Big data analytics, as we know, are large data sets in a variety of data types. The type of information that comes under big data is hidden patterns, unknown correlations, market trends, and customer preferences.

“Information is the oil of the 21st century, and analytics is the combustion engine.” – Peter Sondergaard, Former Senior Vice President, Gartner

Though big data has seen normalcy in businesses today, it doesn’t mean that the journey has been smooth. We often talk about the benefits of big data analytics in our blogs and insights.

But do we know the most typical big data analytics issues, possible root causes, and potential solutions to them?

We have mentioned some of the major big data challenges here.

Data unavailability

One reason that big data analytics fail is due to the lack of data. Also, this can be either caused by the lack of data integrations or poor data organizations. It is required to integrate new data sources so that it can eliminate the lack of data. Also, data storage styles can be a problem, but it can be eradicated by introducing a data lake.

Sometimes, big data failure is that the data you need is still not available or is being collected or pre-processed. In such cases, check whether your ETL (extract, transform, load) can process data frequently. If not, increase the schedule adjustment to 2x times or use Lambda Architecture to combine the traditional batch pipeline with a fast real-time stream.

Poor analytics

Many businesses are taking the help of big data analytics, but it causes them a big-time problem if they come across inaccurate analytics. If the initial data received by your system has defects, errors, or is incomplete, you’ll get poor results. The next move is to ensure data quality management process or an obligatory data validation process covering every stage can guarantee incoming data quality at various levels.

Also, human intervention during the development, testing, and verification process can cause defects. Significantly, high-quality testing and verification of the development lifecycle take place on a periodical basis.

And if the analytics produces poor results even after working with high-quality data, the fault is elsewhere. It makes sense to run a detailed review of the system and check data processing algorithms’ implementation.

Zero or no adoption

There are still several businesses that always want to stick with their conventional means of data collection. They do not want to adopt the data-driven culture in their organization. It is mainly because of lack of understanding, lack of organizational alignment, and no desire to meet the changing standards.

Additionally, several employees do not understand the importance of big data. Those who do not understand Big Data’s reputation may not follow the right protocols required for handling big data.

It is time for employees to understand the importance of big data as it can bring tremendous change to any organization. The management can also organize workshops, seminars, and training programs for introducing employees to the world of big data. Big data adoption will also improve decision-making and ultimately lead toward strong leadership for the organization.

Final thought

In this growing data-driven economy, it is essential to take crucial steps to stay competitive. People are implementing big data tactics and technologies to remain at the forefront of technological advancement. But, another fact which walks parallel to this is there are challenges that can pop up anytime. The field of big data is ever-evolving, i.e., better changes toward progression will be seen in the coming years.

For now, companies who have implemented this technology as their part of fundamental growth should take note of challenges. We have made a small effort to bring respective solutions to the difficulties mentioned above.

You can also download our latest whitepapers on big data.