In today’s digital age, organizations of all sizes are overwhelmed with data. Despite this, many businesses are still unfamiliar with how to effectively leverage data correctly in order to remain competitive. A recent 2023 study from The Decision Dilemma by Oracle and author Seth Stephens-Davidowitz found that 78% of business leaders complain they are being bombarded with more data than ever before. To make matters worse, of those decision-makers, 86% state that the volume of data is making decisions in their personal and professional lives more complicated, not less.
To address these issues, organizations need to take steps to mitigate the ongoing concerns of data management. However, to master this area of expertise takes an understanding of the larger data landscape. By taking a closer look at the specific challenges of data management and forming a proactive strategy, organizations can begin to conquer the massive data frontier that’s undoubtedly transforming modern-day businesses.
General Manager, Data & Analytics, insightsoftware.
Challenges of effective data management
It’s no secret that businesses are struggling with data management. This is especially true when data is fragmented by departments, making it challenging for users to share and collaborate. Siloed data poses significant collaboration challenges, such as reporting delays, limited visibility of data, and poor data quality. If organizations are unable to collaborate effectively, teams will struggle to promptly respond to leadership needs and custom 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 each week on operational reporting. The continued ineffective and disjointed reporting results from siloed data, further reinforcing a lack of an open data culture that many companies promote yet struggle to operationalize.
Too often, organizations continue old patterns and build data programs that are searching for a business problem rather than asking key questions about what data we need to run the business. As a result, it’s difficult to make informed decisions when organizations can’t see their data or understand how it is being used in their business setting or with the right context. Take, for example, Capital One’s study, “Discover data management trends,” which reported 76% of business leaders found it difficult to understand their data in 2022. This finding highlights how many organizations don’t have a solid foundation for their data infrastructure.
It’s also common for organizations to become complacent in their data management strategy without considering how it needs to evolve and meet end users who have the domain expertise where they are. Additionally, if an organization is moving or changing its respective data management processes, this transition is more challenging for those who do. Rather, given the advancement of cloud computing services, data governance, and data fabric offerings, there are ways to look at the operating environment not as a consolidation project but instead as bringing the correct data tools to the end users. How organizations can adopt a democratized and open fabric while also employing the right data management strategies to support faster innovation and adoption is crucial.
Turning obstacles into opportunities
Data problems are not the end-all-be-all for organizations. The reality of the situation is that businesses can take strategic steps to address ongoing data concerns. In fact, the best way for CFOs and additional operating leaders to approach data management concerns and not embark on long-drawn-out transformation projects is by specifically focusing on enabling non-technical employees to generate their own analyses quickly, building on quick wins. Doing so starts to build a data culture of self-service and context around domain-specific data environments that are valuable steps in the data-driven journey. It’s evident 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 troubleshooting a growing backlog of report requests from operational teams. The first step is removing manual processes and one of the report builders and flipping to enabling a sub-set of users where they are today – spreadsheets, BI tools, and or other go-forward deemed analytical programs. For example, this allows operational teams to spend less time collecting and processing data and more time analyzing it. Additionally, following this process will reduce reliance on key individuals because the right software can enforce process rigor and make it easier to onboard new, less-experienced staff.
By taking a step back and understanding where the company is today and what data-driven questions it needs solving, an organization can determine exactly what tools and resources are needed to maximize valuable data insights. The next step is for organizations to harness the power of context, automation, and intelligence.
The power of context, automation, and intelligence in crafting strategic strategy
Ultimately, the key to implementing an effective data strategy starts with context – how the business can best leverage specific data to run the business. Identifying quick wins that bring together disparate teams to focus on cross-department data collaboration and sharing builds confidence and FOMO. From this point on, leaders can employ various automated techniques to rapidly reduce data gaps, handoffs, and manual processes that simply waste time and introduce biases.
Some benefits of automation and successful data management include improved data quality, increased efficiency, reduced errors, improved compliance, better decision-making, and cost savings. Further, sound practices enable the foundation for innovation, such as leveraging AI and machine learning, where appropriate. Whether organizations need to get to market faster, streamline operational procedures, or create a clear view of company data, automation, and data management solutions, give teams a better handle on one of their most critically underserved assets – data that, coupled with employee domain knowledge can help organizations become more agile, predictable and ultimately reduce unnecessary operational overhead.
Businesses are currently drowning in data, and the vast majority of them do not fully comprehend that data can be a massive advantage if leveraged correctly. More importantly, effective data management is not only core to operations – organizations can often see a return on investment once data is taken seriously. Yet critical obstacles still must be overcome before companies begin to see meaningful benefits from their big data and intelligence investments. 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 themselves apart from their competitors.
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