Reliable Data In The Blink Of An Eye – Why Automation Remains a Success Factor

This is shown in The State of Data Management Report by Fivetran and Wakefield Research. The global survey of “Data & Analytics” executives examines the impact of poor data management on internal business processes. The results are alarming: 85 percent of respondents reported financial losses because decisions were made based on poor data.

The problem is not the business intelligence (BI) tools used for data analysis or, for example, the analytical methods applied. It is the underlying infrastructure and thus the unreliable data transfer between isolated applications or databases and cloud destinations such as data lakes and data warehouses

Manual maintenance: a permanent construction site for data engineers

The maintenance effort to ensure the reliability of data pipelines and keep them up to date represents the biggest cost trap. Pipelines move data between sources and destinations – and despite the simple concept, for most companies the process is resource-intensive. A whopping 80 per cent of companies have to completely rebuild their pipelines after deployment. For 39 per cent, re-implementing pipelines is even more common.

The task of keeping data pipelines running smoothly is the responsibility of data engineers. Although highly qualified and entrusted with tasks that form the backbone of the company, they still regularly have to struggle with mundane maintenance tasks. The lost time is not spent on providing reliable data for important analyses. In 82 per cent of companies, data engineers spend more than a quarter of their time building and maintaining data pipelines.

Fluctuation & high personnel costs

A calculation example illustrates the problem: according to Glassdoor, data engineers in Germany earn an average of 63,000 Euros per year. With ten employed data engineers who spend a quarter of their time on tedious maintenance work, this inefficiency costs a company almost 160,000 Euros a year.

Moreover, the unprofitable allocation of resources not only reduces the competitive advantage of companies, but also demotivates employees. Data engineers who maintain and service data pipelines on a daily basis will look for more interesting jobs in other companies in the long run. It is not easy to fill these positions due to a shortage of skilled workers – the competition for data engineers is tougher than ever before. 83 percent of managers see a lack of budget for additional skilled personnel as an obstacle to expanding their data management team.

Managing data properly

Are more data engineers really the solution to unstable data pipelines and insufficient data? Hardly. Almost a quarter of companies run more than 50 data pipelines. Managing the enormous maintenance effort manually is simply not an option. Thus, the case for automation could hardly be stronger. This is especially true when you consider the exponentially growing volumes of data year after year. The time will come when data engineers are simply no longer able to extract relevant information and new insights from the ever-growing data pool.

Fortunately, technology already has an answer: automating data pipelines. This means that providing analysis-ready data, which used to take days, now only takes a few hours. Sixty-nine per cent of data and analytics leaders are confident that business results can improve when their teams are freed from time-consuming maintenance tasks. The know-how and skills of data engineers are also far better served elsewhere, for example in business decision-making and the analysis of business-critical processes. This leads to the next logical step for companies: Implementing automated solutions to relieve data engineers and involve them in decision-making processes instead.

Positive effect: other areas of the company also benefit

Increased effectiveness and speed are only the beginning of a long list of benefits. For example, automation can eliminate significant data security and privacy concerns. The current automated solutions meet all security and data protection standards such as SOC Type 2 and PCI and thus automatically create a reliable compliance basis to conscientiously fulfil regulatory requirements and guidelines (e.g. GDPR). This also further relieves the burden on IT and security teams in companies.

The cross-team and automated provision of data has a positive impact on overall company productivity. For example, marketing teams analyse trends in customer behaviour before designing the next campaign and CFOs review current reports to identify new growth potential. Automation not only makes presenting data quicker and easier, but also takes care of day-to-day tasks. This leaves employees with more time and energy to focus on more important projects

Optimal data management is automated

A technology stack that drives the automation of data pipelines helps data engineers, analysts and data scientists to extract real value from existing data and gain new insights. If you can rely on up-to-date and clean data, you can also rely on the decisions based on it. Business results can be sustainably improved and data engineers can be effectively deployed. In short, it’s time to move to automated, reliable data pipelines so that digital transformation can pick up speed.