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

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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.

What makes data lakes look like stagnant bogs?

The total lack of hands-on experience

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.

Not enough reliable engineering skill

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.

You have an undeveloped operating model

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.

Data scientists use the tools approved by IT. The engineers in your team need to add applicability to the data productized and operationalized by your data scientists. Your company needs a robust operating model that can create a cohesion between the two roles and the two teams.

Most companies need a more reliable operating plan that will bring the big data engine and ecosystem together. Companies shape the organization structure and the model that can support the application of the methodical solution. When you are running a heavily data-driven model, you need to check that your business supports the deployment of such cohesive business models that bring teams together in a symbiotic model.

Poor data governance

What do you understand by data governance? We tend to describe it as a collection of processes that engage the most critical data assets throughout the enterprise. It assures that your data is reliable and trustworthy. In case, any discrepancies are arising from the low quality of data and data-driven activities; people are accountable for the said deviation.

In most cases of data failures, we have found the governance at fault. Poor governance and structure of management of data need to focus on the organization and growth of data in the first phase of the data lake formation. Multiple Users should be able to access data through various applications. Therefore, the data needs to be of consistently high quality. We need to take all productions systems and their architecture into account while talking about data quality.

Companies need to plan from the dawn of data. There should be a plan for every phase of data collection, growth and development. Hadoop is not just another storage system. Your teams should know the implications of using Hadoop and the advantages they can enjoy while using this from the first phase of data collection, migration and organization. Your data teams should know how to move data in a planned and coordinated way to keep the data lake well organized and accessible.

Missing foundational capabilities

Every data lake should have a significant number of technical skills. These may include self-service data ingest, data profiling, data classification, data governance and metadata management. Data classification, data lineage and global search and security are essential parts of any active data lake.

These foundational capabilities are required before your data lakes start collecting huge chunks of data for processing. You need to keep a part of your data budget aside to invest in data cleansing, validation, profiling, indexing and tracking metadata. Data mining and data collection are two interdependent tasks. Your company needs to be able to access the data from the data lake during the hour of need. The pulling needs to be error-free and replicable.

Companies that are facing many hurdles are beginning to release that they need to train their data scientists and data engineers better. If you are facing the same problems with big data, retake a step and rethink about distributing your resources in training your teams better.

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