Why Wrangle Data?
As they digitize and automate archaic processes, businesses all over the world are intimately involved in their digital transformation path and data management. To get there, they are pouring more money into data analytics and business intelligence tools, which allow them to examine large datasets and make better business decisions. As a result, according to IDC analysts, the data analytics business is exploding and currently exceeds $200 billion in annual spending.
One of the most significant Big Data difficulties is determining the best way to handle a huge amount of data, which comprises the process of storing and analyzing a vast collection of data across multiple data storage systems. There are a number of key difficulties that must be handled with agility while dealing with Big Data.
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Complex data integration and preparation
Big data platforms handle the difficulties of compiling and storing large volumes of data of various types, as well as the need for data retrieval quickly for analytics. The data collection technique, on the other hand, could be challenging. To keep the integrity of a company’s acquired data repositories, it’s necessary to update them on a regular basis. This demands access to a diverse range of data sources as well as specific big data integration tools.
Some companies use a data lake as a catch-all repository for massive amounts of big data obtained from many sources without contemplating how the data will be merged. Multiple business domains, for example, generate data that is useful for joint analysis, but the underlying semantics of this data are often confusing and must be reconciled. For the highest ROI on big data endeavors, it’s usually best to have a strategic data integration plan.
Storage: Balancing Cost With Performance
For IT directors, the enormous amount of data generated by enterprises poses a severe data management challenge. IT executives are currently attempting to strike the correct balance between getting maximum commercial value from data and storing it safely and cost-effectively.
Data is collected from a variety of sources within a firm, including social media sites, financial reports, e-mails, ERP software, customer records, presentations, and employee-created reports. Integrating all of this data to generate reports could be a complex task. This is a neighborhood that many businesses overlook. Data integration is crucial for analysis, reporting, and business intelligence, thus it’s excellent.
Lack of Precision Targeting
Due to a lack of a single point of responsibility, data analytics is typically reduced to poorly targeted projects. Such projects, which are handled on an ad hoc basis by discrete business or IT teams, result in steps being skipped and conclusions being reached that are incorrect. No matter how clever a data governance strategy is, it will fail if no one is in charge of it. Worse, a disjointed data management approach makes it difficult to understand what data is available at the corporate level, let alone prioritize use cases.
As big data is difficult to execute, the company has little visibility into its data assets, receives erroneous results from algorithms fed garbage data, and faces increased security and privacy risks. It also costs money since data teams are in charge of data that has no commercial value and no one is accountable for it.
