5 Challenges Your Company Has to Overcome to Succeed in Data Mining

Data lakes are failing and fast. They are not able to support the real time-to-market requirements of the new big data innovations. Many companies still think that data lakes are ineffective and expensive. Data Lakes to be a rich source of useful data for most companies. It is supposed to facilitate the collocation of data in several structural forms, schemas, and files. They are expected to make work easier, smoother and faster for big data operations and managers.

That is far from the reality we are seeing. Most companies assume Data Lake synonymous with disasters.

Data Lake can unfurl its precious resource of raw data if the user knows how to cultivate it. If the user lacks real-life experience, it will seem like a fathomless ocean of illegible hieroglyphs. Most new big data analysts and data miners are thrown by various paradigms required for harnessing the data.

The novelty of most data mining tools and frameworks demands specialized training. Without any practical experience and training, most programmers cannot create new tools or use existing ones since the turnover rate is extremely rapid. The programmers are slow, and the cost is high.

The only way out is working with thought leaders in data mining and big data analytics. Companies should also invest in training their employees. Some training courses like the MS Azure certification course is ideal for data miners. It will teach them how to optimize windows server workloads and work with IaaS architecture, tools, and services.

Most data lakes in the day do not have any standardized data infrastructure or implementation of the data designs. If your engineers know how to master Kafka, HBase, and Spark, it is great. However, they also need a sound knowledge of Hadoop to be able to harness the complete power of big data.

Your engineers need the knowledge for building complex data hierarchies and a well-engineered data lake. Your company should be able to enjoy a production-grade platform. This demands a good understanding of data architecture, data hierarchy, integration of designs, scalable designs and good testability. Otherwise, most companies end up suffering from deleterious instability that requires a complete rewrite.

Companies should not skimp on engineers’ budget. You need the assistance of trained professionals if you want to enjoy the actual benefits of having a data lake. If you already have data, lake and you have no idea how to use it for the company’s benefit. Go ahead and invest a little more in a team of experienced pros who can harness the potential of your business’s big data.

In most of the big data failures we have seen over the last couple of years, companies have (mostly inadvertently) put data engineers in business silos. A successful company will never isolate their data scientists and business op teams. The IT is an integrated part of your firm who can oversee communication, business operations, decision-making, and marketing strategies.