Realizing the full potential of data and analytics is a challenge in many organizations. With so much data to leverage, businesses must adopt modern tools and technologies that enable all users to self-service their own data prep and analytics needs.
Data analytics help fast-paced businesses increase operational efficiencies, drive customer retention and growth, and optimize organizational processes. The most successful businesses capitalize on data analytics’ ability to uncover market trends, map out the competitive landscape and evaluate performance to set themselves apart from the competition. Insight-driven businesses are also leveraging data analytics to better understand internal operations, measure brand reputation and predict customer behavior.
Despite the many advancements enabled by analytics across multiple industries, even some of the most sophisticated organizations don’t realize the full value of their data. They run into obstacles turning data into insights with inefficient data processes, poor data quality and absence of business alignment. Unfortunately, these are not the only data challenges businesses face. Too often over-reliance on IT, overly technical tools and outdated technologies often prevent organizations from using data to drive greater business impact.
Breaking Down Analytical Obstacles
One fundamental issue organizations face is how they should maintain and manage their enterprise data supply chain. When data is collected, shared and deployed at today’s rapid pace, it is often a challenge for organizations to swiftly convert data into actionable and trustworthy insights.
With so much data at stake, the IT department is often the only organizational resource equipped with the tools and expertise to manage, prepare and analyze large-scale data volumes. However, business users are the primary consumers of enterprise data. All too often, these users are left confused and confounded by the technical languages IT uses such as SQL, Java, Python and more.
Compounding these data issues further is widespread reliance on conventional extract, transform and load (ETL) technologies for data integration and analysis. While these tools serve a purpose and facilitate data movement in critical processes, we can’t ignore the need to establish collaboration between business and IT to enable business users to get involved in strategic data analysis, which isn’t always easy with established data integration tools.
Transitioning Toward Business Enablement
Most individuals typically lack the knowledge required to leverage ETL and other highly technical tools for data analysis. Instead, they must submit data requests to their IT department, who are already busy with a myriad of data requests from other business users. As a result, it takes weeks to fulfill requests as IT works to address the backlog of work already in the queue. By the time each request is fulfilled, the results may well be outdated or irrelevant to the requester.
By moving toward more modern technologies that enable self-service data analytics options, organizations provide greater transparency and flexibility, and enable more users to perform data analysis without continued reliance on limited IT resources. Building a common understanding of usable data for the individual consumer is the greatest challenge. By implementing a governance-first approach to data management enables more collaboration and facilitation of data exchanges.
The latest data management technologies also offer the advantage of integrated capabilities, offering organizations all-inclusive options that bring data governance, data quality, and data analytics together to give business users high quality data that they can trust. Now that trust is well-established, diverging lines of business can easily collaborate as they extract, prepare and analyze data from a variety of data sources, producing an all-inclusive and dependable source for analysis that all business users can leverage. And there’s an added benefit of eliminating the lengthy wait times that most end users expect today.
By selecting a tool that provides complete lineage of every data supply chain, combined with extensive capabilities to automate data blending and cleansing, users can search, find, create, and manage insights—all while ensuring the trust is established. This allows organizations to substantially expand analytical possibilities and put the power of data at the fingertips of any user.