Leverage effective data management for competitive advantage
In today’s digital age, organizations of all sizes are inundated with data. Despite this, many companies are still unfamiliar with how to effectively use data in the right ways to stay competitive. A recent 2023 survey from The Decision Dilemma by Oracle and author Seth Stephens-Davidowitz found that 78% of business leaders complain that they are being bombarded with more data than ever before. To make matters worse, 86% of these decision makers say the amount of data makes decisions in their personal and professional lives more, not less, complicated.
To address these issues, organizations must take steps to address lingering concerns around data management. However, mastering this area of expertise requires insight into the larger data landscape. By taking a closer look at the specific challenges of data management and formulating a proactive strategy, organizations can begin to conquer the vast data frontier that is undoubtedly transforming modern businesses.
General Manager, Data & Analytics, insight software.
Challenges of effective data management
It’s no secret that companies struggle with data management. This is especially true when data is fragmented by department, making it challenging for users to share and collaborate. Siled data poses significant collaboration challenges, such as reporting delays, limited data visibility, and poor data quality. If organizations cannot collaborate effectively, teams will struggle to quickly respond to leadership needs and customized data queries required to navigate the business through changing landscapes. This is evident in our recent research, which found that more than two-thirds of IT and finance professionals waste an entire day every week on operational reporting. Persistent ineffective and inconsistent reporting is the result of siled data, further reinforcing the lack of an open data culture that many companies promote but struggle to operationalize.
Too often, organizations continue old patterns and build data programs that look for a business problem instead of asking important questions about what data we need to run the business. As a result, it is difficult to make informed decisions when organizations cannot see their data or understand how it is used in their business environment or in the right context. Take for example Capital One’s research, ‘Discover data management trends’, which found that 76% of business leaders found it difficult to understand their data in 2022. This finding highlights how many organizations do not have a solid foundation for their data infrastructure. .
It is also common for organizations to become complacent in their data management strategy, without thinking about how it needs to evolve and meet end users who have the domain expertise where they are. Additionally, if an organization moves or changes its data management processes, this transition is more challenging for those that do. On the contrary, given the advances in cloud computing services, data management and data fabric offerings, there are ways to view the operating environment not as a consolidation project, but instead as providing the right data tools to the end users. How organizations can adopt a democratized and open structure while deploying the right data management strategies to support faster innovation and adoption is critical.
Converting obstacles into opportunities
Data problems are not the only problem for organizations. The reality of the situation is that companies can take strategic steps to address ongoing data issues. The best way for CFOs and additional operations leaders to address data management issues and not embark on lengthy transformation projects is to focus specifically on enabling non-technical employees to quickly generate their own analytics , building on quick wins. When you do this, you start building a data culture of self-service and context around domain-specific data environments that are valuable steps in the data-driven journey. It’s clear that intuitive, self-service data analysis and reporting features are essential, not just a nice-to-have.
To achieve business agility, technical staff, like IT, must spend their time solving complex technology challenges, not creating or resolving a growing backlog of report requests from operations teams. The first step is to remove manual processes and any of the report builders and enable a subset of users where they are now: spreadsheets, BI tools and/or other cutting-edge analytical programs. For example, this allows operations teams to spend less time collecting and processing data and more time analyzing it. Additionally, following this process will reduce dependence on key people, as the right software can enforce process accuracy and make it easier to onboard new, less experienced staff.
By taking a step back and understanding where the business is today and what data-driven questions it needs to solve, an organization can determine exactly what tools and resources are needed to maximize valuable data insights. The next step is for organizations to leverage the power of context, automation and intelligence.
The power of context, automation and intelligence in creating a strategic strategy
Ultimately, the key to implementing an effective data strategy starts with context: how the company can best use specific data to run the business. Identifying quick wins that bring disparate teams together to focus on data collaboration and sharing across departments builds trust and FOMO. From this point, leaders can use a variety of automated techniques to quickly reduce data breaches, transfers, and manual processes that only waste time and introduce bias.
Some of the benefits of automation and successful data management include improved data quality, increased efficiency, fewer errors, improved compliance, better decision making and cost savings. Furthermore, good practices form the basis for innovation, such as the use of AI and machine learning, where necessary. Whether organizations need to get to market faster, streamline operational procedures, or create a clear view of business data, automation, and data management solutions, give teams better control of one of their most underutilized assets: data that, combined with employee domain knowledge, can help organizations become more agile and predictable and ultimately reduce unnecessary operational overhead.
Businesses are drowning in data right now, and the vast majority of them don’t fully understand that data can be a huge advantage if used properly. More importantly, effective data management is not just core to business operations; organizations can often recoup their investment once data is taken seriously. Yet critical obstacles remain to be overcome before companies begin to see meaningful benefits from their investments in big data and intelligence. By addressing the specific challenges of implementing effective data management and taking proactive measures to address them, organizations can ensure a solid foundation for larger data initiatives, setting them apart from their competitors.
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