Fast data analysis - The best methods

von | 11 January 2018 | Basics

Time is one of the most critical factors in business. Since the global networking of companies, decisions have to be made quickly and sometimes have an immediate effect. Since data analyses are increasingly the basis for decisions, fast data analyses are therefore not an end in themselves, but in many cases decisive for success.

Real-time data analysis has become standard in many business areas. But fast data analysis also plays a major role in employee motivation and productivity. Hardly anything is more annoying than an endlessly running hourglass and the associated waiting times that cannot be used sensibly.

1. ensure optimal data quality

The first effective step that leads to faster data analyses already starts from of the actual data analysis. Before each analysis, all relevant data should be checked for quality. Manual cleaning processes are one of the most time-consuming factors in data science projects. That is why it is recommended, Automatic and routine measures to take measures to ensure a consistent Data quality ensure. With data cleansing and data profiling, data can be cleaned and equipped with all relevant metadata. This allows them to be identified more quickly in searches, which leads to faster processes overall.

2. cloud solutions for faster data analysis

Cloud solutions offer numerous advantages from which companies can benefit. One of these advantages is that data analyses can often be carried out faster compared to on-premise solutions. The reasons for this are manifold. In the Cloud for example, relevant data can be accessed very quickly and selectively. On the one hand, in-house systems are very complex and specialised, so they are not always optimised for fast data access. In contrast to cloud providers, companies cannot always use the latest and fastest hardware. The cloud also avoids the danger of data being stored scattered throughout the company. Whoever relies on a Cloud solution has a central placewhere all relevant data is stored.

3. the data strategy must be right

Many companies spend a lot of time collecting, archiving and managing vast amounts of data. This costs both a lot of time and a lot of money. Instead of slowing down the speed in the system with numerous non-targeted data projects, in our experience it makes sense to focus on a few Focus on a few profitable and insightful use cases. Results are available more quickly and agile teams can be formed that are responsible for data analysis projects. These are characterised by the fact that they are independent of rigid hierarchies in companies. It is often necessary to think "outside the box".

4. systematically dismantle legacy systems

As already mentioned, the speed of data analyses often suffers under so-called legacy systems. Also known as "legacy systems", these IT structures slow down digitisation in companies overall. The term legacy systems refers to both hardware and software systems. Even with the latter, it is usually worthwhile to fall back on the latest programmes. Many companies shy away from the sometimes not inconsiderable investments in a new IT architecture and try to work with existing hardware for as long as possible.

The investments pay off quickly. Often, old systems are associated with significantly higher costs due to maintenance and long waiting times than new ones. As already mentioned, long waiting times also have an impact on employee motivation and productivity. Old systems therefore also cause hidden follow-up costs that are avoidable. Fast data analyses are based on a modern IT architecture in companies.

5. define processes, distribute responsibilities

Process optimisation ensures faster and better data analyses in the context of analysis projects. For example, one of the most common sources of error in data projects is the lack of carefully defined criteria for measuring success as well as return on investment. Also the lack of Data governance models for Big Data- and data analytics projects creates unnecessarily complex processes in companies because responsibilities are not clearly assigned. Companies, on the other hand, that consciously deal with the associated processes and responsibilities also create awareness for data analytics projects. Last but not least, this also reveals gaps in staffing so that missing expertise or equipment can be brought into the company.

Faster data analysis leads to digitalised companies

The goal of achieving faster data analyses in the company is anything but a mere end in itself, and more is achieved along the way than simply increasing the speed of data processing. Measures that lead to faster data analyses serve the overall purpose of improving the Digitisation in the company to drive forward. In addition, the formation of data silos can be avoided and legacy systems can be dismantled. In particular, if faster data analysis is understood as one aspect of a comprehensive data journey, companies can make use of Data a real added value drag.

<a href="https://www.alexanderthamm.com/en/blog/author/michaela/" target="_self">Michaela Tiedemann</a>

Michaela Tiedemann

Michaela Tiedemann has been part of the Alexander Thamm GmbH team since the early start-up days. She has actively shaped the development from a fast-moving, spontaneous start-up to a successful company. With the founding of her own family, a whole new chapter began for Michaela Tiedemann at the same time. Hanging up her job, however, was out of the question for the new mother. Instead, she developed a strategy to reconcile her job as Chief Marketing Officer with her role as a mother.

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