Four Key Big Data Challenges to Watch Out For in 2022

A well-implemented big data strategy may decrease operating costs, shorten time to market, and allow ingenious product development. Firms, on the other hand, encounter a number of big data problems in getting ideas from the boardroom to reality.

Enterprises must make informed business decisions in order to remain competitive in today’s economy, whether those decisions are aimed at growing revenue, keeping consumers, or enhancing product quality. Big data is a critical facilitator for achieving such objectives.

The best approach to handle a large amount of data, which entails the process of storing and analyzing a large collection of data on many data storage, is one of the biggest Big Data issues. When dealing with Big Data, there are a number of key problems that must be overcome with agility.

Addressing the sophistication of data integration and preparation

Big data platforms address the challenges of compiling and storing massive volumes of data of various sorts, as well as the rapid retrieval of data required for analytics. The data collecting procedure, however, might be difficult. Continuous updating is needed to preserve the integrity of a company’s acquired data repositories. This necessitates access to a broad scope of data sources and specialized big data integration solutions.

Some businesses employ a data lake as a catch-all store for enormous amounts of big data gathered from many sources without considering how the different data would be combined. Multiple business domains, for example, provide data that is helpful for joint analysis, but this data typically has ambiguous underlying semantics that must be resolved. It’s usually advisable to adopt a strategic strategy for data integration for the best ROI on big data initiatives.

Inadequate comprehension of large amounts of data

Companies struggle to succeed in their Big Data projects due to a lack of knowledge. Personnel may not comprehend what data is, how and where it is stored, processed, and where it comes from. Others may not have a clear image of what’s going on, even if data specialists do. Employees who do not understand the necessity for knowledge storage, for example, may not be able to preserve a backup of sensitive data and be incompetent to accurately save data in databases. As a result, when this crucial data is required, it is challenging to discover.

Also Read: Four Important Big Data and Analytics Trends to Watch in 2022

Combining data from a variety of sources

In a company, data comes from a variety of places, including social media sites, financial reports, e-mails, ERP software, customer logs, presentations, and employee-created reports. Integrating all of this information to create reports may be a difficult undertaking. This is a neighborhood that numerous firms ignore. It’s ideal since data integration is critical for analysis, reporting, and business intelligence.

Inability to guide big data/AI efforts due to a lack of collaboration

Data analytics is frequently boiled down to poorly targeted projects due to the lack of a single point of responsibility. Such initiatives, which are implemented on an ad hoc basis by isolated business or IT teams, result in skipped stages and erroneous conclusions.

Any data governance policy, no matter how ingenious, will fail if no one is in charge of it. Worse, a fragmented data management strategy makes it hard to grasp what data is accessible at the organizational level, let alone prioritize use cases.

Because of the difficulty in implementing big data, the organization has little insight into its data assets, receives incorrect results from algorithms fed trash data, and faces heightened security and privacy threats. It also spends money since data teams handle data that has no commercial value and no one takes responsibility for it.

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