’ theory applies to data management as well. Hence, it is necessary to prioritize data quality.
Maintaining clean and accurate data is critical for achieving reliable insights. Inaccuracies such as incomplete entries or invalid information can lead to flawed analyses and operational inefficiencies.
Leaders who invest in data quality can reduce operational risks and create more dependable dataset strategy execution. Moreover, clean, high-quality data supports better outcomes across analytics, reporting, and compliance efforts.
Implement robust data security and privacy measures
It’s very simple: both data security and privacy are non-negotiable because breaches can result in severe reputational and financial damage.
Leaders must proactively safeguard sensitive data against breaches and unauthorized access. Security measures such as role-based access controls, encryption, and incident response plans are essential to maintaining data integrity.
Likewise, data security and privacy practices should align with the organization’s ethical considerations. Firms should be transparent with users about how their data is collected and used, seeking explicit consent where required.
Ethical data handling strongly emphasizes the principle of ‘data minimization’ – it involves minimizing data collection—to the extent necessary for the intended purpose, thereby respecting individuals’ privacy and giving them greater control over their personal information. Organizations that prioritize security build trust among customers and stakeholders – moreover it enhances reputation and long-term business sustainability.
Streamline data integrations
Within an organization, data often resides in various silos, making it challenging to access and analyze. With the help of automation and integration tools, leaders can streamline the data flow between systems, further eliminating the need for human entry.
Likewise, leaders should implement data integration solutions that provide a unified view for analysis, reporting, and decision-making.
Data integration accelerates data access and enhances accuracy and consistency. When data is integrated from various sources, it undergoes transformation and cleansing processes to ensure it aligns with predefined data standards. It provides a more reliable and consistent dataset, reducing the likelihood of errors or discrepancies in analysis and reporting.
Foster a data-driven culture
Building a data culture will actively promote the importance of data in everyday decision-making. Leaders should model behavior that highlights data-backed strategies, setting the right tone for teams across the organization.
Likewise, incorporating user-friendly tools and ongoing training will enable employees at all levels to access, understand, and use data effectively. This approach ensures that data becomes an integral part of operations, not just a resource for analysts.
With time, organizations with a strong data-driven culture will easily adapt to new challenges and changes. It will foster collaboration, break down departmental silos, and align teams toward measurable goals, driving innovation and accountability.
Implement substantial practices
In today’s business landscape, effective sustainable data management is critical to a company’s overall success and resilience. It involves adopting environmentally responsible technologies and practices to mitigate the environmental impact.
No matter what the activity is—whether streamlining the data estate or using analytics to improve —the most important thing is to measure emissions at regular intervals. That sounds casual, but it’s absolutely critical. Leaders need to ensure they have the right elements/activities within each operational phase to enable and deliver accurate carbon reporting. Additionally, training team members in carbon literacy is important.
Stay updated on data management trends
Staying informed about the latest data management trends is crucial for organizations looking to stay competitive and agile. It enables organizations to adopt tools and methodologies that will further improve processes, reduce costs, and unlock new opportunities.
Moreover, leaders can regularly participate in industry events, network with thought leaders, and invest in training workshops to be well-informed and skilled about modern data ecosystems. Leaders who embrace these approaches are better positioned to navigate challenges and capitalize on the transformative power of data.
Best data management tools
Here’s a list of the most prominent data management tools in the market.
Ataccama ONE is a unified, AI-powered data management platform for automated data quality, data governance, and to master data management across cloud and hybrid environments. With more than 450 customers around the globe, this platform enables business and data teams to collaborate on creating high-quality, reusable data products and massively scale data-driven innovation while maintaining data accuracy, control, and governance.
Reltio’s AI-powered data unification and management capabilities—encompassing entity resolution, multidomain Master Data Management (MDM), and data products—transform siloed data from disparate sources into unified, trusted, and interoperable data. The platform merges and delivers interoperable data where and when needed, empowering data and analytics leaders with unparalleled business responsiveness.
Stibo Systems is a master data management platform enabling data transparency. Clients are thus able to gain an undistorted, comprehensive view of their master data, enabling them to make knowledgeable decisions and achieve ambitious scaling goals.
Profisee can solve the data quality issues that have been holding back so many strategic initiatives. Customers can leverage Profisee for a true Software-as-a-Service experience or maintain complete deployment flexibility in any cloud, on-premise, or via a hybrid model. With a low total cost of ownership, fast implementations and a truly flexible multidomain platform, Profisee fully cleans and unifies your enterprise data — so you can finally make it happen.
The future of data provides a opportunity for growth
Spurred by digital technology advancements, such as connectivity, mobility, the Internet of Things (IoT), and AI, the amount and availability of data is growing rapidly. According to a recent report by Statista, the global big data market is expected to reach an astounding $103 billion by 2027.
This explosion in data worldwide presents a challenge—but also an opportunity. Organizations that embrace data growth, invest in innovative technologies, and focus on efficient data management will be better equipped to extract actionable insights, drive transformation, and stay on the front lines of innovations.
In brief
When it comes to handling huge data sets, proper data management is critical for businesses to stay successful in the digital age. From ensuring data quality to complying with stringent regulation, fostering innovation, maintain substantial practices – proper data management helps in driving efficiency, productivity and overall organizational success.
