Organizations produce massive data streams with the acceleration of digitization. They need to become more data-driven to generate helpful insights for strategic decision-making.
Data is an invaluable asset that businesses leverage for their benefit. As per a recent report by Salesforce, “Untapped Data Research,” 80% of business leaders believe data is vital in making decisions. At the same time, 73% of businesses agree that it helps minimize uncertainty and make more appropriate decisions in business conversions. A data-driven organization fosters the culture of using data intelligently to make meaningful decisions.
Businesses must address these ten issues that prevent them from becoming data-driven.
Low-quality data is why organizations are reluctant to use data for smarter decision-making. But even if there is sufficient data, it lacks systematic data quality; the data becomes noisy while the insights become faulty.
Data confinement in functional silos outside the application or departments makes it difficult to access. Unlike modern automated data management, traditional organizations maintain data manually.
Employees find it difficult to access data if it is not centralized, making it challenging for decision-makers to plan and work with correct insights. Businesses must deploy the best infrastructure and tools to enable data sharing across departments. They must also station data governance policies establishing a unified source for the overall data.
Organizations require more than the right technology and strategy to foster a data-driven culture. They need to incorporate a data-first culture effectively. However, many organizations find building such a culture challenging, where most employees make gut-based decisions rather than data-driven facts. A data-driven culture also requires adequate skills starting from business leaders and gradually permeating through each department.
Collating data streams into a unified source or merging data into a single tool helps to visualize and analyze it collectively. However, it triggers security and privacy concerns in businesses, common among companies that store data on-premises.
Organizations must consolidate data streams from multiple data silos and multiple physical locations. An efficient and cost-effective way to achieve this is -on the cloud, which offers additional visualization tools, computing power when needed, and machine learning/anomaly detection. Speculations around data consolidation security and privacy concerns are understandable if businesses manage the cloud and data effectively.
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As per a recent report by Salesforce, “Untapped Data Research,” 73% of businesses plan to continue or maximize spending on data skills and training for employees to close the gap. While progressing towards becoming a data-driven company, businesses might find their biggest hurdles related to having the appropriate skillset and experience.
If businesses lack in-house experience, all the primary cloud providers have resource-approved service partners. These partners are validated to work with companies to implement and design a secure and inexpensive solution.
With the evolving digital landscape, companies have multiple data sources, including sales transactions, customer inquiries, product information, and social media interactions. The data sources have many types- numerical, text, and financial. These data sources are updated or generated at multiple frequencies, formats, and locations.
Since each can hold identical information as another data source, companies find it challenging to consolidate, cleanse, and standardize them into a structure to analyze and visualize quickly. While these processes are potentially costly and technically challenging, employing robust tools will help businesses reduce efforts.
A complete view of various data is critical for drawing insights from all. Short visualization makes it difficult to interpret inventory data, employee performance data, production information, vendor data, and financial transaction, hindering strategic planning.
Furthermore, traditional systems cannot process real-time data for insights into corporate processes. It makes it challenging for businesses to determine relevant data points to help them make decisions.
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The lack of adequate analytics capabilities makes it impossible for businesses to unlock the power of automation tools- AI and machine learning to gain better and faster insights.
AI-powered data systems enable organizations to automatically streamline data aggregation from numerous sources, correlate data points, recognize patterns, and present data intuitively to use as real-time information for business actions.
Businesses invest in an analytics platform to help teams to access data easily. However, they often do not use it efficiently. Irrespective of technology fit and training issues, businesses must not limit tool utilization only to data scientists and analysts.
Companies must deploy tools that make the data experience inviting and simple rather than making it hard and imposing to overcome an inclusive data culture.
Analytics technologies will not receive widespread acceptance unless businesses integrate them into the processes. They must tweak existing workflows to make the data a more natural extension of the employee’s operations rather than an added responsibility or task.
Transformation into a data-driven company is critical for many businesses since it ensures that employees embrace and adapt to innovative data-driven business approaches.
New tools and processes are likely to create skill gaps in new approaches. Businesses must address these gaps by assigning new roles and conducting effective training programs. Organizations must overcome these hurdles by building a strong data strategy to leverage the benefits of massive data streams.
These data strategies must remain pliable to changing requirements. Therefore, businesses must construct well-defined structured processes and invest adequately in digital tools. A clear vision and plans are critical for companies to learn and embrace data collectively.
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Apoorva Kasam is a Global News Correspondent with OnDot Media. She has done her master's in Bioinformatics and has 18 months of experience in clinical and preclinical data management. She is a content-writing enthusiast, and this is her first stint writing articles on business technology. She specializes in Blockchain, data governance, and supply chain management. Her ideal and digestible writing style displays the current trends, efficiencies, challenges, and relevant mitigation strategies businesses can look forward to. She is looking forward to exploring more technology insights in-depth.
