Business leaders in every type of organization recognize that good decisions are based on good data, which makes business intelligence (BI) and reporting more important than ever. Unfortunately, not all data management systems are keeping pace with the heightened demand. That’s why managers are often unsure of which reports to trust, struggling to recruit talented people, and too swamped to innovate. Turning data into actionable intelligence can be costly, complex, and confusing.

According to a recent survey, many businesses are afflicted with “data sprawl,” and some compare their data management systems to “dumpster fires.” It’s fair to blame the outdated manual processes still being used to collect and maintain data. Now organizations are learning that automated cloud-based technology can help their data teams save time and effort, allowing them more time to innovate. How can data sprawl be contained and dumpster fires prevented? Automation is the key.

The High Cost of Collecting Data 

Commissioned by Hakkoda, the data sprawl survey drew responses from more than 300 data and analytics professionals from mid-sized to large companies during September 2021. “Collecting data is easy, but turning data into intelligence is hard, expensive, and not always entirely effortless,” the report concluded. “Which is why the cost of data is skyrocketing.”

Although we’ve gotten to a good place where business leaders value reporting and BI, vital data may be poorly managed. Data maintained manually is a labor-intensive, time-consuming, and high-priced hindrance. It’s no surprise that across the entire data analytics life cycle, 54% of the survey respondents said that BI and reporting are their highest costs. 

Automated data lineage is a cost-effective way to view the entire data landscape, mapping data flows and dependencies across all systems. Automated reporting tools can search the BI & analytics landscape for specific metadata, and easily share it with all employees, like business users and developers, not just data specialists and database administrators. 

Complexity and Confusion

Reporting tools become problematic when each team uses different tools. Organizations end up deploying multiple unrelated and sometimes redundant systems, all separately managed. According to the data sprawl survey results, 32% of respondents use four or five BI or reporting applications, 24% use more than 10 applications, and 35% use five or more data warehouses. Each app collects huge amounts of data every day. Duplicate and sometimes conflicting data is inevitable, along with dead-end pipelines that are no longer needed. 

Worse, the chain of source data is constantly changing in multiple places, which fundamentally undermines data-based decisions. When a developer changes a single calculation, for instance, data is affected in many different reports along the line unknown to teams relying on those reports. 

Cross-platform automated data lineage puts an end to silos. Among other functions, it can automatically uncover the source of reporting errors, analyze the potential impact of a broken process, and discover parallel processes that are duplicating work. Without the benefits of automation, analysts and data specialists might spend days or weeks tracking down those answers.

A Question of Trust

This leads to an essential question: When varying reports built on contradictory data from unknown sources are circulated, which is the one to trust? No matter how much money and talent businesses may commit to innovation, it can’t happen without trusted data. Allowing automation to centralize and control data eliminates this dilemma.

Data professionals who spend most of their time fighting ongoing fires manually are left with little time to focus on innovation. And, along the way, data that could be leveraged for intelligence gathering and inspiration is useless if it can’t be trusted. Technology can resolve those issues for the data team and show them where to invest their efforts to get the benefit of trustworthy information.

The recruiting challenge

Talented data professionals want to engage with high-level logic and analytics to study the complex relationships within data, gather relevant insights, and push for new and better insights. This can be a challenge for recruiters. Weighing data experts down with manual investigations is a sure path to frustration. They do not want to spend their time chasing down data sources. 

Automation allows data professionals to concentrate on algorithms and analysis rather than the technical aspects of managing and saving data. Of course, at the moment, highly qualified people may be hard to find. Survey respondents cited “insufficient internal expertise” as the main factor blocking innovation (43%) along with a lack of data resources because resources are “allocated elsewhere” (42%).

On the positive side, moving to automation opens up new possibilities for recruiters. They gain the flexibility to hire people with talents more general than technical, since every employee won’t need to track data sources.  

Technology Delivers Automation

Only recently has technology made it possible to map and model all data and connections from a range of tools — databases, reporting, extract-transform-load (ETL) processes, customer relationship management systems (CRMs) — on a single platform. Even newer is cloud-based data management, which is available to everyone and saves organizations from the complexities of adopting and installing new data systems. 

In another new development, every department across the enterprise is expected to base its decisions on data (e.g., product managers need information on product usage), and that practice is scaling up fast. Data lineage permits business leaders to travel backwards to the original data source, getting a close look at many data processes along the way, and finding exactly where the data chain was broken. 

It’s important to note that companies today already have many solutions at hand. But modern data management systems don’t require lots of tools or long processes. In fact, new technology provides more answers using fewer tools. The system can track an entire data journey in just a few minutes to reveal the full impact of any data change and know which reports will be affected.

With today’s new tools, automating data processes is quick and easy— strictly plug-and-play. There’s no need for businesses to reorganize data, set up a new environment, or bring in another vendor. The idea is to ease the burden on companies, not give them more work to do. There may be one drawback to automated data management solutions, however, and it might give some people pause: Decision-makers have no more excuses to rely on their gut feelings.